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STRATEGIC DECISION-MAKING PROCESSES: THE ROLE OF MANAGEMENT AND CONTEXT 1

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Article history: Received 25 August 2016 Received in revised form 28 December 2016 Accepted 30 December 2016 Available online 28 January 2017 Making good decisions is an important action to each manager. This is because the decision will affect on their business performance. Although many studies involving many factors that influence decision-making, but it still has not been able to guide managers to make the right strategic decisions making. There is still a strong need to test the relationship between the strategic decision making process output with contextual factors such as internal, external organization characteristics and decision specific characteristics. Relevant study on moderating effect of strategic decision process that enhances the relationship between decision process characteristics and decision output is still scanty. Therefore, the aim of this study is to propose a model based on the effect and impact of contextual factors on the strategic decision making process...

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The effects of visualization on judgment and decision-making: a systematic literature review

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• Published: 25 August 2021
• Volume 73 , pages 167–214, ( 2023 )

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The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

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1 Introduction

A visualization is defined as a visual representation of information or concepts designed to effectively communicate the content or message (Padilla et al. 2018 ) and improve understanding in the audience (Alhadad 2018 ). This representation can manifest in a range of imagery, from quantitative graphs (Tang et al. 2014 ) to qualitative diagrams (Yildiz and Boehme 2017 ), to abstract visual metaphors (Eppler and Aeschimann 2009 ) or artistic imagery. Visualization design may also intend to promote a specific behavior in the audience (Correll and Gleicher 2014 ). The visualization of information is associated with effective communication in terms of clarity (Suwa and Tversky 2002 ), speed (Perdana et al. 2018 ), and the understanding of complex concepts (Wang et al. 2017 ). Research shows, for example, that visualized risk data require less cognitive effort in interpretation than textual alternatives and are therefore comprehended more easily (Smerecnik et al. 2010 ), and complex sentiment data visualized in a scatterplot improve the accuracy in law enforcement decisions compared to raw data (Cassenti et al. 2019 ).

Visual experiences are the dominant sensory input for cognitive reasoning in everyday life, business, and science (Gooding 2006 ). As Davis ( 1986 ) points out, image creation and perception are part of the “unique and quintessential competencies of homo sapiens sapiens”. Hence, the visualization of information is an integral research subject in the domains of cognitive psychology, education (Alfred and Kraemer 2017 ), management (Tang et al. 2014 ) including financial reporting, strategic management, and controlling, marketing (Hutchinson et al. 2010 ), as well as information science (Correll and Gleicher 2014 ).

Management researchers study visualizations from a business perspective. First, the field of financial reporting considers the effect of financial graphs on investor perception (Beattie and Jones 2008 ; Pennington and Tuttle 2009 ). Second, the potential consequences of visualizations on decision-making are examined in the area of managerial decision support, with a focus on judgments based on quantitative data such as financial decisions (Tang et al. 2014 ) and performance controlling (Ballard 2020 ). Finally, a small number of works investigate more complex decision-making based on qualitative, multivariate, and relational information (Platts and Tan 2004 ). Altogether visualizations fulfill a variety of functions, from focusing attention to sharing thoughts to identifying data structures, trends, and patterns (Platts and Tan 2004 ).

The vast majority of existing research in visualization, however, arises from the two domains of information science and cognitive psychology. Information science research on how to design visualizations for effective user cognition stretches back almost one century (Washburne 1927 ). While early research focuses on comparing tables and simple graphs, newer research on human–computer interfaces covers advanced data visualizations facilitated by computing power (Conati et al. 2014 ). For example, interactive visualization software enables users to manipulate data directly. While promising in terms of analytic capability, the potential for biases and overconfidence is suggested as a downside (Ajayi 2014 ). Equally, cognitive psychology research notes that visual information may be superior over verbal alternatives in certain cognitive tasks since they can be encoded in their original form, where spatial and relational data is preserved. Thereby, visual input is inherently richer than verbal and symbolic information, which is automatically reductionistic (Meyer 1991 ), but more suited for discrete information retrieval due to its simplicity (Vessey and Galletta 1991 ). However, the processes behind visual cognition remain largely unclear (Vila and Gomez 2016 ).

Despite the ubiquity of visualizations in research and practice, there is no comprehensive understanding of the potential and limits of information visualization for decision-making. Although at times converging, insights from research of different areas are seldom synthesized (Padilla et al. 2018 ), and there has been no effort for a systematic review or overarching framework (Zabukovec and Jaklič 2015 ). However, a synthesis of existing research is essential and timely due to three reasons. First, information visualization is ubiquitous both in the scientific and business community, yet there are conflicting findings on its powers and limits in support of judgment and decision-making. Second, cognitive psychology research provides several promising suggestions to explain observable effects of visualizations, yet these are rarely integrated into research in other domains, including strategic decision-making. Third, the barriers to using information visualization software have fallen to a minimum, making it available to a wide range of producers and users. This raises the issue of the validity of positive effects for various task and user configurations. The goal of this paper is therefore to provide an overview of the fragmented existing research on visualizations across the social and information sciences and generate insights and a timely research agenda for its applicability to strategic management decisions.

My study advances visualization research on three paths. First, I establish a framework to summarize the numerous effects and variable interactions surrounding the use of visualizations. Second, I conduct a systematic literature review across the social and information sciences and summarize and discuss this plethora of findings along with the aforementioned structure. Third, I utilize this work as a basis for identifying and debating gaps in existing research and resulting potential avenues for future research, with a focus on the area of strategic management decisions.

The structure of the article is as follows. The next chapter briefly describes the research field, followed by the methodology of my literature search. Next, I analyze the results of my search and discuss common insights. In the ensuing chapter, I develop an agenda for management research by building on particularly relevant ideas with conflicting or incomplete evidence. Finally, I conclude my review and discuss contributions and implications for practice.

2 Definition of the research field

2.1 definition of key terms.

Information visualizations support the exploration, judgment, and communication of ideas and messages (Yildiz and Boehme 2017 ). The term “graph” is often used as a synonym for information visualization in general (Meyer 1991 ) as well as describing quantitative data presentation specifically (Washburne 1927 ). As my review exhibits, these graphs constitute the prevalent form of information visualization. Common quantitative visualizations are line and bar charts, often showcasing a development over time and regularly used in financial reporting (Cardoso et al. 2018 ) and controlling (Hutchinson et al. 2010 ). In scientific literature, probabilistic charts such as scatterplots, boxplots, and probability distribution charts (Allen et al. 2014 ) frequently depict risk and uncertainty. More specialized charts include decision trees to depict conditional logic (Subramanian et al. 1992 ), radar charts to display complex multivariate information (Peebles 2008 ), or cluster charts and perceptual maps for marketing decision support (Cornelius et al. 2010 ).

Despite the breadth of existing visualization research, its application to strategic decisions is narrow and there is an abundance of research limited to elementary tasks and choices. To provide a clear distinction, I focus my search on decisions, judgments, and inferential reasoning as more advanced forms of cognitive processing. Decision-making can be broadly defined as choosing between several alternative courses of action (Padilla et al. 2018 ). On the other hand, reasoning and judgment refer to the evaluation of a set of alternatives (Reani et al. 2019 ), without actions necessarily being attached as for decision-making. Such efforts are cognitively demanding and complex when compared to more elementary tasks, such as a choice between options (Tuttle and Kershaw 1998 ), and include the rigorous evaluation of alternatives across a range of attributes, which is characteristic for strategic decisions (Bajracharya et al. 2014 ). For this reason, I include studies that examine the influence of visualizations on some form of decision or judgment outcome. Mason and Mitroff ( 1981 ) highlight that strategic decisions, in management and elsewhere, involve complex and ambiguous information environments. Information visualization may relate to decision quality in this context since one critical factor in the effectiveness of strategic decisions is the objective and comprehensive acquisition and analysis of relevant information to define and evaluate alternatives (Dean and Sharfman 1996 ).

2.2 Perspectives in literature

Visualization research exists within a range of domains in the social and information sciences, which reflects the diversity of the empirical application. I identify psychology (cognitive and educational), management (financial reporting, strategic management decisions, and controlling), marketing, and information science as the primary areas of research. This heterogeneity in terms of application area provides the first dimension in my literature review. Second, I classify existing studies along the type of variable interaction they primarily investigate. Based on the framework first introduced by DeSanctis ( 1984 ), I hereby differentiate four categories: Works principally focused on (1) the effects of visualizations on comprehension and decisions as dependent variables provide the basis of all research. This relationship is then investigated through: (2) User characteristics as moderators; (3) task and format characteristics as moderators; and (4) cognitive processing as mediator. An overview of this classification, including the prevalence of extant findings across domains, is given in Fig.  1 .

Visualization research structured by domain and variables primarily investigated

First, the investigation of visualization effects on decisions and judgments is established across all research areas mentioned, and primarily studies outcome variables such as decision accuracy (Sen and Boe 1991 ), speed (Falschlunger et al. 2015a ), and confidence (Correll and Gleicher 2014 ). While these studies contribute examples for graphs influencing observable decision effectiveness and efficiency across a range of contexts, they do not investigate moderating or mediating factors.

Second, psychology research pushes this investigation further towards including moderating effects of user characteristics , such as domain expertise and training (Hegarty 2013 ), and measures of cognitive ability such as numeracy (Honda et al. 2015 ) or literacy (Okan et al. 2018a ). The relevance of these moderating factors is validated both in studies focusing on cognition as well as experiments in educational research, for example by providing evidence that the quality of a judgment made based on a graph may depend more on the user than the format itself (Mayer and Gallini 1990 ).

Similarly, human–computer interface research spearheads further insights into moderating factors of task and format characteristics, such as task type (Porat et al. 2009 ), task complexity (Meyer et al. 1997 ), data structure (Meyer et al. 1999 ), and the graphical saliency of features (Fabrikant et al. 2010 ) through rigorous user testing. At the same time, Vessey ( 1991 ) developed the theory of cognitive fit as a concept bridging cognitive and information systems research, stating that positive effects of graphs depend on a fit between task type and format type, differentiating between symbolic and spatial archetypes.

Finally, cognitive psychology research aims at explaining the observable effects of visualization in terms of mediating cognitive mechanisms . Here, cognitive load theory provides the foundation, stating that an individual’s working memory capacity is limited, and performance in a task or judgment depends on the cognitive load they experience while assessing information. According to this logic, cognitive load that is too high damages performance (Chandler and Sweller 1991 ). Reducing cognitive load by providing visualizations in complex environments is therefore often stated as a key goal of graph design (Smerecnik et al. 2010 ).

Importantly, the boundaries between these variable categories are fluid. Many studies investigate more than one relationship and the inclusion of moderating variables has become common. Various application areas covering these interdependencies attest to the heterogeneous nature of visualization research. However, previous reviews highlight that insights are seldom shared across fields and call for the integration of findings into new studies (Padilla et al. 2018 ). In particular, strategic management research does not yet follow such a holistic approach.

3 Method of literature search

3.1 search design.

The methodological basis of this paper is a systematic literature search as a means to collect and evaluate the existing findings in a systematic, transparent, and reproducible way on the specified topic (Fisch and Block 2018 ) in order to produce a more complete and objective knowledge presentation than in traditional reviews (Clark et al. 2021 ). I conduct a keyword search on the online search engines EBSCOhost and ProQuest, limited to English-language works that have been peer-reviewed, in order to ensure the quality of the sources. Gusenbauer and Haddaway ( 2020 ) identify both search engines as principal academic search systems as they fulfill all essential performance requirements for systematic reviews. On EBSCOhost, I use the databases Business Source Premier , Education Research Complete , EconLit , APA PsycInfo , APA PsycArticles , and OpenDissertations to search for empirical works; on ProQuest, I use the databases British Periodicals , International Bibliography of the Social Sciences (IBSS) , Periodicals Archive Online , and Periodicals Index Online with a filter on articles to cover the social sciences comprehensively. The keyword used is the concatenated term “(visualization OR graph OR chart) AND (decision OR judgment OR reasoning)”, searched for in abstracts. Footnote 1 The terms were chosen as “visualization” is commonly used as a category name for visualized information (Brodlie et al. 2012 ), and the “graph” is the focus of traditional visualization research (Vessey 1991 ). The term “chart” is a synonym for both quantitative and qualitative graphs which has seen increasing use particularly in the 2000s (Semmler and Brewer 2002 ). The terms “judgment OR decision OR reasoning” were added to ensure that studies examining observable outcomes of visualization use, as opposed to cognitive processes such as comprehension only, were highlighted. After a review of the evolution of visualization research over time, I focus my search to articles published from the year 1990 in order to capture the recent advancements covering modern modes of information visualization. Footnote 2 This search results in 1658 articles combined, after removing duplicates 1505 articles remain.

Next, I review all article abstracts based on the three content criteria defined in the following. I include all articles rooted in the (1) social sciences or information sciences , where the focus of the study lies on (2) how a visualization per se or a variation within related visualizations affects a user's or audience's decision or judgment in a given task , and the topic is studied through (3) original empirical works. Most articles are excluded in this process and 116 studies remain due to the prevalence of graphs as auxiliary means, not the subject of research, in various domains, particularly in medical research. I repeat this exclusion process by reading the full texts of all articles and narrow down the selection further to 81 papers.

Building on this systematic search, I conducted a supplementary search through citation and reference tracking, as well as supplementary search engines, such as JSTOR (Gusenbauer and Haddaway 2020 ). Footnote 3 This includes gray literature such as conference proceedings or dissertations, which lie outside of traditional academic publishing. In addition, I limit the inclusion of gray literature to studies by researchers included in my systematic search and completed within the last 10 years in order to gather a comprehensive and up-to-date overview of the findings of working groups particularly relevant to visualization research. Thereby I identify 52 additional articles, resulting in a total of 133 articles included.

3.2 Limitations of search

Due to the plethora of existing literature mentioning the topic of visualization in various contexts and degrees of quality, I subject my search to well-defined limitations. First, I only include peer-reviewed articles in my systematic search. These are studies that have been thoroughly validated and represent the major theories within a field (Podsakoff et al. 2005 ). However, I incorporate gray literature of comparable quality as part of my additional exploratory search.

Second, I limit the search to information and social sciences to deliberately omit results from the broad areas of medicine and natural sciences. In these, various specific concepts are visualized as a means within research, yet not investigating the visualization itself. For the same reason, I only apply the search terms to article abstracts, since the terms “graph” and “chart” in particular will result in a high number of results when searched for in the full text, due to the common use of graphs in presenting concepts and results.

Third, I only include original empirical work in order to enable the synthesis and critical validation of empirical findings across research areas. At the same time, I acknowledge the existence of several highly relevant theoretical works, which inform my search design and structure while being excluded from the systematic literature search and analysis.

4.1 Overview of results

I identify a total of 133 articles, published between 1990 and 2020. Interest in visualization research gained initial momentum in the early 1990s (Fig.  2 ). More recently, the number of studies rises starting around 2008, with the continued publication of five to ten papers per year since and a visible peak in interest around 2014/15. A significant share of recent works stems from the information science literature, and the wealth of publications around 2014 coincides with the advent of mainstream interest in big data (Arunachalam et al. 2018 ), which is closely linked to information visualization for subsequent analysis and decision-making (Keahey 2013 ). In addition, a cluster of publications by one group of authors (Falschlunger et al. 2014 , 2015a , c, b) in the financial reporting domain enhances the observed peak in publications, which is therefore not indicative of a larger trend. Instead, the continued wealth of publications in the last decade shows the contemporary relevance of and interest in visualization research.

Articles included in systematic search by publication year and area of research

Next to the information sciences, the largest share of the studies identified originates in cognitive psychology research. Furthermore, management literature discusses visualization and graphs continuously throughout the last three decades, with notable peaks in interest around the year 2000 in the domain of annual reporting (Beattie and Jones 2000 , 2002a , b ; Arunachalam et al. 2002 ; Amer 2005 ; Xu 2005 ) and internal management reporting with classic bar and line graphs around the year 2015 (Falschlunger et al. 2014 , 2015a , c ; Tang et al. 2014 ; Hirsch et al. 2015 ; Zabukovec and Jaklič 2015 ). Consumer research in marketing constitutes a further domain regularly discussing visualizations and their effect on decisions and judgment (Symmank 2019 ), albeit to a smaller extent. This heterogeneity in research areas is reflected by the journals identified in my search, where the 133 articles spread across 83 different journals, complemented by ten studies from conference proceedings and three papers included in doctoral dissertations (Table 1 ). Apart from the articles in conference proceedings added through the supplementary exploratory search, the studies were published in journals with a SCIMAGO Journal Rank indicator ranging from 0.253 (Informing Science) to 8.916 (Journal of Consumer Research). All but four journals received Q1 and Q2 ratings, which equals the top half of all SCImago rated journals. The h-index ranges from 6 (Journal of Education for Library and Information Science) to 332 (PLoS ONE) (Scimago Lab 2021 ).

