Linking business analytics affordances to corporate strategic planning and decision making outcomes

Business analytics (BA) systems are becoming increasingly important, especially in the information system and operations research literature as well as in practice (Yin and Fernandez 2020; Bange and Lorenz 2021). In this context, BA with the key aspect of decision-making (Vidgen et al. 2017, p. 3) is seen as a promising solution in multiple areas and thus also in strategic planning (Laursen and Thorlund 2017, pp. 17–44). Thereby, “[…] analytics serves planning by guiding the goals, decisions, and performance metrics of an organization with facts and data" (Troilo et al. 2016, p. 74). A more specific example in the context of strategic planning is the simulation of different scenarios (Sharma et al. 2014, pp. 438–439; Lepenioti et al. 2020; Rabia and Bellabdaoui 2022), which is becoming more and more important in the increasingly dynamic and complex environments and high volumes of data, such as from product-service systems (Klatt et al. 2011, p. 31; Delen and Ram 2018; Sakao and Neramballi 2020, p. 3). This application is supported by a survey of Bange and Lorenz (2021, p. 11) who indicated that decisions in companies are increasingly based on data with the aim of improving decisions, optimizing processes and reducing costs. Furthermore, the value of BA is comprehensively discussed and investigated in several studies. For example, as described by Ashrafia, et al. (2019), many studies deal with the relationship between BA and firm performance, agility, information quality, innovation capability and environmental turbulence (Côrte-Real et al. 2017; Richards et al. 2019; Duan et al. 2020; Anton et al. 2021). Against this background, studies found a positive relationship for example between the use of BA in strategic planning and the firm performance and therefore financial results (Miller and Cardinal 1994; Klatt et al. 2011; McAfee et al. 2012; Cao and Duan 2015; Aydiner et al. 2019).

However, the integration of BA is associated with challenges. Thereby, the organizational integration is one of the biggest challenges addressed in the literature (McAfee et al. 2012; Vidgen et al. 2017). Furthermore, as the works of for example Vidgen et al. (2017) and Bange and Lorenz (2021, p. 20) show, companies struggle in creating value from BA and the literature does not show clear results in which way BA creates value (Ashrafia et al. 2019, p. 1). Rather, as pointed out previously, past research has focused strongly on the effect of BA on the firm performance (Wolf and Floyd 2017, pp. 7–9) and show a limited consideration of the outcomes of strategic planning (Cao and Duan 2015; Kunc and O’Brien 2018). At the same time, the results of Cao and Duan (2015) and Kunc and O’Brien (2018) indicate that the interrelationships between the opportunities provided by BA and the influencing factors that lead to an increasing dynamic capability and organizational performance are very complex. In this context, the data-driven culture, collaboration and integration of BA experts are important building blocks (Tim et al. 2020; Cao and Duan 2015). Against this background, for example Abbasi et al. (2016) derived the questions "How can big data analytics improve business processes?" and "How do organizations/individuals/groups actually make decisions in the big data environment?" in their elaboration of a research agenda. Furthermore, research is calling for more qualitative research on BA affordances and their impact on strategic decision making (Cao and Duan 2015).

Since, to the best of our knowledge, the current literature does not provide a comprehensive overview of affordances of BA applications in the context of strategic planning and to what extant these support the organization, we see a research gap. Coming from the results of for example Cao and Duan (2015), Wolf and Floyd (2017) and Kunc and O’Brien (2018), we follow the affordance theory which is currently often used in Information System research (Dremel et al. 2020; Strong et al. 2014). The affordance theory provides a framework to analyse the relationship between technical objects and the social environment (Volkoff and Strong 2013). At the same time, the application of the socio-technical system theory (STS) further supports the analysis of the effects of an object on an organization (Seidel et al. 2013). Thus, based on the results of a systematic literature review, we conduct a qualitative analysis to derive affordances and cluster them according to the framework of STS. In doing so, we aim to answer the following research question: To what extant can business analytics support the corporate strategic planning and decision making? Thus, we support the organizational integration of BA in the context of strategic planning and decision making.

The remainder of this paper is organized as follows. First, the background information regarding the business analytics, strategic planning as well as the affordance theory and STS. Afterwards, the related research and points out their limitations against the background of the research gap identify above are presented. Furthermore, the following section contains a description of the applied research method in order to answer the research question. Following, we present the results of our research and finally, a discussion and conclusion are provided.

In general, the literature contains large communities dealing with strategic planning and business analytics. At this point, we would like to provide the relevant background on these focused topics as well as on affordance theory and STS applied in this work.