In the 133 articles identified, experiments are by far the most common method for data collection, with 113 (85%) of studies conducting a total of 182 controlled experiments with over 28,000 participants (Fig.  3 ). In addition, I find seven instances of archival research covering over 600 companies, six instances of surveys with almost 1000 participants in total, four quasi experiments, two natural experiments, and one field experiment to complete the picture.

Articles included in systematic search by methodology

Of the 182 experiments conducted, the majority works with students as subjects (125 or 69%). The largest remaining share investigates a sample of the general (online) population (32 or 18%) and only 13% study the effect of visualization with practitioners in their respective domain (24). In contrast, four out of the six surveys were conducted with practitioners that were addressed explicitly. Besides, one survey each was conducted with students and subjects from the general population.

Following the advice by Fisch and Block ( 2018 ), I categorize the results from literature in a concept-centric manner, based on the primary variable interaction investigated. I further distinguish by the four application domains and seven subdomains discussed and present a structured overview at the end of each subchapter. The independent variable in all cases is the use of a visual representation designed for a specific use case, either as opposed to non-visual representation methods such as verbal descriptions [e.g. Vessey and Galletta ( 1991 )], or traditional visualizations that the research aims to improve on [e.g. Dull and Tegarden ( 1999 )].

4.2 Effects of visualizations on decisions and judgments

4.2.1 judgment/decision accuracy.

The most common dependent variable investigated in visualization research is the accuracy of the subjects on a given comprehension, judgment, or decision task. Most studies are in psychology research, with positive effects dominating. In cognitive psychology, experiments show that well-designed visualizations can improve problem comprehension (Chandler and Sweller 1991 ; Huang and Eades 2005 ; Nadav-Greenberg et al. 2008 ; Okan et al. 2018b ). For example, Dong and Hayes ( 2012 ) show in their experiment with 22 practitioners that a decision support system visualizing uncertainty improves the identification and understanding of ambiguous decision situations. Likewise, visualizations improve decision (Pfaff et al. 2013 ) and judgment accuracy (Semmler and Brewer 2002 ; Tak et al. 2015 ; Wu et al. 2017 ) and improve the quality of inferences made from data (Sato et al. 2019 ). Findings in educational psychology support this claim. In teaching, visual materials improve understanding and retention (Dori and Belcher 2005 ; Brusilovsky et al. 2010 ; Binder et al. 2015 ; Chen et al. 2018 ) in students, and support the judgment accuracy of educators when analyzing learning progress quantitatively (Lefebre et al. 2008 ; Van Norman et al. 2013 ; Géryk 2017 ; Nelson et al. 2017 ). Furthermore, Yoon’s longitudinal classroom intervention (2011) using social network graphs enables students to make more reflected and information-driven strategic decisions. However, other studies arrive at more mixed or opposing findings. In their experiment, Rebotier et al. ( 2003 ) find that visual cues do not improve judgment accuracy over verbal cues in imagery processing. Other experiments even demonstrate verbal information to be superior over graphs in comprehension (Parrott et al. 2005 ) as well as judgment accuracy (Sanfey and Hastie 1998 ). Some graphs appear unsuitable for specific content, such as bar graphs depicting probabilities (Newman and Scholl 2012 ) and bubble charts encoding information in circle area size (Raidvee et al. 2020 ). In addition, more complex charts like boxplots, histograms (Lem et al. 2013 ), and tree charts (Bruckmaier et al. 2019 ) appear less effective for the accurate interpretation of statistical data in some experiments, presumably as they elicit errors and confusion in insufficiently trained students.

Studies in management and business research arrive at further, more pessimistic results. While Dull and Tegarden ( 1999 ) find in their experiment with students that three-dimensional visuals can improve the prediction accuracy in financial reporting contexts, and Yildiz and Boehme ( 2017 ) observe in their practitioner survey that a graphical model of a corporate security decision problem improves risk perception when compared to a textual description, most other studies present a less positive picture. Several studies do not find graphs superior over tables in financial judgments (Chan 2001 ; Tang et al. 2014 ; Volkov and Laing 2012 ), and in consumer research (Artacho-Ramírez et al. 2008 ). In financial reporting, a dedicated school of research investigates the effect of distorted graphs lowering financial judgment accuracy (Arunachalam et al. 2002 ; Beattie and Jones 2002a , b ; Amer 2005 ; Xu 2005 ; Pennington and Tuttle 2009 ; Falschlunger et al. 2014 ), irrespective of whether the distortion is intended by the designer. Chandar et al. ( 2012 ) elaborate on the positive effect of the introduction of graphs and statistics in performance management for AT&T in the 1920s, but more recent case study examples are rare.

By contrast, several experimental studies from human–computer interaction research largely contribute evidence for a positive effect. Targeted visual designs lead to higher judgment accuracy in specific tasks (Subramanian et al. 1992 ; Butavicius and Lee 2007 ; Van der Linden et al. 2014 ; Perdana et al. 2018 ) and improve decision-making (Peng et al. 2019 ). For example, probabilistic gradient plots and violin plots enable higher accuracy in statistical inference judgments in the online experiment by Correll and Gleicher ( 2014 ) than traditional bar charts. However, experiments by Sen and Boe ( 1991 ) and Hutchinson et al. ( 2010 ) equally lack a significant effect on data-based decision-making quality. Amer and Ravindran ( 2010 ) find a potential for visual illusions degrading judgment accuracy similar to results from financial reporting, and McBride and Caldara ( 2013 ) find that visuals lower accuracy in law enforcement judgments when compared to raw data presentation (Table 2 ).

4.2.2 Response time

The next most common outcome variable investigated in visualization research is response time , often referred to as efficiency. Across the board, experimenters observe that information visualization lowers response time in various judgment and decision tasks. In psychology, this includes decision-making in complex information environments (Sun et al. 2016 ; Géryk 2017 ). The opposite effect emerges from only one study, where Pfaff et al. ( 2013 ) find that a decision support system visualizing complex uncertainty information requires a longer time to use than one omitting this graphical information. In management research, Falschlunger et al. ( 2015a ) find that visually optimized financial reports can speed up judgment both for students and practitioners. Studies originating in information science validate this picture, observing that well-designed visualizations reduce response time in quantitative (Perdana et al. 2018 ) as well as geospatial judgment tasks (MacEachren 1992 ). Furthermore, McBride and Caldara ( 2013 ) observe that students in their experiments arrive at faster judgments when provided with a network graph as opposed to a table (Table 3 ).

4.2.3 Decision confidence

Next to these directly observable metrics, experimenters regularly elicit measures of decision confidence in visualization research based on subjects’ self-assessment. From a cognitive psychology perspective, Andrade ( 2011 ) finds that subjects display excessive confidence in estimates based on visualizations, which biases subsequent decision-making. On the other hand, Dong and Hayes ( 2012 ) show that a visual decision support system depicting uncertainty in engineering design leads to marginally lower decision confidence, compared to traditional methods omitting uncertainty information. In management research, Tang et al. ( 2014 ) present an increase in confidence in the context of financial decision-making, and Yildiz and Böhme (2017) find in their practitioner survey that an appealing visual increases decision confidence in a managerial setting without changing the actual decision outcome. Similarly, further experiments in information science provide evidence for increased confidence with a link to increased judgment accuracy (Butavicius and Lee 2007 ) or without (Sen and Boe 1991 ; Wesslen et al. 2019 ). In the context of uncertainty, Arshad et al. ( 2015 ) once again report novice subjects having lower confidence in the use of graphs with uncertainty visualized, however, this effect does not occur for practitioners (Table 4 ).

4.2.4 Prevalence of biases

Several studies investigate the prevalence of biases by searching for distinct patterns of deviations in judgment and decision accuracy with largely mixed results. Through a total of seven cognitive psychology experiments, Sun et al. ( 2010 , 2016 ) and Radley et al. ( 2018 ) find that varying scale proportions in graphs change the resulting decision-making since data points are evaluated in a cognitively biased manner based on their distance to other chart elements. Furthermore, Padilla et al. ( 2015 ) demonstrate that uncertainty is understood to a disparate extent when it is encoded through spatial glyphs, color, or brightness. In human–computer interaction research, experiments observe similar framing biases through salient graphical features (Diamond and Lerch 1992 ) such as color schemes (Klockow-McClain et al. 2020 ). Lawrence and O’Connor ( 1993 ) also show that graph scaling affects judgment and relate this to the anchoring heuristic. Finally, financial reporting research extensively dedicates its field of impression management on the observation that such biases are prevalent and possibly intended in annual report graphics, including through distorted graph axes (Falschlunger et al. 2015b ) and an intentional selection of information to visualize (Beattie and Jones 1992 , 2000 ; Dilla and Janvrin 2010 ; Jones 2011 ; Cho et al. 2012a , b ). Two further experiments compare the prevalence of cognitive biases with graphs compared to text directly and find no difference for the recency bias in financial reporting (Hellmann et al. 2017 ) as well as for other heuristics in data-based managerial decision-making (Hutchinson et al. 2010 ) (Table 5 ).

4.2.5 Attitude change and willingness to act

Observations on attitude change and the willingness to act on information constitute the final category of outcome variables found in visualization research. Cognitive psychology research observes an effect of visualizations on risk attitude, where salient graphs can either enhance risk aversion (Dambacher et al. 2016 ) or risk-seeking (Okan et al. 2018b ), depending on the information that is highlighted most saliently. Similarly, varied financial graphs change investors’ risk perception and subsequent investment recommendations (Diacon and Hasseldine 2007 ). In the area of performance management, the visualization of KPIs motivates managers’ intention to act on the information when compared to text (Ballard 2020 ). Consumer research investigates such phenomena commonly, where brand attitude and the intention to purchase a product represent specific cases of judgment and decision-making. Miniard et al. ( 1991 ) were among the first to show that different pictures can result in different attitudes, while Gkiouzepas and Hogg ( 2011 ) extend this investigation to visual metaphors. Finally, information science research provides further insights. King Jr et al. (1991) find that visualizations are more persuasive in attitude change than text, and Perdana et al. ( 2018 ) increase student subjects’ willingness to invest in their experimental setting through visualization software. On the other hand, Phillips et al. ( 2014 ) find their subjects to be less willing to seek out additional information in ambiguous decision settings (Table 6 ).

4.3 User characteristics as moderating variables

4.3.1 expertise and training.

Common moderating variables investigated both in psychological and information science research are the users’ expertise or training experience in a given domain. Experimenters widely encounter a positive impact of experience on the influence of visualizations on judgment accuracy and efficiency. In cognitive psychology, Hilton et al. ( 2017 ) find that graphs of statistical risk improve decision quality for more experienced practitioners alone. On the other hand, some results from educational psychology point towards the opposite effect of experience. Mayer and Gallini ( 1990 ) find in their student experiments that learners with higher pre-test performance benefit less from visual aids than learners on a lower level. In the information sciences, Conati et al. ( 2014 ) find in their testing of computer interfaces that experience with visualizations leads to a pronounced advantage in judgment accuracy. Training sessions (Raschke and Steinbart 2008 ) and experience through task repetition (Meyer 2000 ) enhance the positive effects of graphs (Table 7 ).

4.3.2 Cognitive ability

Another user characteristic regularly investigated in the social sciences is the measurement of cognitive ability . In psychology studies, Honda et al. ( 2015 ) and Cardoso et al. ( 2018 ) find that reflective ability determines in part how well subjects translate visualizations into accurate judgments. Visual working memory (Tintarev and Masthoff 2016 ) and numeracy (Honda et al. 2015 ) are further traits related to cognitive ability in dealing with visualizations and found to enhance the benefits of visualizations on judgment effectiveness and efficiency. The only study presenting contrary results consists of three experiments by Okan et al. ( 2018a ), where subjects with higher graph literacy are more prone to specific biases when shown bar graphs of health risk data, and thereby make less accurate judgments. On the other hand, experiments in financial reporting (Cardoso et al. 2018 ) confirm the positive effect of the reflective ability. Conati and Maclaren ( 2008 ) and Conati et al. ( 2014 ) extend this idea to perceptual speed in the area of consumer research (Table 8 ).

4.3.3 User preferences

Finally, experimenters investigate user preferences at times. In the adjacent field of musical education, for example, Korenman and Peynircioglu ( 2007 ) demonstrate that the visual presentation of learning material is only helpful to students with the respective learning style. In cognitive psychology, Daron et al. ( 2015 ) observe a variation in user preferences when presented with visualization options, however without a significant effect on decision performance. This result is replicated in an online survey on human–computer interaction by Lorenz et al. ( 2015 ). O’Keefe and Pitt ( 1991 ) operationalize cognitive style from the MBTI framework and find a weak association with the subjects’ reported preferences for text or specific chart types. However, no relation to actual judgment accuracy or efficiency is found (Table 9 ).

4.4 Task and format characteristics as moderating variables

4.4.1 task type.

One common task characteristic identified as a moderating variable is the task type , originally defined in the information sciences. In her seminal theoretical paper, Vessey ( 1991 ) identifies spatial and symbolic tasks as the two archetypes, which correspond to spatial and symbolic types of cognitive processing and spatial (graphical) and symbolic (textual/numerical) representations. She hypothesizes that visualizations improve judgment effectiveness where these three manifestations align, which she defines as cognitive fit and validates through experiments (Vessey and Galletta 1991 ), including in the sphere of multiattribute management decisions (Umanath and Vessey 1994 ). Further research in information science widely supports this moderating effect by comparing tables and standard quantitative graphs in judgment tasks of increasing complexity (Coll et al. 1994 ; Tuttle and Kershaw 1998 ; Speier 2006 ; Porat et al. 2009 ). On the other hand, experiments in managerial forecasting (Carey and White 1991 ) and financial reporting (Hirsch et al. 2015 ) present the effectiveness of graphical displays in spatial decisions, based on cognitive fit theory. Fischer et al. ( 2005 ) provide further evidence from the domain of cognitive psychology, showing that bar graphs support spatial-numerical judgments particularly well when the chart orientation equals the cognitive processing by following a left-to-right direction (Table 10 ).

4.4.2 Level of data structure

I identify two other task characteristics investigated in the literature, albeit infrequently. First, the level of data structure has been investigated only once in the information science domain. Meyer et al. ( 1999 ) find line charts superior over tables in judgment tasks when the underlying data is structured, with the opposite effect for unstructured data (Table 11 ).

4.4.3 Task complexity

Second, two further experiments observe task complexity as a moderating effect. Meyer et al. ( 1997 ) demonstrate that the speed advantage they find for tables over bar graphs in their computer interface tasks becomes more pronounced with increasing task complexity. However, the same effect does not occur for line graphs. On the other hand, Falschlunger et al. ( 2015c ) find task complexity to be the main factor in predicting task efficiency and effectiveness in handling financial reports but do not observe interaction effects with the visualization (Table 12 ).

4.4.4 Graphical saliency of relevant data

Finally, various studies investigate modifications in the graph format as a variable, with a focus on the graphical saliency of relevant data . This area of research is bridging the two domains of cognitive psychology and information science with widely overlapping results. For example, Verovszek et al. (2013) observe in their information science experiment that colored visualizations are less effective in supporting laypeople’s judgments on urban planning than simple black-and-white line drawings since colorful, irrelevant features distract from the core information. Van den Berg et al. ( 2007 ) identify color as a more powerful feature to highlight salient information in graphs than other variables, such as size. Spence et al. ( 1999 ) find that variations in brightness lead to faster response times in comparison tasks than variations in color. Breslow et al ( 2009 ) demonstrate that the moderating effect of the use of color on judgment speed depends on the task type, with multicolored visuals ideal for identification tasks and black-and-white brightness scales preferable for comparison tasks. Finally, MacEachran et al. (2012) find colorless suited to represent uncertainty when compared to features such as fuzziness or transparency in their surveys with students and practitioners.