Based on a literature review and to the best of our knowledge, we found two related works focusing on the application of BA or related approaches and their relationships to constructs of strategic planning or in particular on BA affordances in a general context of strategic planning. First, Cao and Duan (2015) test hypotheses using questionnaire survey of UK enterprises to investigate the impact of BA on strategic decision making and organizational performance. The authors use the affordance theory with focus on decision-making affordances provided by BA through a data-driven culture. Thus, the authors come to the conclusion that BA supports the development of a data-driven culture. The data-driven culture should at the same time strengthen BA's impact on the decision-making affordances. At last, the data-driven culture and the decision-making affordances should have a positive effect on the strategic decision making and thus on the organizational performance. Furthermore, Cao and Duan (2015) found that BA supports the rational decision-making and concluded that there is a relationship between data volume and intuition in decision-making. Accordingly, Cao and Duan (2015) do not provide an overview of the affordances of BA but focus instead on decision-making affordances. However, they do not provide a more detailed analysis of these affordances either. Rather, the authors offer the finding that BA supports organizational performance and the rationality of decision-making as part of legitimation, which are according to Wolf and Floyd (2017) two of 16 outcomes of strategic planning.

Second, Kunc and O’Brien (2018) provide a mapping of tools of business analytics and activities of the strategy process from an operations research perspective. Therefore, the authors applied a literature review focusing on research of management and only to a limited extent on disciplines like computer science and information systems Kunc and O’Brien (2018, p. 5). Furthermore, the applied keywords are limited to analytics, strategy and business analytics. However, Kunc and O’Brien (2018, pp. 9–10) find that business analytics has the potential to support the strategy process and thereby often combines different approaches. On the one hand, BA is intended to support the strategy implementation and, on the other hand, especially the optimization of operational efficiency. Furthermore, at the beginning of the strategy process, BA offers a data basis for making decisions through real time information of the environment and issues of the organization. In addition, predictive analytics methods can be used to develop, test and compare different scenarios. Finally, the authors conclude that BA should consider both fact-based and experience-based information in the strategy process. Besides the limited data base, the work of Kunc and O’Brien (2018) does not focus on affordance theory but rather offers implications from which affordances can be derived.

In contrast, this paper provides a comprehensive overview and detailed analysis of the affordances of BA for strategic planning and decision making. By applying the STS framework and linking affordances to strategic planning outcomes, implications and propositions for research and practice are further derived to demonstrate the extent to which BA supports the strategic planning and decision making.

With the aim to understand how BA can support strategic planning, this paper applies the affordance theory and combines it with the STS. As required by for example Cao and Duan (2015), we build on the body of literature on BA and strategic planning and conduct a systematic literature review to derive affordances on an organizational level from the identified literature. Therefore, we followed the guidelines provided by Tranfield et al. (2003). In order to answer the research question, the derived affordances are assigned to the outcomes of strategic planning according to Wolf and Floyd (2017) which are influenced by the corresponding affordance.

For the identification of the relevant literature, the Elsevier's Scopus and Thomson Reuters' Web of Science database was searched in April 2022. Therefore, the following search string (1) was used to search the title, abstracts and keywords: (affordance* AND ("business analytic*" OR "data analytic*" OR "business intelligence" OR "decision support system" OR "predictive analytic*" OR "descriptive analytic*" OR "prescriptive analytic*")). The keywords are chosen according to the research aim and are based on the background provided in the background section.

The initial search revealed 112 hits. After the duplicates were removed, 95 hits remained for further screening. As recommended by Tranfield et al. (2003), the inclusion and exclusion criteria (see Table 4) were used to search the titles, abstracts, and the entire text in order to reduce the large number of publications to the relevant ones. Accordingly, the hits were reduced to 35 after screening the title, 22 hits after screening the abstract and eleven after screening the full text. In addition, we performed a backward and forward search using Elsevier's Scopus database. Ambivalent to the initial database search, we also searched keywords in the title, keywords and abstract. However, with the exception of the publications already screened, no other relevant publications could be identified after screening the title and abstract according to our inclusion and exclusion criteria.

In addition to the direct search for the affordances of BA, we searched the databases again using the following search string (2): ("business analytic*" OR "data analytic*" OR "business intelligence" OR "decision support system" OR "predictive analytic*" OR "descriptive analytic*" OR "prescriptive analytic*") AND ("strategic planning" OR "strategic decision making" OR "strategic decision-making" OR "strategic management"). Thus, the focus was not directly on the affordances of BA but on the application of BA in the strategic planning and decision making. The keywords are chosen according to the research question and topic and are based on the explanations in the background section. As in the first database search, the search was limited to the title, abstracts and keywords. In addition, to narrow down the search to the relevant publications, the search was filtered to the topics computer science, business management and accounting, decision science, economics, econometrics and finance.

The second database search resulted in 387 initial hits. Without duplicates, 316 publications were considered relevant for the screening process. The screening of the titles and abstracts using the inclusion and exclusion criteria of Table 4 resulted in 109 respectively 70 publications. After screening the full texts, 22 publications were included in the final database. Due to the broad focus of the second search string a supplementary backward and forward search was omitted. Thus, the two-step data collection process results in 34 relevant publications. Figure 2 illustrates flow diagram of the screening processes.

We performed a qualitative analysis of the focused publications in order to extract relevant affordances. Thus, with regard to the descriptions in Sect. 2.3, we searched for affordances – i.e. the possibilities to use BA in strategic planning and decision making. In this context, we followed the stepwise approach of Mayring (2000):

If possible, the affordances were taken directly from the text in a first step. In a second step, the use-cases of BA systems described in the publications that are relevant for strategic planning and decision making were extracted. In a third step, affordances were derived from the use-cases. At last, the derived affordances were combined and simplified if they were similar in terms of topic and content. Afterwards, the derived affordances were classified as basic or higher level and assigned to the dimensions according to the STS as well as to the outcomes of strategic planning according to Wolf and Floyd (2017).

As described in research method, we analysed the relevant publications and assigned the affordances according to the dimensions task, technology, actor and structure of STS (see Fig. 1). Furthermore, we assigned them to the proximate and distal outcomes of strategic planning and distinguished between basic and higher level affordances. The result is shown in Table 5. At this point, however, it should be noted that a clear assignment is not always possible. For example, the affordance of improving the data-driven culture could also be assigned to the structure dimension. However, based on the explanations in the background section, we rather understand the data driven culture as a capability of actors that should be improved through the use of BA.

Hence, 20 affordances could be derived in total. From the perspective of the outcomes, it can be noted that the proximate outcomes have an average of eight and the distal outcomes an average of seven links with the affordances. Furthermore, it can be noted that only the affordances 1.1 and 1.7 interact exclusively with distal outcomes. In general, the five outcomes organizational performance, quality of strategic decisions, strategic planning effectiveness, adaptation and dynamic capability show the most links to affordances. Thereby, it can be noted that the outcome strategic planning effectiveness is primarily linked to the affordances of the technology and actor dimension, the outcome organizational performance to the task dimension and the outcome dynamic capability to the actor dimension. At the same time, the five outcomes strategic change and renewal, realized strategy, strategic legitimacy, strategy communication and planned emergence show the lowest number of links with the affordances.

From the perspective of the dimensions of STS, the task dimension with seven affordances shows more links with outcomes than the other dimensions. Additionally, more publications focus on the task dimension than on the other dimensions. At the same time, it can be noted that the dimensions actor and structure are in average linked to seven outcomes. This generally indicates a high importance of these dimensions for the strategic planning and decision making, although more publications focus on the task dimension. Furthermore, the actor and structure dimensions are strongly influenced by higher level affordances. Due to the focus of this work and consequently of the publications examined, the affordance of supporting strategic planning and decision making, which is linked to all outcomes, was derived. Besides the affordance of supporting strategic planning and decision making, the five affordances 1.4, 3.1, 3.5, 4.2, and 4.1 have an above-average number of links to the outcomes. However, it can be noted that only affordance 1.4 is strongly represented in the literature. In the following, the individual affordances are briefly explained.

As Kunc and O’Brien (2018) and Cao and Duan (2015) show, our results also demonstrate that BA can support the strategic planning and decision making. In contrast to the results of Kunc and O’Brien (2018), which primarily refer to the task dimension, our analysis shows that affordances in other dimensions (technology, actor, and structure) also provide an added value for the strategic planning and decision making. Since neither the proximate nor the distal outcomes clearly show more links to affordances (MF1), we can conclude that BA influences both processes and long-term outcomes. Further, in contrast to the finding of Kunc and O’Brien (2018), we see evidence and possibilities that BA indicate a use including in the early phase of setting direction of the strategy process. This is supported on the one hand by the fact, that for example Cao and Duan (2015) describe that one affordance of BA is the definition of strategic directs and criteria for success. On the other hand, a study by Neubert and van der Krogt (2018, pp. 44–45) found that decision makers would use BA to identify for example market opportunities. These findings imply that organizations should consider BA as a support for the overall process and long-term success (MI1).