Next to color, three-dimensional depth cues have received attention in research. Several psychology experiments find that three-dimensional depth cues irrelevant to the information visualized lower judgment accuracy (Zacks et al. 1998 ; Edwards et al. 2012 ) as well as speed (Fischer 2000 ). Negative effects occur equally for other irrelevant visual cues lowering the saliency of actually relevant information (Fischer 2000 ). Further studies show that increasing the saliency of relevant features can enhance the tendency to make compensatory choices (Dilla and Steinbart 2005 ) and shorten response time (Fabrikant et al. 2010 ), while visual clutter decreases judgment accuracy and boosts response times (Ognjanovic et al. 2019 ). Several other studies test the suitability of a specific set of graphs for unique judgment areas such as uncertainty simulation in urban development (Aerts et al. 2003 ), risk communication (Stone et al. 2017 ; Stone et al. 2018 ), and performance management (Peebles 2008 ) (Table 13 ).

4.5 Cognitive aspects as mediating variables

4.5.1 cognitive load.

Cognitive psychology research introduces the idea of cognitive processes mediating the influence of visualizations on judgment performance, with a focus on cognitive load . Jolicœur and Dell’Acqua ( 1999 ) show in their experiment that the perception of visualizations is subject to structural constraints in working memory capacity, and Allen et al. ( 2014 ) manipulate cognitive load as a dependent variable to demonstrate that judgment accuracy and speed using visualizations decrease under higher cognitive load. Subsequently, psychology experiments provide evidence that visualizations improve decision performance by reducing cognitive load as a mediating factor, operationalized and measured either through pupil size and dilation (Smerecnik et al. 2010 ; Toker and Conati 2017 ) or self-reported load (Cassenti et al. 2019 ). In management research, Ajayi ( 2014 ) investigates this relationship in the context of a proprietary visualization tool for financial data but finds no effect of the visualization component on cognitive load or judgment accuracy. Two further experiments in human–computer interface research operationalize cognitive load based on subjective reporting (Anderson et al. 2011 ) and performance in a secondary task (Block 2013 ) and demonstrate that cognitive load mediates the relationship between visualization use and judgment accuracy and speed, with some types of graphics better suited than others (Table 14 ).

4.5.2 Gazing behavior

Another concept frequently operationalized to represent working memory capacity is gazing behavior , which more recent experiments observe through the use of eye-tracking technology, pioneered by the information sciences. Reani et al. ( 2019 ) observe in their experiment with 49 students that gazing behavior is associated with judgment accuracy, where subjects that pay more attention to relevant visual areas deliver more accurate answers. Similarly, Lohse ( 1997 ) finds that in the more complex decision environment of a budget allocation simulation, decision accuracy is related to efficient gazing behavior and can be improved through the use of colors to reduce the time subjects spend looking at the chart legend. Psychology experiments validate that well-designed graphs enable subjects to focus their attention on relevant information and subsequently improve decision accuracy (Huestegge and Pötzsch 2018 ) and response time (Vila and Gomez 2016 ) (Table 15 ).

4.5.3 Attention

Another variable operationalized at times in eye-tracking experiments is attention, which is elicited through metrics such as the average gazing duration on a specific visual element (Pieters et al. 2010 ). In their cognitive psychology experiment, Smerecnik et al. ( 2010 ) observe that graphs attract more attention in risk communication compared to tables and text and are associated with more accurate judgments. Applying this idea to consumer research, Pieters et al. ( 2010 ) study the consumer’s attention towards visual advertisements and observe that visual complexity based on features such as decorative color can hurt attention, while well-structured complexity such as arrangements of relevant information enhances attention and the attitude toward the brand (Table 16 ).

4.5.4 Affect

Finally, some research emerges into the potential mediating role of affect . Harrison ( 2013 ) shows in her large-scale online experiment that affective priming can significantly influence judgment accuracy in tasks supported visually and that the graphs themselves can cause a change in affect valence. Similarly, Plass et al. ( 2014 ) demonstrate in their educational research that color and shape in visualizations can evoke positive affect and are associated with better student learning (Table 17 ).

5 Discussion

In this paper, I have presented a systematic and integrative review of the current state of research on the effect of information visualization in the social and information sciences. I structured and summarized the results of my systematic literature review along the type of variable interactions present in experimental research. In order to discuss and synthesize the variety of literature insights, I categorize them into three groups: Descriptions of the positive effects for visualizations within decision-making, elaborations on moderators of this potential, and insights into negative effects of misguided visualization use. Table 18 highlights this categorization of results by application domain.

5.1 Positive Effect 1: Information visualization improves decision accuracy and quality

Research findings overwhelmingly confirm the hypothesis that visualizations enable the user to comprehend information more effectively, subsequently improving performance in judgments and decisions. The reason behind this effect is most commonly attributed to cognitive mechanisms. Suwa and Tversky ( 2002 ) point out that based on cognitive load theory, less working memory is needed when visuals provide external representations of concepts, which one can easily refer back to and thereby need not keep in mind, leading to improved judgments. Allen et al. ( 2014 ) show in their experiment that under externally induced cognitive load, well-designed charts suffer less than cluttered ones. Furthermore, graphs enable a simpler gazing pattern than text, which can be used as an indicator of cognitive effort (Smerecnik et al. 2010 ). Based on the concept of cognitive load reduction, visualizations are effectively used in various application areas including management research (Falschlunger et al. 2014 ) and more specifically managerial decision-making (Yildiz and Boehme 2017 ), next to psychology and information sciences more broadly.

5.2 Positive Effect 2: Information visualization steers attention towards uncertainty

A large share of studies identified points towards the strength of visualizations in enhancing uncertainty and risk features in a data set. Beyond increasing the awareness of uncertainty (Dong and Hayes 2012 ), the question of whether visualizations can also improve the reasoning with probabilistic information is studied extensively. Various studies show that visualizations can reduce typical comprehension issues, resulting in the more accurate use of probabilities from a statistical perspective (Allen et al. 2014 ; Wu et al. 2017 ; Stone et al. 2018 ). Positive effects in risk understanding are evaluated particularly in the contexts of safety, such as food safety (Honda et al. 2015 ) and violence risk (Hilton et al. 2017 ). Studies investigating the cognitive processes more closely provide evidence that simpler charts indeed perform best (Edwards et al. 2012 ) since they can reduce cognitive load (Anderson et al. 2011 ) and ultimately improve the internal processing of probabilistic models (Tak et al. 2015 ). As Quattrone ( 2017 ) points out, ambiguity and uncertainty are inherent in managerial decision-making and should be embraced by information visualization, but research on this insight in management is scarce.

5.3 Positive Effect 3: Information Visualization Speeds Up Cognitive Processing

There is evidence that graphs lead to faster processing, learning, and decision-making (Block 2013 ), as judgment and decision efficiency are measured and operationalized as the response time in various experiments. Utilizing eye-tracking technology, Reani et al. ( 2019 ) point out that different types of graphs result in varying gazing patterns in users and hypothesize a link to the reasoning processes. Based on the principle of saliency, multiple studies show that graphs optimally designed to focus attention on the most relevant information lead to more efficient and thereby faster gazing (Falschlunger et al. 2014 , 2015a ), since more time can be spent focusing on highly relevant information (Vila and Gomez 2016 ). Much of this existing work stems from the area of management reporting, investigating quantitative financial data. Overall, the evidence for visual aids speeding up cognitive processing and decision-making appears robust and applicable to management research.

5.4 Moderator 1: The effects of visualization depend on cognitive fit within the decision context

Cognitive fit is a moderator in the effectiveness of visualizations that has been well validated across psychological, management, and information science. Introducing cognitive fit theory, Vessey ( 1991 ) explains many existing research findings in the graph versus table literature claiming that graphs are not (always) more effective, most notably by DeSanctis ( 1984 ). Cognitive fit theory is validated widely (Vessey and Galletta 1991 ; Carey and White 1991 ; Coll et al. 1994 ; Meyer et al. 1997 ; Meyer 2000 ; Porat et al. 2009 ; Perdana et al. 2019 ). Padilla (2018) recognizes that this well-documented effect arises because a cognitive mismatch between data, task, and approach (format) requires more working memory, which negatively affects cognitive processing effectiveness and efficiency. Though highly reliable, many studies investigate elementary processing tasks with limited external validity for more complex decision-making in practice. Umanath and Vessey ( 1994 ) and others (Tuttle and Kershaw 1998 ; Hirsch et al. 2015 ) extend the original cognitive fit theory and successfully apply it to multi-attribute judgments—though at a potential time-accuracy tradeoff. Finally, the idea of matching task and format complexity can be seen as an extension to cognitive fit theory, where graphs are only helpful when they represent as much data complexity as necessary to complete the respective task, but as little as possible (Pieters et al. 2010 ; Van der Linden et al. 2014 ; Géryk 2017 ).

5.5 Moderator 2: Differences within users can be more relevant than the visualization design

Task complexity in relation to user ability needs to be strictly controlled for as a moderator of positive visualization effects. Early studies including individual differences hypothesize that graph potential may be limited to users with a high level of ability (Subramanian et al. 1992 ). Other studies claim that the positive effects of visualizations may be more significant for (McIntire et al. 2014 ) or even limited to (Mayer and Gallini 1990 ) less-skilled individuals. However, these seemingly conflicting results can be explained by the idea that since graphs are effective by requiring less working memory than other formats, improvements are only visible where working memory capacity is limited and needed elsewhere (Lohse 1997 ).

Furthermore, the majority of studies including user factors emphasize the importance of training and expertise, as opposed to inherent ability. Various studies support the claim that experience significantly enhances the contribution of visuals (Porat et al. 2009 ; Edwards et al. 2012 ; Falschlunger et al. 2015a ; Ognjanovic et al. 2019 ), with some claiming that training constitutes a requirement (Géryk 2017 ; Hilton et al. 2017 ) or that users without training are subject to stronger biases (Raschke and Steinbart 2008 ). Consequently, the training factor needs to be closely monitored particularly for a novel or complex visualization. However, extensive training of users is frequently time-consuming and costly. Therefore, the imperative arises for interactive visualization interfaces to accommodate for varying user needs in demanding decision situations. Interactive data visualization software is shown to improve investment decisions (Perdana et al. 2018 ) and judgments by reducing cognitive load (Ajayi 2014 ), for example with flexible performance management dashboards that reduce information load while hosting a full set of KPIs (Yigitbasioglu and Velcu 2012 ). Contrary to much of the early research on static visualizations, the progress in interactivity studies has been driven by practice and case studies, with calls for science to follow suit (Marchak 1994 ; McInerny et al. 2014 ). Overall, I conclude that a match in ability and training with format complexity and novelty, respectively, is a significant determinant of the effectiveness of visualizations. However, there has been little to no empirical research on the subject in the domain of management.

5.6 Negative Effect 1: Visualizations May Not Always Be Helpful: Risk to Impair Decision Making by Misguiding Attention

Several studies, including in management research, argue that visualizations misguide attention even in the presence of cognitive and user fit. For example, Hutchinson (2010) finds graphs to be as exposed to cognitive biases as tables in data-based managerial decision-making. Similarly, other studies identify graphical representations as equally or less effective than verbal formats in financial reports (Volkov and Laing 2012 ), forecasting (Chan 2001 ), probabilistic comprehension (Parrott et al. 2005 ), evidence evaluation (Sanfey and Hastie 1998 ), and communication (Rose 1966 ). The common denominator in these studies is the suboptimal use of salient visual elements, leading to distraction. For example, overly realistic visualizations encompassing color and higher complexity (DeSanctis 1984 ), may lead to visual clutter that decreases performance (Alhadad 2018 ). As Padilla et al. ( 2018 ) argue, visualizations are powerful because they attract fast cognitive bottom-up processing. However, when this superficial processing is focused on irrelevant elements, decision quality can suffer. A well-studied example of this effect is the addition of superfluous three-dimensional cues to quantitative graphs, which lowers accuracy in using the graph (Zacks et al. 1998 ; Fischer 2000 ).

5.7 Negative Effect 2: Visualizations can increase decision-maker overconfidence

The most documented cognitive bias in my review is overconfidence, which can be aggravated by the use of visualizations (O’Keefe and Pitt 1991 ). Multiple studies demonstrate that graph use can increase decision confidence without enhancing decision quality to the same extent in the context of management and finance (Tang et al. 2014 ; Yildiz and Boehme 2017 ; Wesslen et al. 2019 ). This may result from the perception that visualizations show more information at once (Miettinen 2014 ), thereby seemingly requiring less search for additional information (Phillips et al. 2014 ). In particular, this can be the case when graphs appear to visually simplify a problem and the decision-maker fails to adjust his confidence to the underlying complexity (Sen and Boe 1991 ). There is some research with inconclusive results (Pfaff et al. 2013 ), showing no difference in confidence (Hirsch et al. 2015 ) or even lowered confidence (Dong and Hayes 2012 ; Arshad et al. 2015 ). However, the majority of these studies deal with uncertainty communication, which is inherently tied to a decrease in confidence (Watkins 2000 ). Overall, the evidence demonstrates that unless highlighting uncertainty, visual aids result in higher decision confidence. The case of overconfidence is particularly well established in the area of management controlling and financial reporting but understudied for strategic decisions.

6 Research agenda

In summary, there is ample evidence for the potential of information visualization to improve decision-making in terms of effectiveness and efficiency, yet my review highlights possible limitations and risks where its use is misguided or inappropriate. I argue that several of these are particularly critical for further research since there is little to no application to the domain of strategic management decisions, despite the ubiquity of visualizations to support these in practice. Based on the summary of my insights by application domain in Table 18, I identify five research gaps in the field of strategic management decisions.

First, there is conflicting evidence regarding the effect of information visualization on decision-making under uncertainty, and existing research is mostly limited to information science (Aerts et al. 2003 ). Depending on the context and design, visualization use can increase or reduce risk-taking (Dambacher et al. 2016 ) but has the potential to improve probabilistic reasoning in an objective manner (Allen et al. 2014 ). Given the importance of uncertainty as a defining factor of strategic management decisions (Quattrone 2017 ), the possibility of information visualizations to improve risk understanding in the management context deserves closer evaluation. For example, the framing bias is a well-documented phenomenon in strategic decision-making (Hodgkinson et al. 1999 ), leading to different subjective risk interpretations and subsequent decisions based on the presentation of information. Naturally, the question arises whether information visualization can mitigate this bias and which salient visual features are beneficial. I suggest exploring this question through experiments with strategic management decision vignettes.

Research Gap 1: How can information visualization mitigate the framing bias and improve risk understanding in strategic management decisions?

Second, my review has made clear that the effectiveness of information visualization depends in large parts on user characteristics such as expertise (Hilton et al. 2017 ), numeracy (Honda et al. 2015 ), and graph literacy (Okan et al. 2018b ), yet there exists no transfer of this insight towards individual managerial traits. At the same time, well-established concepts such as the Upper Echelons Theory (Hambrick 2007 ) highlight the relevance of CEO characteristics, both observable and psychological for strategic managerial choices and, subsequently, company performance. While some concepts such as experience may be transferrable from existing visualization research (Falschlunger et al. 2015c ) requiring validation only, others, such as group position or individual values, present opportunities to extend theory substantially. I suggest exploring this area through a dedicated analysis of relevant CEO characteristics and corresponding empirical research with practitioner subjects.

Research Gap 2: How do CEO characteristics influence the effectiveness of information visualization in strategic management decisions?

Third, while the prevalence of visualization use for impression management in financial reporting is well-established (Falschlunger et al. 2015b ), there is a complete lack of transfer of this phenomenon to the realm of strategic management decisions. As Whittington et al. ( 2016 ) highlight, strategy presentations can be seen as an effective tool for CEO impression management. Given the popularity of visualizations in this communication medium – both through quantitative charts and schematic diagrams (Zelazny 2001 ), the question arises to what degree impression management also takes place in this case, for example through the reporting bias (Beattie and Jones 2000 ). I suggest investigating this subject empirically, for example through archival studies.

Research Gap 3: To what extent does CEO impression management occur through visualization use in strategy presentations?

Fourth, while overconfidence in managerial decision-making is a commonly reported issue with significant efforts to develop corrective feedback as a remedy (Chen et al. 2015 ), there is little understanding of the role of information visualization in this matter. My review has demonstrated that visual aids often increase decision confidence as much as they improve the judgment itself (Yildiz and Boehme 2017 ) or even more (Sen and Boe 1991 ), but can also reduce confidence, particularly where uncertainty information is depicted (Dong and Hayes 2012 ). However, the latter effect was only studied for topics unrelated to management. Therefore, there is a complete lack of understanding of the effects of visualizations on managerial overconfidence, and I suggest exploring this research gap empirically with practitioners.

Research Gap 4: How do visual aids influence overconfidence in managerial decision-making?

Finally, a large share of cognitive psychology research discusses the effectiveness of visualization use through the reduction of cognitive load, yet they usually start off with low-load contexts, which is the opposite of high-stress managerial decision-making (Laamanen et al. 2018 ). Allen et al. ( 2014 ) find evidence that the effectiveness of distinct graph types changes with the level of externally induced cognitive load, raising the question to what extent previous insights on helpful visual aids are applicable to managerial decisions in a high-stakes environment filled with distractions and parallel issues requiring attention. Therefore, I suggest studying visualization use in experimental environments with varying levels of cognitive load as the independent variable, ideally with management practitioners and a realistic strategic task setting.

Research Gap 5: How does cognitive load influence the effectiveness of information visualization in strategic management decisions?

7 Conclusion

Information visualization has become ubiquitous in our daily professional and private lives, even more so with the advent of accessible and powerful computer graphics. However, the impact that visualizations have on human cognition and ultimately decisions stills remains unclear to a large extent. While the prevalence of visualization research across a plethora of application domains shows its pertinence, the decentralized approach has led to a scattered and unstructured field of theories and empirical evidence. My literature review thus sought to provide a far-reaching overview of this work and a detailed research agenda. As a result, three contributions arise from my review.

First, I provide an overarching structure to summarize the range of effects and interacting variables that can be found surrounding visualization research. This includes a wide set of dependent variables ranging from decision quality and speed to confidence and attitudes, as well as complex moderating and mediating effects that are crucial to understanding the overall power of visualizations. This precise framework is paramount to a holistic and comprehensive review of the scattered existing literature.

Second, to the best of my knowledge, my systematic literature review is the first on visualizations spanning the whole of social and information sciences simultaneously. While some previous reviews such as the one by Yigitbasioglu and Velcu ( 2012 ) utilize a multidisciplinary approach, they usually define the visualization type investigated more narrowly, for example by focusing on dashboards only. I believe that my integrative overview provides a valid contribution to the ongoing work to synthesize the mixed results in visualization research.

Third, I demonstrate that despite the plethora of evidence at first sight, visualization research is far from complete due to its multitude of moderating variables and at times conflicting results. Building on my systematic review of existing literature, I specify an agenda of potential research directions for future studies to follow in order to advance our understanding of the cognitive implications of visualizations in the context of managerial decision making in particular.

This paper also has direct implications for management practice. As Zhang ( 1998 ) points out, managerial decision-making is particularly well-positioned to profit from good visualizations since it often utilizes unstructured, large sets of information that are computer-centered, dynamic, and need to be interpreted constantly under time pressure. However, the interaction of visualization use with various factors should not be underestimated in the design of computer graphics for decision support. The high validity of the cognitive fit theory and the contingency on user characteristics found in the literature demonstrates that the designer should spend extensive time on clarifying for whom and what the visualization is intended. Furthermore, the potential for overconfidence and automatic processing based on visualized information may result in decision-makers skipping on more elaborate thought, which may be desirable in some, but certainly not all situations.

Availability of data and material

Not applicable.

Code availability

Thanks to the anonymous reviewer for encouraging me to extend my keyword search.

Thanks to the anonymous reviewer for this valuable impulse.

Thanks to the anonymous reviewer for pointing me towards additional, highly relevant articles.

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Eberhard, K. The effects of visualization on judgment and decision-making: a systematic literature review. Manag Rev Q 73 , 167–214 (2023). https://doi.org/10.1007/s11301-021-00235-8

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A strategic management process: the role of decision-making style and organisational performance

Journal of Work-Applied Management

ISSN : 2205-2062

Article publication date: 16 February 2023

Issue publication date: 24 April 2023

The purpose of this paper is to present a conceptual framework for integrating strategic thinking factors, organisational performance and the decision-making process.

Design/methodology/approach

The methodology involves a synthesis of literature and proposes a framework that explores the relationship between strategic thinking enabling factors, organisational performance and the moderating effect of decision-making styles.

The framework includes strategic thinking enabling factors (systems perspective, focused intent, intelligent opportunism, thinking in time and hypothesis-driven analysis), organisational performance and the moderating effect of decision-making styles (intuitive and rational).

Research limitations/implications

This research results in a conceptual model only; it remains to be tested in actual practice. The expanded conceptual framework can serve as a basis for future empirical research and provide insights to practitioners into how to strengthen policy development in a strategic planning process.

Originality/value

A paradigm shift in the literature proves that strategic management and decision-making styles are vital in determining organisational performance. This paper highlights the importance of decision-making styles and develops a framework for strategic management by analysing the existing strategic management literature.

• Strategic management
• Intuitive decision-making
• Rational decision-making
• Strategic thinking process
• Organisational performance

Sinnaiah, T. , Adam, S. and Mahadi, B. (2023), "A strategic management process: the role of decision-making style and organisational performance", Journal of Work-Applied Management , Vol. 15 No. 1, pp. 37-50. https://doi.org/10.1108/JWAM-10-2022-0074

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Copyright © 2023, Tamilarasu Sinnaiah, Sabrinah Adam and Batiah Mahadi

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1. Introduction

Managers are appointed to achieve the organisation's objectives and goals. As these objectives gradually increase with competition, managers must become strategic thinkers with excellent decision-making skills. The strategy towards the organisational outcome highlighted in this section has been widely debated among academic scholars and practitioners. Organisational strategies are essential in sustaining an organisation's competitive advantage to face a complex and uncertain future.

Effective strategic management frameworks enable managers to focus on the complex issues that must be prioritised to hasten decision-making processes ( Dlamini et al. , 2020 ). Whilst enabling managers important to make the decisions needed to direct the organisational effort towards overcoming specific issues ( Wang et al. , 2021 ). The organisation's effectiveness in addressing critical issues with solutions that best fit the current environmental factors will ensure the vitality and image of the organisation. Strategic management is pertinent to manage the organisation in a continuous, systematic manner.

The first segment of strategic management is the effective action programs chosen to reach these goals and objectives.

The second segment is the resource allocation pattern that relates the organisation to its environment.

Moreover, strategic management is defined as translating the thinking process into an action plan that benefits the organisation to sustain its competitive advantages. Strategy also can be categorised as strategic thinking and strategic planning. Strategy is also the commitment of the top-level management to attaining outcomes aligned with the organisation's strategic objectives. Strategy can be realised when there is consistent outcomes or patterns over the years. Therefore, strategy is planning for the future or determining patterns based on consistent outcomes. Organisations must develop plans and also evolve patterns derived from previous organisational outcomes. These phases can be explained as intended strategy and realised strategy.

The effectiveness of the strategies employed can indicate the organisation's performance in achieving its objectives and goals. Organisations need to measure the outcome of the strategies employed by having measurable objectives that will enhance the employees' commitment towards achieving the goals. Conversely, organisational learning and financial measures such as organisational profitability can also benchmark organisational performance. The responsiveness of organisational performance has a direct relationship and is influenced by management efforts to emphasise leadership within the organisational structure. This is done by observing the support and strategies utilised by managers to achieve the objectives and goals. This paper aims to enhance an understanding of strategic management processes involving decision-making styles towards organisational performance. First, this paper highlights strategic management's operational and theoretical approach towards organisational performance. Moreover, this study enhances the result of previous literature on strategic enablers by explaining the effort involving decision-making to strengthen the organisational structure, particularly the decision-making styles (intuitive and rational), that moderates the relationship between the strategic thinking process and organisational performances ( Ritter, 2014 ).

Academic scholars and practitioners have highlighted the importance of strategic management in measuring organisational performance in terms of innovation, entrepreneurship, technology, knowledge, economics, healthcare and organisational performance ( Adam et al ., 2018 , 2020 ; Alosani et al. , 2020 ). Conversely, there is a knowledge gap on the effective judgement practices of strategic management enablers and organisational performance during decision-making ( Abuhjeeleh et al ., 2018 ; Acciarini et al. , 2021 ; Elrehail et al ., 2020 ; Nguyen, 2020 ). This paper analyses the relationship between strategic management and organisational performance and suggests a framework to elucidate the relationship variables such as moderators, rational and intuitive decision-making styles.

2. Literature review

Strategic management is applying strategic decisions towards the organisational vision to achieve strategic competitiveness and sustain competitive advantages ( Alosani et al. , 2020 ; Rodrigues and Franco, 2019 ). Strategic management is a cognitive impairment of structuring the internal capabilities to fulfil external demands and involves plans, patterns, positions, perspectives and plots ( Mintzberg et al ., 2020 ). Strategic management is the managerial discourse involving a framework of the decision-making process, which highlights how the strategy process is formulated in organisations, acknowledging the cognitive management structure of the organisations. Additionally, the organisation's members need to respond effectually to the decisions made by the management and cooperate to ensure that the organisational vision is reached, given that this will affect the organisational adaptability, legitimacy and performance ( Johnsen, 2015 ). Organisations must be aware of the uncertain environments that can influence their welfare.

Consequently, the strategic management process can be reflected in two directions: strategic planning and strategic thinking. Strategic planning emphasises formulating strategies or disciplined efforts to produce strategic decisions to achieve the organisation's objectives ( Bryson, 2018 ). Strategic planning also can be reflected as a system that enhances the decision-making process among the members of an organisation. The strategic management process needs to be fulfilling for the organisation to sustain its competitive advantages. Moreover, strategic thinking is creative, disruptive, future-focused and experimental and often contradicts traditional notions of strategic planning ( Liedtka, 2000 ). Strategic planning is the principal element of the strategic management process involving resource management, implementation, control and evaluation of strategies ( Poister et al ., 2010 ). Strategic planning focuses on formalising existing strategies and employing creativity to enhance perspectives ( Mintzberg et al ., 2020 ). The uncertainties of environments and conflicting perspectives can be evaluated and addressed using strategic thinking as a part of the organisational decision-making process ( Chin et al ., 2018 ). Studies by Goldman et al . (2015) indicated that organisational members are not actively involved during the strategic decision-making process, leading to the decline in the organisation's performance.

The importance of the strategic decision-making process towards organisational performance was emphasised by Steptoe‐Warren et al. (2011) . The research suggested that evaluating, identifying and validating the process will enhance the strategic thinking process to positively impact performance ( Norzailan et al ., 2016 ). Moreover, strategic thinking plays a vital role in analysing the external factors influencing the process. If the organisational members take it lightly, it will lead to perception deficiencies ( Kızıloglu and Serinkan, 2015 ). Additionally, the study highlighted that strategic planning occurs after strategic thinking ( Alatailat et al ., 2019 ; Bonn, 2001 ; Mintzberg, 1994 ). Consequently, this study will focus on strategic thinking as the fundamental phase in the strategic management process.

A conceptual framework that highlights the management principles among the business process in delivering effective solutions for problems is shown in Figure 1 .

3. Strategic management

Strategic management is defined as a framework for achieving success, and it is pivotal for organisations to achieve their objectives and continuously perform better ( Elliott et al ., 2020 ). Additionally, strategic management is a continuous process of looking for a better action plan to ensure the organisation's competitiveness.

3.1 Strategic thinking

The most challenging issue an organisation faces is awareness of the strategic vision and missions, available resources and identifying opportunities for growth within the organisation ( Bryson, 2018 ). Therefore, strategic thinking is a vital element in the chain of processes, which must be carried out effectively and systematically ( Sahay, 2019 ). Nevertheless, organisations need to be aware that strategic thinking can fail miserly if the decision-makers do not realise the strategic enablers or the factors responsible for the effective strategic thinking process. Strategic enablers influence the thoughts and decision process of the organisational members ( Goldman et al ., 2015 ). Therefore, strategic enablers will lead the organisation's members towards idea growth and personal development, while strategic thinkers expedite the organisational performances ( Alatailat et al ., 2019 ).

Individuals involved in the organisational structure utilise their experiences and thought processes in managing conflicts to enhance strategic thinking ( Alaarj et al ., 2016 ). Strategy managers or thinkers recognise the relationship between business responsibilities and departments and organisations and their business stakeholders ( Cabral et al. , 2019 ). This relationship is known as “system thinking”, where an organisation explores the structure reflected in the action and environment that causes the incident. Additionally, the direction or the organisational destiny is a type of strategic intent utilised to help achieve the business objectives. This occurs when all the employees can concentrate on their purpose until it is achievable.

Strategic intent is pertinent in increasing competitive advantages and improving organisational performance ( Chen et al ., 2015 ). Intelligent firms must be considered before becoming competitive to ensure the organisation can create intelligent opportunities to lead the business emerging strategies towards their vision ( Alaarj et al ., 2016 ). Conversely, the organisation should integrate previous events with the current situation to achieve and align with the organisation's objectives. This is vital for organisations to connect to the past and present environment to envision the firms and prepare for any internal or external challenges in their business ( Abubakar et al ., 2019 ). A hypothesis-driven analysis is the core element in the strategic thinking process to gather relevant information regarding the business. Therefore, the challenges faced must be transformed into a hypothesis-driven analysis to understand better the measures needed to be taken by the stakeholders to improve the organisational performances.

3.2 Decision-making style

The role of managers within an organisation must be elucidated to help enhance the decision-making process to create competitive advantages for the organisation ( Dionisio, 2017 ). Moreover, Porter (1990) emphasised the differences between competitive strategy and competitors. Decision-making styles also play a vital role in formalising the strategic decision procedure and can be defined as a habitual or formal response pattern taken by managers when there is an incident ( Kulcsár et al ., 2020 ). According to Acciarini et al. (2021) , decision-making styles are directly related to cognitive styles involved in the strategic thinking process. Decision-making style, which can be both at individual and team levels, can be classified into intuition and rationality ( Dayan and Di Benedetto, 2011 ; Dayan and Elbanna, 2011 ; Giermindl et al ., 2022 ; Luan et al ., 2019 ; Sukhov et al ., 2021 ). Therefore, the author highlighted that cognitive styles could be divided into two different categories: “feeling as information evaluators”, where managers actively gather information intuitively, and “thinking as information evaluators”, where managers systematically collect information ( Behling et al ., 1980 ). Alternatively, decision-making styles can be considered intuitive and rational information gathering and evaluating styles ( Calabretta et al ., 2017 ).

The intuitive decision-making style can be defined as the episodes of uncertainty patterns of action imposed by managers or the decision-makers based on the current situation. In addition, intuitive decision-makers must be aware of current issues and relate the relationship between cognitive schemes with holistic thinking to resolve problems ( Calabretta et al ., 2017 ). It is also believed that the intuitive decision-making process can be influenced by a sudden awareness of information ( Zhu et al ., 2017 ). Decision-makers can determine solutions without fully understanding or realising the extent of information available. Studies agree that the intuitive decision-making process can occur when unsorted information is restructured into an organised pattern of action that transforms into a conscious solution ( Zander et al ., 2016 ). Furthermore, the intuition organisations performance is enhanced when decision-makers utilise the intuition decision-making style when there is no access or relevant analytical data to support them in making strategic decisions that align with the organisation's objectives ( Temprano-García et al ., 2018 ). Conversely, intuition decision-making also contributes positively to the organisations performance when the issues are resolved quickly despite limited resources or knowledge on the current issues.

Studies by Sauter (1999) emphasised that intuition decision-making or illumination is a sudden awareness of information where the decision-makers are unaware of fundamental facts or information. The author also highlighted several ways to establish the intuitive decision-making process. First, detection is an intuition where decision-makers think of several different situations rather than focusing on the current issue ( Kolbe et al. , 2020 ). Working on current strategic issues will enable managers to comprehend related information to help solve the issue by connecting facts or elements that previously did not relate to each other ( Temprano-García et al ., 2018 ). Another form of intuition is evaluation, where the solution appears as an available option creating a sense of certainty or vague feelings towards the analytical data ( Hodgetts et al ., 2017 ).

Conversely, the intuition decision-making process can also be hypothesised as an explicit and implicit decision-making style ( Tabesh and Vera, 2020 ), where explicit decision utilises feelings or emotion and implicit decisions refer to the experience of the relevant situation ( Bhat  et al ., 2021 ; Remmers et al ., 2016 ). Moreover, intuitive decision-making styles also utilise the subconscious processing of verbalised and nonverbalised facts or information ( Tabesh and Vera, 2020 ). A recent study suggests that intuitive decision-making aided managers in enhancing the strategic decision towards the organisation's performance ( Francioni and Clark, 2020 ).

Rational decision-making involves several solutions that will be analysed based on the issues and the relevance of this information towards the current problem before implementing the final decision ( Temprano-García et al ., 2018 ). The structured information consisting of conscious thinking must be evaluated critically ( Acciarini et al. , 2021 ). In addition, the rational decision-making process will enhance the effectiveness of the decision by structuring the decision criteria by highlighting and evaluating the alternatives individually ( Fitzgerald et al ., 2017 ). The decision-makers or the managers who utilise rational decision-making styles are more likely to be vigilant and organised about available information during decision-making ( Zhu et al ., 2021 ).

3.3 Organisational performance

For five decades, organisational performance has been widely researched by academic scholars and business practitioners ( Adam et al ., 2018 ). Organisational performance has been analysed in terms of normative and descriptive explanations in strategic planning research for continuous improvement in managing organisational performance ( Buddika et al ., 2016 ). Organisational performance can be explained by describing how things happen without judging good or bad. Alternatively, the organisational performance also can be elucidated by an evaluation in terms of performance against a benchmarked alternative or standard or by a descriptive statement explaining how the situation occurs without judgement ( Camilleri, 2021 ). Even though most research is done on the continuous improvements of organisational performance, practitioners still have many arguments and discussions on the terminology and conceptual bases to determine organisational performance ( Sarraf and Nejad, 2020 ).

Organisational performance can be reflected based on the results of the organisation's common objectives, given that the methods implemented are coherently used. Consequently, the performance processes' flow or the input resources can be critically analysed ( Tsai et al ., 2020 ). The effectiveness of organisational performance is influenced by the process implemented and can be measured by the achievements. Furthermore, organisational performance is defined as analysing the series of improvements to achieve organisational objectives. Generally, various factors can be associated with organisational performance, such as organisational structures, conflict, cross-cultural and social influences ( Sinnaiah et al. , 2023 ).

Performance measurement is a systematic series to identify the effectiveness and efficiency of people's behaviour to perform to their utmost abilities. Adam et al . (2018) described performance measurement as a unit, department or business process. Therefore, it is conceptualised that there is a structural relationship between organisational performance and performance measurement. Moreover, performance measurement requires substantive and relevant restructuring of input resources and processes to be aligned with the current system to increase productivity level or performance. Failure to analyse the performance measures will weaken the organisational strength and drain the organisation's efforts ( Alosani et al. , 2020 ). Thus, strategic thinking can be a highly effective performance measure for organisations.

4. Propositions

4.1 strategic thinking process and performance.

Strategic thinking is a structured assessment of analysing and synthesising information, intensively assessing the current situation and initiating new ideas or best available options to achieve strategic objectives ( Dhir and Dhir, 2020 ). An organisation's success depends on strategic thinking as it will enhance a decision-maker's skills, abilities and knowledge and help sustain competitiveness in uncertain environments ( Dhir et al ., 2021 ). Consequently, the process of strategic thinking is crucial for any organisation to successfully achieve and survive in the market for a more extended period. Decision-makers need to be effective and cognisant of the business opportunities that arise from innovating new ideas to enhance the strategic portfolio of organisations ( Bryson et al ., 2018 ).

Strategic thinking process will positively influence organisational performance.

4.2 Rational decision-making style, strategic thinking process and performance

In evaluating an organisation's performance and the uncertainties of the environment that influences the complexities in achieving positive growth for the organisation successfully, managers must have decision-making skills that utilise strategic thinking processes. Moreover, managers must be responsible for making fast and effective solutions by analysing, evaluating and prioritising available information to overcome strategic issues and obtain positive results ( Acciarini et al. , 2021 ). According to Calabretta et al . (2017) , there is a positive correlation between the strategic thinking process and decision-making style. Decision-making styles have the same structure as strategic thinking, which involves different levels, such as organisation or individuals.

Rational decision-making will moderate the relationship between the strategic thinking process and organisational performance.

4.3 Intuitive decision-making style, strategic thinking process and performance

Several studies highlight the roles of the strategic thinking process among managers within the boundaries of our cognitive capacities ( Kaufmann et al ., 2017 ) and postulate that mental flexibility can influence it ( Barlach and Plonski, 2021 ). Studies also emphasise that managers or decision-makers often utilise intuition during challenging situations, which is expected compared to the rational way of analysing the issues ( Kaufmann et al ., 2017 ). This intuition process can be a two-fold construct consisting of experience-based and emotionally affected situations. Additionally, this can involve a complex process of information affected by new cues towards previous experiences stored in their memory and transform it into subconscious action in the decision-making process ( Stanczyk et al ., 2015 ). Based on the study done by Simon (1976) , academic scholars and practitioners emphasised that managers are highly keen on inner feelings or gut feelings involving strategic decisions when faced with competitive issues ( Al-Jaifi and Al-Rassas, 2019 ; Bozhinov et al ., 2021 ; Palaniappan, 2017 ). The decision-making process utilising intuition uses available information, which might not have been available in the past, to quicken the process of decision-making. It is also important to realise that decision-making depends on the issues faced by the organisations, and not all issues require a rational decision-making style. For specific issues, managers might only need relevant information, deliberation and formal procedures to derive effective solutions for the organisation compared to instances where the managers are not bounded by any set of procedures or rules to solve the issue.

Therefore, strategic thinking is a process of synthesis, and based on intuitive decision-making style, where the outcome is an integrated perspective of the enterprise, managers can utilise intuition decision-making style to arrive at a solution with complete freedom and flexibility towards the organisational performance. The decision-makers attempt to be involved in the decision-making process while being aware of the current issues and having a sense of relationship among the cognitive schemas with the approach of holistic thinking to determine the solution to the problem ( Khemka and Hickson, 2021 ). It is clear that the intuitive decision-making process would include the issues faced by the organisation in analysing the issues and synthesis ( Zhu et al ., 2017 ) although all the processes occur under the sense of relationship or perception. It is also believed that the intuitive decision-making process could be influenced by the decision-makers upon the sudden awareness of information ( Peng et al ., 2020 ), whereby the decision-makers could propose a solution without the understanding or realisation of why the facts are present.

Intuitive decision-making will moderate the relationship between the strategic thinking process and organisational performance.

5. Discussion and conclusion

This paper reviews strategic management involving the strategic thinking process, organisational performance and decision-making styles with extant empirical work transforming into propositions, with the ultimate goal being to integrate the strategic management process into a systematised and approachable process that needs a fast response. Strategic management plays a vital role in aligning the standard repertoire of an organisation's strategic thinking. Moreover, managers must realise that strategic thinking has a unique process that depends on the situation. The thinking process should be aligned with the specific scenarios to ensure the best solution can be implemented. To sustain competitive advantage, managers should be effectively involved in the strategic thinking process to positively impact their organisations ( Bryson et al ., 2018 ).

The importance of strategic thinking enablers (systems perspective, focused intent, intelligent opportunism, thinking in time and hypothesis-driven analysis) was emphasised in the strategic thinking process and organisational performance. The systems perspective exposes the importance of organisations understanding the relationship between functions and departments internally and externally. Furthermore, organisations need to consider the functional, business and organisation strategies towards a highly competitive environment ( Buddika et al ., 2016 ). Consequently, these systems perspectives will help organisations manage interactions effectively across all departments to enhance productivity. Focus on intent will guide the organisations towards achieving strategic objectives and resisting eccentricity ( Bromiley and Rau, 2015 ). Focus intent will positively aid organisations to be more competitive in the long run as the managers realise the sense of discovery in managing strategic objectives. Therefore, it will improve the performance and consciously push the organisation towards innovation by eliminating limitations and becoming high achievers. Conversely, intelligent opportunism will enhance the strategic objectives by creating new opportunities to be more competitive although the strategies do not align with the current vision of the organisation. This is where intelligent opportunism will play an essential role at the managerial level of the organisation to effectively communicate and measure organisational performances ( Camilleri, 2021 ).

Emerging strategies will boost the organisation's motivation and productivity and should be carefully evaluated from time to time as the future of the organisations might be projected based on the past performance. Therefore, the importance of swift thinking permits the strategic managers to purposefully analyse the mission and vision of the organisation over time. The right action at the right time will help the organisations sustain competitively and save the organisations from self-destruction by limiting the positive changes made to help improve the organisation's performance ( Adam et al ., 2018 ).

Maintaining the balance between thinking creation and cognitive processing ( Calabretta et al ., 2017 ) and enhancing organisational performance (education, financial, creative, innovation, e-commerce and quality) is a challenge faced when creating effective management strategies ( Adam et al ., 2018 ; Al-Jaifi and Al-Rassas, 2019 ; Alharbi et al ., 2019 ; Arvis et al ., 2018 ). In addition, based on previous theoretical perspectives, most of the research scenarios will be based on the governance mechanisms of management and the policy development impacts on organisational performance ( Abubakar et al ., 2019 ). Therefore, based on extensive empirical and conceptual research, strategic thinking processes positively contribute to measuring organisational performance. Based on previous research, this study infers that cognitive development plays an effective role in the segregation of control between strategic thinking, which serves as a barrier to becoming more competitive and innovative in the long run ( Adam et al ., 2018 ). In addition, this happens among employees and directly impacts the quality of the organisational harmonies, such as mutual respect, trust and welfare of the employees. A cognitive processing environment is the use of intuition and rationality in decision-making with equal importance. The managers utilise intuition decision-making styles to resolve unrelated information received. During the strategic thinking process, the managers will receive unsorted information without processed knowledge which will be later organised into sorted knowledge using intuition styles ( Zander et al ., 2016 ). However, the rational decision-making style focuses more on the analytical procedure to conclude an issue the organisation faces. This helps the managers build confidence in the solution by eliminating uncertainty during decision-making ( Zhu et al ., 2021 ). Moreover, managers will only accept solutions with clear and less ambiguous information (rational) compared to managers utilising a more subconscious style (intuition) when formulating solutions. Consequently, there will be conflict in the decision-making process within the organisations.

According to Boamah et al. (2022) , the effectiveness of decision-making styles can differ according to the situation and the dependents. Alternatively, both decision-making styles were highlighted as an alternative way of generating a problem–solution approach within organisations ( Kolbe et al. , 2020 ; Stanczyk et al ., 2015 ). This study argues that both decision-making styles have equal importance in resolving problem–solution approaches and can be a harmonious process to achieve an effective performance measure. This argument is supported by Acciarini et al. (2021) , Tabesh and Vera (2020) . Therefore, this study concludes that both decision-making styles (rational and intuition) positively impact the strategic thinking process and organisational performance. Based on the framework in Figure 1 , the proposed framework highlights the missing sections of cognitive processing among businesses when delivering effective solutions for a complex problem. Organisations have only emphasised human capital and treated it as a scarce resource that will determine the organisation's performance. This study proposed that future strategic management researchers should explore the thinking process literature's core principles to investigate policy development further. Future research should transform these academic initiatives into empirical research by implementing this proposed model.

Conceptual framework

Competing interests: The authors reported no competing interests.

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Acknowledgements

The authors acknowledge the administration of Azman Hashim International Business School, Block T08, Universiti Teknologi Malaysia, Johor, for providing the facilities and the PhD Scholar room during this research.

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Strategic Decision Making: Process, Models, and Theories

The theories and models underpinning strategic decision-making (SDM) are somewhat eclectic that demand multidisciplinary approach and appears non-differential from decision-making (DM) theories. This paper is a first attempt that puts the discipline into perspective of its coherent whole. We start by defining strategy and SDM in order to set the expectations for the rest of the paper. Next, we make an outline on the contribution of management science (MS) to SDM before establishing the relationship with MS and its application to micro, small, and medium enterprises (MSMEs). Subsequently, we make a discussion on the SDM process, SDM theories and models before concluding that the discipline has reached maturity.  

Business Management and Strategy  ISSN 2157-6068

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ORIGINAL RESEARCH article

Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: a hybrid structural equation modeling – artificial neural network approach.

• 1 School of Economics and Management, Harbin University, Harbin, China
• 2 School of Economics, Harbin University of Commerce, Harbin, China
• 3 Department of Finance, Harbin University, Harbin, China
• 4 Faculty of Business Management and Study, Nazeer Hussain University, Karachi, Pakistan

This study builds a conceptual model of strategic decision-making ability that leads to entrepreneurial performance (EP) based on the two-system decision-making theory and logical analysis. An empirical approach using structural equation modeling – artificial neural network (SEM-ANN) was performed to describe the linear and nonlinear relationships in the proposed model. The empirical results reveal that strategic decision-making abilities are affected by five factors: attention, memory, thinking, emotion, and sentiment, and whose influence mechanisms and degrees are varied. Results also describe that these abilities have a positive effect on overall EP. Therefore, results suggest that businesses’ strategic decision-making is usually strengthened when entrepreneurs have a clear understanding of these influencing elements, and the interaction between them leads to improved performance.

Introduction

Entrepreneurship is thought to be a means of performance and wealth development. With the information economy expanding so quickly, knowledge-based decision-making is seen as a key tool for success and prosperity ( Yang et al., 2018 ; Jiatong et al., 2021a ). The key factor influencing the performance and profitability of organizations is strategic decision-making ( Feng et al., 2022 ). Because, the willingness to take risks and make decisions, as well as organizational product invention and market innovation, are all linked to an individual’s entrepreneurial performance (EP) ( Li et al., 2020a ). Furthermore, there is a correlation between value creation and creative thinking, recognition of opportunities, as well as time, resources, risk, and other components sof strategic decision-making and EP ( Theriou and Chatzoudes, 2015 ; Li et al., 2020a ). In the current green environmental pressure, businesses must prospectively include competitive issues into their strategic plans to build innovative initiatives and achieve a foothold in the extremely competitive business world ( Abbas, 2020 ). Research showed that managers’ decision-making skill affects the organization’s innovative strategies. It is indeed important to research what affects and improves managers’ decision-making ability ( Pan et al., 2020 ).

Since the 21st century began, organizational culture has integrated IT and industries ( Shahzad et al., 2017 ; Agarwala and Chaudhary, 2021 ). Strategic decisions to engage in innovation are significantly influenced by global technological growth, which produces new patterns of doing business ( Xu et al., 2022 ). To increase management decision-making ability, enterprises should optimize the governance system, raise development awareness, design development plans based on long-term sustainable growth, and encourage enterprise strategy execution ( Feng et al., 2022 ; Hu et al., 2022 ; Wang and Liu, 2022 ). Furthermore, strategic decision-making behavior requires organizations to match the external environment and internal resource capabilities; however, most are constrained by internal resources and are unable to develop flexible strategies ( Li et al., 2020b ). Several studies use cognitive-behavioral theory to evaluate cognitive behavior, claiming that it is critical to organizational decision-making, especially in the fields of entrepreneurship, technology adoption decisions, social media, and consumer behavior ( Dwivedi et al., 2017 ; Chou et al., 2020 ; Wang et al., 2022 ). Strategic decision-making has not been linked to EP in previous research. Therefore, to fill a research void in organizational psychology, this study examines the factors that influence an entrepreneur’s abilities to make strategic decisions that ultimately lead to enhanced EP.

In a new current theory, behavioral change is used as a proxy for success or failure, and cognitive and affective aspects can be investigated as predictors of decision-making quality ( Feng et al., 2022 ). The level of cognitive acuity not only allows for more information sharing but also reduces the stress of controversy ( Kang and Lee, 2017 ). In a similar vein, as the importance of being able to make strategic decisions as an entrepreneur has increased, the study of such talents has attracted a significant amount of interest in both academic and business communities ( Bilancini et al., 2019 ). The link between influencing factors and the ability to make strategic decisions is complex because strategic decision-making involves a cognitive and psychological transformation process ( Narayanan et al., 2011 ). However, because the model is excessively complex, parameters are difficult to estimate, a huge amount of work is required, and the model is unstable, it is difficult to implement in practice ( Kaplan, 2011 ). Therefore, it is also essential to determine whether the main factors that influence strategic decision-making are linear or non-linear in their interaction with one another to steer the EP. The research aims to establish a linear and nonlinear model between a particular aspect and strategic decision-making ability. SEM and ANN are used to express nonlinear relationships between variables and have excellent self-learning abilities. Academics have given little attention to research structure due to its complexity. Lack of research hinders the establishment of a thorough research system to determine the impact on strategic decision-making.

The performance of entrepreneurial enterprises is the output of entrepreneurial activities at the organizational level and an important embodiment of entrepreneurial success. Although entrepreneurs have unique entrepreneurial advantages, their EP is uneven in the face of the dual uncertainties of the market and technology. The environment of an enterprise is characterized by dynamic, uncertain, and complex characteristics, rather than being in a static and stable state. Enterprises need to realize adaptability between their resources and their pursued opportunities according to their environment and develop a strategic plan that can best match external environmental opportunities with internal resources. Adaptation of a strategy is a process of “matching” or “matching the organizational resources” with the opportunities of the environment ( Ruobing et al., 2022 ). At present, there is a lack of relevant reasons in the academic circle, and only the difference in creativity characteristics during entrepreneurship is regarded as the influencing factor ( Jiatong et al., 2021b ; Xie et al., 2021 ), but the creativity characteristics are more the external expression of technical ability and transfer of knowledge ( Zhou et al., 2021 ), which do not reflect the personal characteristics that affect the major decision-making and management of enterprises. Therefore, the key research questions of this study are framed as (1) What are the cognitive and affective factors that affect strategic decision-making abilities? (2) What is the impact of strategic decision-making abilities on EP? This research conclusion can be filling the research gap in organizational psychology, and provide countermeasures and suggestions for the improvement of enterprise EP.

Literature review and hypotheses development

Enterprise strategic decision-making ability combines many ability factors. The research on strategic decision-making ability focuses on the composition of strategic decision-making ability and the factors that influence it ( Wally and Baum, 1994 ; Bilancini et al., 2019 ). According to the literature, strategic decision-making ability encompasses three abilities: first, the capacity to locate, predict and capture strategic opportunities by assessing the environment. Second, is the ability to make strategic decisions such as setting a goal and deciding on a business strategy. The third step is to integrate resources by selecting, acquiring, and utilizing resources ( Milliken and Vollrath, 1991 ; Wally and Baum, 1994 ). The study by Schønning et al. (2019) believes the strategic decision-making ability system has three dimensions: strategy analysis, strategy selection, and optimization, and adaptive and updating capacity. All elements work together to develop and perfect strategic decision-making. By carefully studying rational decision-making variables, a comprehensive and workable driving model is created ( Spanuth et al., 2020 ).

Furthermore, enhancing a person’s capacity for strategic decision-making allows one to better understand both the environment’s special traits and the ever-changing trend ( Bilancini et al., 2019 ). Strategic decision-making, especially in the realm of environmental sustainability, is the focus of academic research ( Bilancini et al., 2019 ). Since demographic characteristics are mostly related to human capital and theoretical explanations are limited, scholars have begun to study the impact of entrepreneurial and executive team characteristics on the quality of strategic decision-making ( Friedman and Carmeli, 2018 ; Feng et al., 2022 ). There is evidence from prior research suggesting a link between the aggression, core self-evaluation, and strategic decision-making abilities of the senior management team ( Clohessy and Acton, 2019 ; Gao et al., 2021 ).

Recently, there has been intense interest in how the psychological makeup of decision-makers affects their ability to make sound strategic choices. The cognitive approach, complexity, requirements, and variation are the primary intellectual considerations ( Forbes, 2007 ; Nadkarni and Narayanan, 2007 ). When seen from a cognitive viewpoint, the strategic decision-making process consists of three stages: environment scanning, interpretation, and action ( Kumbure et al., 2020 ). In this research, we break the capability of making strategic decisions down into three categories: scanning, interpretation, and action ability. The capability to scan the environment for crucial information. The aptitude to make insightful and original sense of the data one has gathered. The capacity for logical appraisal and judgment, followed by the selection of appropriate actions in response to the environment, is what we mean when we talk of behavioral ability ( Li et al., 2019 ).

Both an analytical, rational decision-making system and a heuristic, empirical system have been advocated in decision-making circles ( Bryant, 2007 ; Shukla et al., 2019 ). The former is slow because it needs your brain to do the heavy lifting of applying logic rules and calculating probabilities. Cognition also plays a significant role in this process, as it is a form of decision-making. The latter make snappier judgments because of less deliberate engagement and the use of prior experience or logical associations. It follows that the ability of strategic decision-making is constrained not only by the cognitive aspects of decision-makers but also by cannot to resist the influence of emotional variables ( Shahzad et al., 2022 ).

Cognitive factors

The term “cognitive ability” is used to describe a person’s innate and fundamental intellectual function. This includes the person’s capacity for learning, reasoning, and communication ( Chou et al., 2020 ). In theory, one’s logical prowess influences their decision to go out on their own. Furthermore, co-creation processes will aid in the development of better decisions ( Anugrah and Hermawan, 2019 ). Individuals with higher cognitive abilities may foresee market conditions and trends more accurately, and they are frequently able to respond to rapidly changing markets in a timely and suitable manner ( Bilancini et al., 2019 ). Attention, memory, and ideation are the most important cognitive factors. Through the selection and understanding of the development trend of things from a strategy perspective, a decision-maker with strong attention can receive rich information while ensuring objectivity and comprehensiveness of information ( Brosch et al., 2013 ). A keen eye can also detect the drawbacks of strategic decision-making in real time and alter and change it in response to information input from its implementation ( Jugovac and Cavallero, 2012 ). Simultaneously, attentiveness can assist in capturing the best chance for strategic judgments and ensuring the best return from strategic decisions ( Sabet et al., 2017 ). Therefore, the following hypotheses are proposed in this research:

H1a: Attention has a positive effect on scanning ability. H1b: Attention has a positive effect on interpretation ability. H1c: Attention has a positive effect on action ability.

Memory is the ability to remember things. Memory’s accuracy and persistence provide quick, quality decisions. Before conveying the input to the target language audience, it must be kept in long-term memory in the source language ( Yenkimaleki and van Heuven, 2017 ). Memory can store information and conclusions. When facing similar issues, it might leverage previous knowledge ( Stocco et al., 2018 ). Strategic decision-making is memory-based. Strong-memory strategic decision-makers can accurately repeat valuable knowledge, enhancing decision-making efficiency and degree ( Fechner et al., 2016 ). The large and deep network of information may help or traumatize employees’ sustainable innovation performance ( Wang et al., 2018 ; Wiseman et al., 2022 ), which becomes part of their memory. A good memory can provide strategic decision-making concepts, processes, approaches, insights, and lessons ( Bechara and Martin, 2004 ). Therefore, the following hypotheses are proposed in this research:

H2a: Memory has a positive effect on scanning ability. H2b: Memory has a positive effect on interpretation ability. H2c: Memory has a positive effect on action ability.

Ideation involves analyzing, synthesizing, reasoning, and judging based on perception. All decision-making plans and ideas are the results of mental processing ( Del Missier et al., 2015 ). Ideation-based explanations are independent of systems and tactics to generate ideas, such as leverage points as generation seeds. Strategically manipulating associative memory involves focusing on substructures ( Barbot, 2018 ).

Strategic decisions are constantly based on social, political, and economic situations. It’s done by analysis, synthesis, comparison, abstraction, and generalization, according to social-psychological demands ( Heidari and Ebrahimi, 2016 ). Analysis and prediction of variable components in strategic decision-making depend on ideation, and ideation cannot be separated from other elements that drive strategic decision-making ( Griessenberger et al., 2012 ). Therefore, the following hypotheses are proposed in this research:

H3a: Ideation has a positive effect on scanning ability. H3b: Ideation has a positive effect on interpretation ability. H3c: Ideation has a positive effect on action ability.

Affective factors

Affective factors are classified into two types: low-level, namely emotion, and high-level, or sentiment. Positive or negative emotions characterize people. Positive emotions inspire decision-makers to work hard and be entrepreneurial. In this scenario, strategic decision-making establishes a higher aim, and the tools to achieve it are stable and complete ( Treffers et al., 2020 ). While strategic decision-making in a negative emotional state reduces the decision-making goal and its measurements ( Shukla et al., 2019 ). In a catastrophe, we should be calm, positive, and sensible. Before significant decisions, we should restrict emotional reactions, establish a good group decision-making environment, and enhance cohesiveness ( Zhang et al., 2015 ). When people are overexcited, the brain’s exciting points are concentrated in one area and the other regions temporarily lose contact. Logic is almost lost, which hinders strategic decision-making ( Shukla et al., 2019 ).

H4a: Emotion has a positive effect on scanning ability. H4b: Emotion has a positive effect on interpretation ability. H4c: Emotion has a positive effect on action ability.

The sentiment is a sophisticated, rational, steady, high-level feeling that combines emotion, and ethics. It has a normative role in people’s behavior and is the inner incentive for heroic activity ( Kauffmann et al., 2019 ). Sentiment influences decision-making as a high-level emotion and social conduct including strategic decision-making. Moral decision-makers assess the impact of their decisions on the group, others, and society, not only on themselves ( Morente-Molinera et al., 2019 ). Novel-minded decision-makers can relinquish their interests, restrain their impulses, and focus on group unity and societal impact in entrepreneurial strategic decision-making that may lead to enhanced performance ( Sun et al., 2021 ). Therefore, the following hypotheses are proposed in this research:

H5a: Sentiment has a positive effect on scanning ability. H5b: Sentiment has a positive effect on interpretation ability. H5c: Sentiment has a positive effect on action ability.

The influence of strategic decision-making ability on entrepreneurial performance

The strategy formulation of entrepreneurial enterprises regards the external environment as an important part of the strategic decision-making process ( Chen, 2022 ). The scientific understanding of the environment, the accurate understanding of the characteristics, and changing trends of the environment, and seeking the best entrepreneurial path are the key to the success of the enterprise ( Zhou and Wu, 2018 ; Xi et al., 2019 ). Decision-makers with strong environmental scanning ability can collect more comprehensive internal and external related information about the enterprise, which is also conducive to interacting with external stakeholders, obtaining strategic information, and constantly reducing information bias through information supplement and correction ( Hongjia et al., 2010 ; Wang and Xu, 2019 ). Policymakers with strong interpersonal abilities can have a scientific understanding of the external competition, and economic, financial, and legal environment ( Sarker and Palit, 2014 ). Decision-makers with strong action ability can design more strategic decision-making plans. Provide a diverse perspective for policymakers to evaluate strategic programs. Through the discussion, the decision-makers can evaluate the technological innovation strategic solutions comprehensively and objectively and ensure that high-quality solutions are selected from the numerous technological innovation strategic solutions ( Yuetong, 2022 ). Therefore, strong strategic decision-making ability can make a suitable outline for enterprises in development and innovation. It points out the development direction of enterprises, guides entrepreneurial activities, and supervises the performance of entrepreneurship and entrepreneurship for a long time. Therefore, the following hypotheses are proposed in this research:

H6a: Scanning ability has a positive effect on EP. H6b: Interpretation ability has a positive effect on EP. H6c: Action ability has a positive effect on EP.

The conceptual model is presented here in Figure 1 .

Figure 1. Conceptual model.

Methodology

Selection of the study methods.

Structural Equation Modeling (SEM) has the advantage of simultaneously observing the relationship between variables and latent variables and the relationship between latent variables and latent variables and can also eliminate random measurement errors. Therefore, more accurate results can be obtained compared to the traditional regression analysis. Although the SEM method is widely used in empirical analysis research, only the linear relationship between the variables is considered in the analysis, which restricts its application depth. In order to make up for this defect of SEM, some scholars try to add interaction terms or quadratic terms to reflect the nonlinear relationship between variables ( Tuu and Olsen, 2010 ), but the SEM model after adding interaction or quadratic terms is too complex, has poor computation, stability, and large parameter estimation is difficult disadvantages, making this method is difficult to be widely used in practice. Artificial Neural Network (ANN) can realize function approximation, data clustering, mode classification, optimization calculation, and other functions, and can automatically adjust the connection weight between the network nodes to fit the non-linear relationship of variables ( Pasini, 2015 ). Applying ANN models can find complex linear and nonlinear associations between variables. Moreover, ANN models can perform more accurate predictions compared to linear analysis methods. However, the current topology structure of the current ANN model is mainly determined based on experience, and the neuronal nodes are often fully connected, which leads to the lack of a theoretical explanation of the path and degree of the influence between variables and neurons.

From the above analysis, we show that SEM is more flexible in reflecting causal relationships between variables than ANN but has limitations of difficulty to handle non-nonlinear relationships among variables. However, the ANN model can approximate the nonlinear relationship between the variables, and it poorly explains the input variable to influence the path and the degree of the output variable. To this end, we use a modeling approach combining SEM and ANN to test the series of hypotheses and theoretical models proposed in the paper. Structural equation-based model testing is performed using structural equation models as parameter estimation and hypothesis testing techniques to obtain the path of influence between variables. The SEM is based on the topology of the model and the structured ANN model according to the test results of the structural equation model.

Analysis method of structural equation modeling

Structural Equation Modeling consists of a measurement model and a structural model. The measurement model analyses the link between an observed variable and a latent variable through Confirmatory Factor Analysis (CFA). Three matrices represent SEM.

Equations (1, 2) are measurement models, X and Y represent observed variable vectors; Λx and Λy represent factor load matrix; ξ and η represent latent variable vectors; δ and ε represent measurement error vectors. Equation (3) is the structural model, B represents the “effect coefficient matrix of endogenous latent variables on endogenous latent variables,” Γ the “effect coefficient matrix of exogenous latent variables on endogenous latent variables,” and the residual items vector. Absolute and relative fitting indexes evaluate model fit. Model fit indexes are over 0.90, indicating that the data are well-fitted. The closer to 1, the better the model’s fit.

Analysis method of structural equation modeling – artificial neural network

The SEM results determine the SEM-ANN topology. Widely utilized in the research of nonlinear issues, it may fit the relationship between variables by altering the weights of connections between network nodes ( Foo et al., 2018 ; Shahzad et al., 2020 ). However, ANN topology is mostly determined by experience, and neurons are frequently connected. The model does not explain input and output variables, impact path, or degree between neurons. When ANN and SEM are integrated, SEM’s topology design determines the influence path of elements on strategic decision-making ability. ANN’s nonlinear mapping and self-learning abilities are utilized to fit the causal link among various elements. This overcomes linear and SEM parameter estimation difficulties and improves ANN’s topological structure. In this paper, SEM and ANN are used to model strategic decision-making ability.

Backpropagation (BP) neural networks are multi-layer feed-forward networks trained using error BP. BP trains the nodes’ connection weights. The BP algorithm separates forward and back propagation ( Tan et al., 2014 ). Forwarding propagation transfers the input sample signal from the input layer to the output layer. The number of extraneous variables determines input layer nodes. The connection between networks is determined by measurement variables and potential variables, and node connection weight is generated by load and path coefficient ( Shahzad et al., 2021 ). It calculates output error based on actual and expected output, distributes it to all layer nodes to obtain each node’s error signal, and uses this to alter weight. After the changes, forward propagation is reprocessed, and network output error is reduced. MSE and R 2 measure algorithm accuracy. Less MSE means higher algorithm accuracy. R 2 around 1 indicates a stronger model interpretation.

Experiments

Variables measurements and data source.

The measures of attention typically refer back to work by Jugovac and Cavallero (2012) regarding attentional focus, focus stability, focus allocation, and focus transfer. As far as how memory is evaluated, most people look to studies conducted by Luber et al. (2013) on memory speed, memory stamina, and memory correctness. Specifically, the work of Campos-Blázquez et al. (2020) is cited as a primary source for determining how to quantify agility, flexibility, and depth of thought in the realm of ideation. In terms of tolerance, optimism, melancholy, and rage, the studies of Goldstein et al. (2013) are the most frequently cited when discussing the quantification of emotional states. Kazmaier and van Vuuren’s (2020) work on measuring morality, responsibility, and reason plays a significant role in the field. Scanning proficiency is evaluated based on how quickly, cheaply, and effectively it can gather information ( Thomas et al., 2009 ).

According to Autier and Picq (2005) the ability to interpret information about one’s external environment, internal resources and capabilities, strengths and weaknesses, opportunities and threats, are the key factors in evaluating a person’s level of interpretive ability. The work of Ruigrok et al. (2006) is most often cited when discussing the many approaches, breadth of evaluations, and precision of selection available for gauging action ability. The measures of EP refer back to work by Baoshan et al. (2009) , from the financial indicators and non-financial indicators of two aspects, regarding cash assets, return on investment, Interest rate, return on assets, and a number of new product items. This paper adopts the following two ways of getting data: the first is to obtain the help of local government departments, send the electronic questionnaire via e-mail and collect it by mail; the second is to fill in the paper questionnaire and collect it on the spot. A total of 1,500 questionnaires were sent out, 1,247 were returned, and 1,126 were considered for analysis after removing those with unengaged responses, missing data, and other issues. The significance level for the independent sample t -test was 0.498 (>0.05), indicating that there was no discernible difference between the three groups and that they could be combined.

Empirical analysis based on structural equation modeling

Each variable’s reliability is tested using SPSS 21.0. The variables’ convergent validity, discriminant validity, and reliability were assessed using the SEM method in AMOS 24.0. Table 1 shows the findings.

Table 1. Test results of reliability and validity of variables.

The reliability study revealed that each variable’s Cronbach’s value was larger than the standard of 0.70, indicating that the latent variables were reliable. The CFA of measurement model showed x 2 / df = 1.031, <2.0; RMSE = 0.012, less than 0.05; GFI = 0.924, CFI = 0.998, TLI = 0.997, all exceeding the specified critical value of 0.90, measuring the overall fitness of the model. The model’s values coincide with the sample data. Each measure’s factor loading was above 0.70, indicating high convergent validity. Each variable’s CR exceeds 0.70. Each variable’s AVE was above 0.50, indicating high discriminant validity. The variable measurement model has a good validity structure in general and can be investigated further.

The AMOS 24.0 examines the SEM of the enterprise’s sample data and the structural model’s overall fitting degree using the SEM analysis method to test the conceptual model. From the absolute fitting index, we can see that x 2 /df = 1.074, Less than 3 to achieve significance; RMSEA = 0.008. The model presentation is satisfactory at this stage. All three measures of relative fit (NFI, GFI, and CFI) are higher than the theoretical requirement of 0.90. All the results from the statistical analysis were statistically significant. This supports the validity of the model.

Examining the path coefficient’s significance is a primary way that SEM helps researchers confirm their research hypotheses. Coefficients with positive signs indicate a significant positive relationship between the two variables. The hypothetical relationship does not true if the coefficient is not significant. This hypothetical relationship, as seen in Figure 2 and Table 2 , is briefly discussed below.

Figure 2. The fitting results based on structural equation modeling (SEM).

Table 2. Hypothesis test results.

As depicted in Figure 3 , a model of influencing elements of strategic decision-making ability based on SEM has been constructed by removing the hypothetical path that was rejected following the results of the verification.

Figure 3. Influencing factors model of strategic decision-making capacity based on structural equation modeling (SEM).

From the perspective of the relationship between cognitive factors and decision-making ability, the structural equation model results show that attention has no significant influence on action ability, memory on scanning ability, and thinking on scanning ability, which does not accord with the assumptions of this paper. The reason may be that some of the cognitive factors have not been effectively played because the strategic decision-makers make decisions.

From the relationship between emotional factors and decision-making ability, the structural equation model structure shows that emotion has no significant influence on paraphrasing ability, emotion on action ability, and emotion on scanning ability, which does not accord with the assumptions of this paper. May emotions have a negative impact on strategic decisions, as positive emotions make people optimistic or pessimistic about risk assessment of decision, strategic decision quality has a positive and negative effect, positive effect helps to improve the strategic decision quality, and vice versa. These two effects offset the board of social capital on technology innovation strategic decision quality influence is not significant.

Empirical analysis based on structural equation modeling – artificial neural network

Figure 4 shows a structured neural network model based on SEM results. In a structured neural network, five exogenous latent variables provide 17 inputs, while one endogenous latent variable generate five outputs. The first hidden layer represents an external latent variable, and there are five nodes. The second hidden layer represents the endogenous latent variable, three nodes. The third hidden layer represents the endogenous latent variable, one node.

Figure 4. A structured neural network model structure.

Using MATLAB neural network training model parameters, 788 training samples and 338 testing samples are used. Input, hidden, and output layers pick logistic. Maximum learning iterations (network steps) can be set to 2,000, target accuracy to 0.00, and learning efficiency to 0.1. MSE and R2t are determined using the trained model on test data. The RMSE of each test index is 0.1, and the maximum error is 0.2 ( Figure 5 ). The model converges better after 2,000 iterations. Each measurement index’s R 2 is over 0.5, suggesting acceptable model fitting.

Figure 5. Neural network test results.

Normalized neuronal connection weight reflects each factor’s influence. A neural network study reveals that various factors affect strategic decision-making in distinct ways. For scanning ability, attentiveness is 0.25 and emotion is 0.19. Ideation has the biggest weight (0.27), followed by memory and attention (0.21 and 0.20), and feeling (0.18). For action ability, ideation weighs 0.24, followed by memory, and sentiment. Strategic decision-making ability affect EP in distinct ways, scanning ability is 0.33, interpretation ability is 0.25, and action ability is 0.24 shown in Figure 6 .

Figure 6. Influencing factors model of strategic decision-making capacity based on structural equation modeling – artificial neural network (SEM-ANN).

This paper proposes an SEM-ANN-based method for measuring strategic decision-making ability, and empirical analysis proves its potential effectiveness. A structured neural network model can describe the link and influence between network nodes and increase interpret ability. ANN can illustrate nonlinear relationships between influencing elements in SEM. Neural network nonlinear fitting improves model fit. Attention, memory, ideation, emotion, and sentiment influence strategic decision-making. Five factors affect strategic decision-making. Learning to interact with others is important for personal growth and professional relationships. Entrepreneurs with strong cognitive abilities can build a large, high-quality social circle to promote their business and get the ultimate performance. This study has the following implications and we also provide the limitations and future research directions.

Theoretical implications

This research found that entrepreneurs can benefit from enhancing the following facets of their strategic decision-making abilities. Prioritize attention enhancement because it has a sizeable effect on scanning speed and a sizeable favorable effect on interpretation speed. Entrepreneurs should train themselves to observe things methodically and deliberately, with a focus on noticing minute shifts and variances in the decision-making environment and the psychology of those involved. Second, cognition enhancement should be a priority. The capacity for remembering information greatly aids in understanding and acting. A better decision-maker is one who actively works to develop their memory, studies the art of memory, and places a premium on using the memory method. Third, think about how to better your ideas. Entrepreneurial ability to generate new ideas is the single most important aspect of their intelligence. They should be adept at letting their imaginations run wild, challenging conventional wisdom, and looking at issues from many angles. In the fourth place, try to consciously hold on to a positive feeling. If you want to avoid being swayed by your emotions when deciding, you should make your choice after the excitement has died down. To foster settings conducive to evidence-based decision-making, decision-makers must acquire the skills necessary to consciously nurture and cultivate noble sentiments. The government should allocate funds to improve the quality of required education and training.

Practical implications

The research conclusion of the paper enrich and strengthen the evidence of the positive influencing factors of strategic ability, empirically prove the relationship between strategic decision-making ability and EP, and put forward practical suggestions for improving decision-making ability and EP. However, the following limitations remain, and the data cross-section design makes the study results have some common methodological bias. Although the CFA was passed, the relevant impact is still not completely excluded, and future studies can use a longitudinal design for further verification.

Limitations and future directions

Apart from the implications, our study has a few limitations that could be covered by future researchers. First, this cross-sectional study used data from a single source, which may introduce methodological biases and causal constraints when assessing relationships. Single-source data are unsuitable for robust findings, even if we have tried to mitigate typical procedure bias and found no bias. To validate the model, we recommend collecting data on various time lags in future research. Second, the data is collected only for one country which may neglect the potential effect of cultural aspects. Therefore, it is also recommended that future scholars used cross-cultural data to analyze the EP by considering various cultural factors.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

JF: conceptualization, methodology, data curation, and writing – original draft preparation. PH: supervision, fund acquisition, and project administration. WZ: software, formal analysis, and data curation. AK: validation and writing – review and editing. All authors equally contributed to revising and finalizing the manuscript.

This work was supported by the Central Government’s support for the Reform and Development of Local Colleges and Universities Fund Talent Training Project.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : strategic decision-making, entrepreneurship, performance, cognitive, SEM

Citation: Feng J, Han P, Zheng W and Kamran A (2022) Identifying the factors affecting strategic decision-making ability to boost the entrepreneurial performance: A hybrid structural equation modeling – artificial neural network approach. Front. Psychol. 13:1038604. doi: 10.3389/fpsyg.2022.1038604

Received: 07 September 2022; Accepted: 12 October 2022; Published: 31 October 2022.

Reviewed by:

Copyright © 2022 Feng, Han, Zheng and Kamran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jiaying Feng, [email protected] ; Ping Han, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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As technology plays an ever-increasing role in both business and everyday life, the volume of digital information is constantly expanding. Studies estimate that 90% of the global data currently in existence has been generated in the last two years. Modern businesses have access to unprecedented quantities of data. However, this information is only beneficial if it’s used effectively.  

Data-driven decision-making is the process of leveraging data to guide organizational practices and procedures. Everything from website traffic and social media metrics to sales records and customer reviews can provide invaluable insights into a business’s strengths and weaknesses. By analyzing data and using it to inform business decisions, companies can streamline operations, increase customer satisfaction, mitigate risks, and achieve other strategic goals. Continue reading to discover the benefits of data-driven decision-making and explore how high-performing businesses harness data to make strategic choices.

Benefits of Data-Driven Decision Making 

According to Forbes , businesses that make decisions based on data are 19 times more likely to remain profitable and 23 times more likely to outperform competitors in customer acquisition. Gone are the days of relying on speculation to guide business practices. In today’s technological world, companies can ground their approaches in verifiable evidence, enhancing decision-making accuracy and precision. 

Data-driven decision making (DDDM) can positively influence nearly every aspect of a business, including: 

• Strategic planning. Government agencies, nonprofits, corporations, and small businesses alike draft strategic plans to articulate their short- and long-term goals. By aligning proposed methods with reliable data, these organizations ensure that their plans are informed, achievable, and measurable. 
• Operational efficiency. Through data analysis, organizations can identify delays, errors, and wasted resources that reduce productivity and profitability. They can track operational key performance indicators (KPIs) such as warehousing costs and downtime rates to find areas for improvement, enabling them to address these problems and optimize workflows. 
• Transparency and accountability. When decisions are based on objective data, internal and external stakeholders can see the rationale behind business practices. Data-driven decision-making ensures that personal biases or other irrelevant factors don’t influence policies and procedures.
• Resource allocation. In an era marked by supply chain disruptions, inflation, and staffing shortages, effective resource utilization is crucial. Organizations can use data to make informed decisions regarding task prioritization, resource allocation, and human capital management.
• Marketing. Website analytics, social media interactions, and customer relationship management (CRM) platforms are goldmines of information about customers’ needs and preferences. By aligning marketing strategies with data-derived conclusions, companies can ensure that their methods are as effective as possible. 
• Financial management. Through data-driven decision-making, organizations can identify financial risks, optimize budgeting, and improve cash flow management. 
• Forecasting. Statistical modeling and machine learning algorithms use historical data to forecast future events, allowing organizations to predict everything from financial trends to customer behavior. Forecasting empowers businesses to take a proactive approach that anticipates problems before they arise. 

 Steps in Data-Driven Decision Making 

• Identify objectives. To get the most out of the data analysis process, decision-makers should begin by establishing clear, measurable goals that align with the organization’s primary targets. For example, a company experiencing a revenue decline may set a goal to increase conversion rates on its e-commerce platform by 10%. Similarly, an organization struggling with inventory management may seek to determine the ideal inventory levels for its top-selling products.
• Collect data. Point-of-sale (POS) systems, financial reports, website traffic, and enterprise resource planning systems (ERPs) are just some of the many data sources that can lay the foundation for DDDM. Many organizations use business intelligence reporting tools that automatically gather and organize data in preparation for analysis. However, it's up to each business to understand the significance and applications of this information.
• Analyze data. Once the data is collected, businesses can use various digital tools and techniques to explore the relationships between variables, find correlations and patterns, and make predictions. Power BI, Tableau, and Apache Spark are examples of software programs that can manipulate and visualize data, enabling stakeholders to gain insights and extract actionable conclusions.
• Interpret results. The next step is to interpret the findings and understand their implications. For example, a marketing company may find that a particular advertising campaign had low engagement. Or a retail chain may notice a spike in sales during certain months of the year. By scrutinizing the patterns and trends illuminated during the data analysis process, companies can determine the best way to achieve their strategic goals.
• Make decisions. Finally, businesses use data-derived conclusions and insights to guide critical decisions. This may involve implementing new products and services, changing how they allocate resources, pivoting to a new marketing plan, or adjusting their pricing strategies. The applications for data-driven decision-making are nearly endless. 

Challenges in the Implementation of Data-Driven Decision-Making  

Using data to steer business strategies can yield incredible results. However, many organizations encounter obstacles during the data-driven decision-making process, including: 

• Inaccurate or inconsistent data. When data is flawed or incomplete, businesses can draw inaccurate conclusions. Data cleaning techniques can help fill in the missing information, account for outliers, remove duplicate data points, and correct inconsistencies so that data is ready for analysis. 
• Resistance to change. Sometimes called “organizational inertia,” businesses and their employees often favor the status quo— even when data-driven decision-making offers a better alternative. When a company changes how it handles important decisions, there can be significant backlash. One of the best ways to combat resistance to change is to foster a culture of transparency and collaboration. When people understand the reasons behind a change and can participate in the transformation, they’re more willing to embrace it.  
• Difficulty integrating data tools. Incorporating new technologies into existing workflows can also present challenges when adopting DDDM. Companies implementing data collection and analysis tools must ensure that the new technologies are compatible with the current infrastructure and will scale to accommodate fluctuating needs. Choosing programs with intuitive, user-friendly interfaces can also minimize the learning curve and ease the transition. 
• Concerns about data privacy. When collecting and storing data, companies have both an ethical and a legal obligation to ensure confidentiality. This is especially true in the finance and healthcare industry. DDDM initiatives must adhere to data protection laws such as the Health Insurance Portability and Accountability Act (HIPPA) and Gramm-Leach-Billey Act (GLBA).

Staying Competitive 

Data-driven decision-making allows organizations to make strategic choices, adapt to evolving conditions, and maintain a competitive edge. As a result, professionals who can gather, evaluate, and interpret data are in high demand. If you’re interested in elevating your skills and getting ahead in your career, earning a master’s degree in data analytics can be a great way to do so. 

At WGU, our M.S. Data Analytics degree is designed with input from industry leaders, so students are equipped with the knowledge and skills employers seek in candidates. Our programs are competency-based, meaning students can advance to the next course as soon as they demonstrate knowledge of the course material. Whether you’re a working professional fitting college into your busy schedule or just beginning your career journey, a degree from WGU can help you achieve your goals. Apply today!

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7 Ways to Improve Your Ethical Decision-Making

• 03 Aug 2023

Effective decision-making is the cornerstone of any thriving business. According to a survey of 760 companies cited in the Harvard Business Review , decision effectiveness and financial results correlated at a 95 percent confidence level across countries, industries, and organization sizes.

Yet, making ethical decisions can be difficult in the workplace and often requires dealing with ambiguous situations.

If you want to become a more effective leader , here’s an overview of why ethical decision-making is important in business and how to be better at it.

Access your free e-book today.

The Importance of Ethical Decision-Making

Any management position involves decision-making .

“Even with formal systems in place, managers have a great deal of discretion in making decisions that affect employees,” says Harvard Business School Professor Nien-hê Hsieh in the online course Leadership, Ethics, and Corporate Accountability . “This is because many of the activities companies need to carry out are too complex to specify in advance.”

This is where ethical decision-making comes in. As a leader, your decisions influence your company’s culture, employees’ motivation and productivity, and business processes’ effectiveness.

It also impacts your organization’s reputation—in terms of how customers, partners, investors, and prospective employees perceive it—and long-term success.

With such a large portion of your company’s performance relying on your guidance, here are seven ways to improve your ethical decision-making.

1. Gain Clarity Around Personal Commitments

You may be familiar with the saying, “Know thyself.” The first step to including ethics in your decision-making process is defining your personal commitments.

To gain clarity around those, Hsieh recommends asking:

• What’s core to my identity? How do I perceive myself?
• What lines or boundaries will I not cross?
• What kind of life do I want to live?
• What type of leader do I want to be?

Once you better understand your core beliefs, values, and ideals, it’s easier to commit to ethical guidelines in the workplace. If you get stuck when making challenging decisions, revisit those questions for guidance.

2. Overcome Biases

A bias is a systematic, often unconscious inclination toward a belief, opinion, perspective, or decision. It influences how you perceive and interpret information, make judgments, and behave.

Bias is often based on:

• Personal experience
• Cultural background
• Social conditioning
• Individual preference

It exists in the workplace as well.

“Most of the time, people try to act fairly, but personal beliefs or attitudes—both conscious and subconscious—affect our ability to do so,” Hsieh says in Leadership, Ethics, and Corporate Accountability .

There are two types of bias:

• Explicit: A bias you’re aware of, such as ageism.
• Implicit: A bias that operates outside your awareness, such as cultural conditioning.

Whether explicit or implicit, you must overcome bias to make ethical, fair decisions.

Related: How to Overcome Stereotypes in Your Organization

3. Reflect on Past Decisions

The next step is reflecting on previous decisions.

“By understanding different kinds of bias and how they can show themselves in the workplace, we can reflect on past decisions, experiences, and emotions to help identify problem areas,” Hsieh says in the course.

Reflect on your decisions’ processes and the outcomes. Were they favorable? What would you do differently? Did bias affect them?

Through analyzing prior experiences, you can learn lessons that help guide your ethical decision-making.

4. Be Compassionate

Decisions requiring an ethical lens are often difficult, such as terminating an employee.

“Termination decisions are some of the hardest that managers will ever have to make,” Hsieh says in Leadership, Ethics, and Corporate Accountability . “These decisions affect real people with whom we often work every day and who are likely to depend on their job for their livelihood.”

Such decisions require a compassionate approach. Try imagining yourself in the other person’s shoes, and think about what you would want to hear. Doing so allows you to approach decision-making with more empathy.

5. Focus on Fairness

Being “fair” in the workplace is often ambiguous, but it’s vital to ethical decision-making.

“Fairness is not only an ethical response to power asymmetries in the work environment,” Hsieh says in Leadership, Ethics, and Corporate Accountability . “Fairness–and having a successful organizational culture–can benefit the organization economically and legally as well.”

It’s particularly important to consider fairness in the context of your employees. According to Leadership, Ethics, and Corporate Accountability , operationalizing fairness in employment relationships requires:

• Legitimate expectations: Expectations stemming from a promise or regular practice that employees can anticipate and rely on.
• Procedural fairness: Concern with whether decisions are made and carried out impartially, consistently, and transparently.
• Distributive fairness: The fair allocation of opportunities, benefits, and burdens based on employees’ efforts or contributions.

Keeping these aspects of fairness in mind can be the difference between a harmonious team and an employment lawsuit. When in doubt, ask yourself: “If I or someone I loved was at the receiving end of this decision, what would I consider ‘fair’?”

6. Take an Individualized Approach

Not every employee is the same. Your relationships with team members, managers, and organizational leaders differ based on factors like context and personality types.

“Given the personal nature of employment relationships, your judgment and actions in these areas will often require adjustment according to each specific situation,” Hsieh explains in Leadership, Ethics, and Corporate Accountability .

One way to achieve this is by tailoring your decision-making based on employees’ values and beliefs. For example, if a colleague expresses concerns about a project’s environmental impact, explore eco-friendly approaches that align with their values.

Another way you can customize your ethical decision-making is by accommodating employees’ cultural differences. Doing so can foster a more inclusive work environment and boost your team’s performance .

7. Accept Feedback

Ethical decision-making is susceptible to gray areas and often met with dissent, so it’s critical to be approachable and open to feedback .

The benefits of receiving feedback include:

• Learning from mistakes.
• Having more opportunities to exhibit compassion, fairness, and transparency.
• Identifying blind spots you weren’t aware of.
• Bringing your team into the decision-making process.

While such conversations can be uncomfortable, don’t avoid them. Accepting feedback will not only make you a more effective leader but also help your employees gain a voice in the workplace.

Ethical Decision-Making Is a Continuous Learning Process

Ethical decision-making doesn’t come with right or wrong answers—it’s a continuous learning process.

“There often is no right answer, only imperfect solutions to difficult problems,” Hsieh says. “But even without a single ‘right’ answer, making thoughtful, ethical decisions can make a major difference in the lives of your employees and colleagues.”

By taking an online course, such as Leadership, Ethics, and Corporate Accountability , you can develop the frameworks and tools to make effective decisions that benefit all aspects of your business.

Ready to improve your ethical decision-making? Enroll in Leadership, Ethics, and Corporate Accountability —one of our online leadership and management courses —and download our free e-book on how to become a more effective leader.

About the Author

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Temporal Abstractions in Multi-agent Learning

Learning, planning, and representing knowledge at multiple levels of temporal abstractions provide an agent with the ability to predict consequences of different courses of actions, which is essential for improving the performance of sequential decision making. However, discovering effective temporal abstractions, which the agent can use as skills, and adopting the constructed temporal abstractions for efficient policy learning can be challenging. Despite significant advancements in single-agent settings, temporal abstractions in multi-agent systems remains underexplored. This thesis addresses this research gap by introducing novel algorithms for discovering and employing temporal abstractions in both cooperative and competitive multi-agent environments. We first develop an unsupervised spectral-analysis-based discovery algorithm, aiming at finding temporal abstractions that can enhance the joint exploration of agents in complex, unknown environments for goal-achieving tasks. Subsequently, we propose a variational method that is applicable for a broader range of collaborative multi-agent tasks. This method unifies dynamic grouping and automatic multi-agent temporal abstraction discovery, and can be seamlessly integrated into the commonly-used multi-agent reinforcement learning algorithms. Further, for competitive multi-agent zero-sum games, we develop an algorithm based on Counterfactual Regret Minimization, which enables agents to form and utilize strategic abstractions akin to routine moves in chess during strategy learning, supported by solid theoretical and empirical analyses. Collectively, these contributions not only advance the understanding of multi-agent temporal abstractions but also present practical algorithms for intricate multi-agent challenges, including control, planning, and decision-making in complex scenarios.

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• West Lafayette

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Advisor/supervisor/committee co-chair, additional committee member 2, additional committee member 3, usage metrics.

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Research reveals visuals' crucial role in strategic decision-making

by Lele Sang, University of Michigan

Management consultants and professors seem to be obsessed with visuals. When it comes to strategy, they either pull out their impeccable slides, replete with graphics ranging from a SWOT analysis to Porter's Five Forces to the Strategy Canvas, or they pick up a marker to sketch out their own frameworks on a whiteboard.

This phenomenon has piqued the interest of Felipe Csaszar, professor of strategy at the University of Michigan's Ross School of Business. Csaszar and colleagues Nicole Hinrichs of EHL Hospitality Business School in Switzerland and Mana Heshmati of the University of Washington set out to investigate the role of visuals in strategic decision-making processes.

Their research, published in the Strategic Management Journal , not only sheds light on why visuals are pervasive in strategy, but also reveals how visuals significantly impact decision quality.

Drawing on insights from cognitive science and organization theory, the study finds that visuals play a crucial role in improving four cognitive functions essential for solving strategy problems.

"They include working memory, long-term memory, pattern recognition , and knowledge transfer and transformation," Hinrichs said.

Csaszar says that employing visuals while solving a strategy problem resembles expanding a computer's memory, allowing it to process more data and generate more accurate results. Without visuals, comparing the pros and cons of alternative scenarios could lead to errors due to the limits of human working memory.

Further, strategy problems are often multidimensional, posing a challenge for managers to remember which dimensions to consider. Visuals serve as mnemonic devices, aiding managers in remembering these dimensions—for instance, the graphical form of Porter's Five Forces helps their recall.

In addition, visuals assist in pattern recognition, such as when displaying sales as a graph facilitates the detection of trends. Visuals also enhance knowledge transfer by offering succinct and adaptable formats for expressing and absorbing complex information. Lastly, they foster the co-creation of new knowledge by bridging differences and creating a shared understanding among individuals with diverse perspectives.

The study also finds that visuals not only serve as tools to assist managers in navigating the complex and high-stakes nature of strategy decisions but also influence the quality of decision-making.

"Visuals with greater usability and malleability are more likely to help experienced managers identify superior solutions," Heshmati said.

Usability refers to the extent to which helpful information can be extracted from visuals and depends on two mechanisms: pre-attentive processing, which allows users to quickly detect patterns without conscious attention, and Gestalt principles, which help resolve ambiguities in visuals.

An example of a visual with high usability is the Strategy Canvas, which is used to compare the value proposition of firms. On the other hand, malleability is the extent to which it can be changed by those using it.

"Visuals are not mere decoration," Csaszar said. "Instead, they are powerful thinking tools that can enhance users' cognitive capabilities and aid in developing high-quality strategies."

Embracing visuals when making strategic decisions can lead to better decisions, and incorporating visuals when teaching strategy can facilitate understanding, retention and application of strategy concepts, fostering student development and strategic thinking.

Currently, the use of visuals in strategy mainly relies on traditional tools like paper, pen, markers and whiteboards. However, with the rise of new technologies such as artificial intelligence and virtual reality , there's potential to revolutionize this process. AI has the capability to analyze data and generate insights that can be integrated into visuals.

"Think about comparing pros and cons," Csaszar said. "AI can gather relevant information, merge inputs from different sources, and even suggest specific frameworks. It's both efficient and informative."

Virtual reality may allow for immersive and interactive visuals, which can contain more data yet still convey insights and allow for more and better collaboration.

While incorporating visuals into strategy carries many benefits, it is important to recognize that visuals can also pose risks. They may complicate decision-making by obscuring essential details or oversimplifying complex issues. Moreover, visuals can be manipulated in ways that benefit specific actors rather than the whole organization.

"Given that much is at stake when making strategic decisions, a nuanced understanding of how visuals shape decision processes is crucial," Csaszar said.

Journal information: Strategic Management Journal

Provided by University of Michigan

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AGI isn’t here (yet): How to make informed, strategic decisions in the meantime

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Ever since the launch of ChatGPT in November 2022, the ubiquity of words like “inference”, “reasoning” and “training-data” is indicative of how much AI has taken over our consciousness. These words, previously only heard in the halls of computer science labs or in big tech company conference rooms, are now overhead at bars and on the subway.

There has been a lot written (and even more that will be written) on how to make AI agents and copilots better decision makers. Yet we sometimes forget that, at least in the near term, AI will augment human decision-making rather than fully replace it. A nice example is the enterprise data corner of the AI world with players (as of the time of this article’s publication) ranging from ChatGPT to Glean to Perplexity. It’s not hard to conjure up a scenario of a product marketing manager asking her text-to-SQL AI tool, “What customer segments have given us the lowest NPS rating?,” getting the answer she needs, maybe asking a few follow-up questions “…and what if you segment it by geo?,” then using that insight to tailor her promotions strategy planning.

This is AI augmenting the human.

Looking even further out, there likely will come a world where a CEO can say: “Design a promotions strategy for me given the existing data, industry-wide best practices on the matter and what we learned from the last launch,” and the AI will produce one comparable to a good human product marketing manager. There may even come a world where the AI is self-directed and decides that a promotions strategy would be a good idea and starts to work on it autonomously to share with the CEO — that is, act as an autonomous CMO. 

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Overall, it’s safe to say that until artificial general intelligence (AGI) is here, humans will likely be in the loop when it comes to making decisions of significance. While everyone is opining on what AI will change about our professional lives, I wanted to return to what it won’t change (anytime soon): Good human decision making. Imagine your business intelligence team and its bevy of AI agents putting together a piece of analysis for you on a new promotions strategy. How do you leverage that data to make the best possible decision? Here are a few time (and lab) tested ideas that I live by:

Before seeing the data:

• Decide the go/no-go criteria before seeing the data: Humans are notorious for moving the goal-post in the moment. It can sound something like, “We’re so close, I think another year of investment in this will get us the results we want.” This is the type of thing that leads executives to keep pursuing projects long after they’re viable. A simple behavioral science tip can help: Set your decision criteria in advance of seeing the data, then abide by that when you’re looking at the data. It will likely lead to a much wiser decision. For example, decide that “We should pursue the product line if >80% of survey respondents say they would pay $100 for it tomorrow.” At that moment in time, you’re unbiased and can make decisions like an independent expert. When the data comes in, you know what you’re looking for and will stick by the criteria you set instead of reverse-engineering new ones in the moment based on various other factors like how the data is looking or the sentiment in the room. For further reading, check out the endowment effect . 

While looking at the data:

• Have all the decision makers document their opinion before sharing with each other. We’ve all been in rooms where you or another senior person proclaims: “This is looking so great — I can’t wait for us to implement it!” and another nods excitedly in agreement. If someone else on the team who is close to the data has some serious reservations about what the data says, how can they express those concerns without fear of blowback? Behavioral science tells us that after the data is presented, don’t allow any discussion other than asking clarifying questions. Once the data has been presented, have all the decision-makers/experts in the room silently and independently document their thoughts (you can be as structured or unstructured here as you like). Then, share each person’s written thoughts with the group and discuss areas of divergence in opinion. This will help ensure that you’re truly leveraging the broad expertise of the group, versus suppressing it because someone (typically with authority) swayed the group and (unconsciously) disincentivized disagreement upfront. For further reading, check out Asch’s conformity studies .

While making the decision:

• Discuss the “mediating judgements”: Cognitive scientist Daniel Kahneman taught us that any big yes/no decision is actually a series of smaller decisions that, in aggregate, determine the big decision. For example, replacing your L1 customer support with an AI chatbot is a big yes/no decision that is made up of many smaller decisions like “How does the AI chatbot cost compare to humans today and as we scale?,” “Will the AI chatbot be of same or greater accuracy than humans?” When we answer the one big question, we’re implicitly thinking about all the smaller questions. Behavioral science tells us that making these implicit questions explicit can help with decision quality. So be sure to explicitly discuss all the smaller decisions before talking about the big decision instead of jumping straight to: “So should we move forward here?”
• Document the decision rationale: We all know of bad decisions that accidentally lead to good outcomes and vice-versa. Documenting the rationale behind your decision, “we expect our costs to drop at least 20% and customer satisfaction to stay flat within 9 months of implementation” allows you to honestly revisit the decision during the next business review and figure out what you got right and wrong. Building this data-driven feedback loop can help you uplevel all the decision makers at your organization and start to separate skill and luck.
• Set your “kill criteria”: Related to documenting decision criteria before seeing the data, determine criteria that, if still unmet quarters from launch, will indicate that the project is not working and should be killed. This could be something like “>50% of customers who interact with our chatbot ask to be routed to a human after spending at least 1 minute interacting with the bot.” It’s the same goal-post moving idea that you’ll be “endowed” to the project once you’ve green lit it and will start to develop selective blindness to signs of it underperforming. If you decide the kill criteria upfront, you’ll be bound to the intellectual honesty of your past unbiased self and make the right decision of continuing or killing the project once the results roll in.

At this point, if you’re thinking, “this sounds like a lot of extra work”, you will find that this approach very quickly becomes second nature to your executive team and any additional time it incurs is high ROI: Ensuring all the expertise at your organization is expressed, and setting guardrails so the decision downside is limited and that you learn from it whether it goes well or poorly. 

As long as there are humans in the loop, working with data and analyses generated by human and AI agents will remain a critically valuable skill set — in particular, navigating the minefields of cognitive biases while working with data.

Sid Rajgarhia is on the investment team at First Round Capital and has spent the last decade working on data-driven decision making at software companies.

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