As described above, we were able to identify affordances on all dimensions of the socio-technology system theory. However, although Cao and Duan (2015) consider affordances of decision making – i.e. those of an activity – and do not focus on the affordances of the IT artifact BA, various parallels can be identified and added to the results. For example, our results on the actor dimension show that the affordance of improving data driven culture has many links with the outcomes of strategic planning and is thus an important factor for the strategic planning and decision making as identified as well by Cao and Duan (2015). As Tim, et al. (2020) and Wang, et al. (2020) describe, in addition to the official formalization of analytics as part of the organizations and through new use cases of analytics, the value and trust should be increased in order to improve the data driven culture. Additionally to the importance of the role of a data driven culture, our detailed analysis of affordances indicates that the actor and structure dimension and especially the data driven culture are primarily driven by the basic affordances of the task dimension and that the basic affordances of the task dimension can be influenced by the basic affordances of the technology dimension (Ilyés and Szekeres 2017; Lu et al. 2021, p. 701; Wang et al. 2020, p. 4). Furthermore, Wang, et al. (2020, p. 4) point out, that the data driven culture depends on regulations, structures und processes and hence depend as well on the structure dimension. At the same time, the new tasks and the associated demands on employees result in new structures that go hand in hand with the higher level affordance of developing hybrid teams in the organization (Gurcan and Sevik 2019; Wang et al. 2020). As Tim, et al. (2020) found, analytics experts are increasingly integrated into departments, supporting the cross-learning and collaboration as well as improving the dynamic capabilities. Additionally, a hybrid team structure and the associated different perspectives support the data driven culture and the integration of BA in an organization as well (Tim et al. 2020; Goede 2021). This importance of hybrid teams is also reflected in the number of links between this affordance and the outcomes of strategic planning (MF2). Coming from these findings, we see a need that research and practitioners should consider the complex correlations between the affordances and dimensions when investigating affordances and implementing BA. However, the data driven culture should be considered as a central affordance which is influenced by for example the task dimension and structure dimension. In this context, the results of Cao and Duan (2015) show that one challenge results from the interaction between the integration of BA which supports the data-driven culture and the data-driven culture which supports the effects of BA. Furthermore, our findings imply that the affordances of the technology dimension are the basis for multiple affordances and especially of the task dimension. Additionally, the development of hybrid teams should be supported since they not only support the development of a data driven culture but as well support the dynamic capabilities (MI2).

In the other direction, our results and those of Cao and Duan (2015), indicate that the data-driven culture will ultimately improve the organizational performance. However, considering the affordance of supporting strategic planning and decision making, we identified the affordances of improving dynamic capabilities as well as efficiency and effectivity in this context. For example, Côrte-Real et al. (2017) and Yan (2018) have shown that the dynamic capabilities depend on the knowledge that is collected and shared with partners. In this context, Yan (2018) refers to the process that generates knowledge and improves the dynamic capabilities. In particular, this process can improve collaboration, flexibility, data management and analysis, and ultimately the effectivity and efficiency of a BA system based process (Kunc and O’Brien 2018; van Rijmenam et al. 2019; Wang et al. 2020) (MF2). Hence, we draw the implication that the BA system based processes have a central role to transform the knowledges generated on the task dimension and by the redefined relationship between firm and customer into the dynamic capabilities as well as efficiency and effectivity of the strategic planning and decision making (MI2).

Furthermore, we found no evidence in our research that BA dominates the strategy process and strategic decision, as highlighted by Kunc and O’Brien (2018), and thus undermines the experience of strategic planners and decision makers. Rather, our findings indicate that BA supports the planning and decision making by gathering information and opening new perspectives, for example, through the use of data mining and the analysis of unstructured data (Alnoukari and Hanano 2017; Alnoukari 2021; Capurro et al. 2022). Likewise, Cao and Duan (2015) conclude that rational and intuitive planning and decision making complement each other (MF3). In addition, BA systems support the processing of simple tasks such as spreadsheet creation (Kościelniak et al. 2017, p. 72), which provides employees more time for complex tasks such as interpreting results (MF4). Against this background, and as also suggested by Kunc and O’Brien (2018), our results suggest that BA should be used as an integrative part of the strategy process. Hence, organizations should integrate new methods in the strategic planning in order to support rational and intuitive planning. Following on from this, the new perspectives resulting from the results of BA approaches should be taken into account. (MI3) Furthermore, organizations should proactively plan how they use the time gained by the support of BA approaches. (MI4).

As our results show, the actor and structure dimensions have on average many links with the outcomes. This indicates that the affordances of these dimensions strongly support strategic planning and decision making. However, the strength of the correlations between the affordances and outcomes are not measured in this study, it is possible to derive some indications. First, it can be noted that the actor and structure dimensions are mainly characterized through higher level affordances. As previously described, higher level affordances are more difficult to be influenced directly and primarily depend on basic affordances (Glowalla et al. 2014, pp. 14–15). In contrast, the technology and task dimensions have fewer links with the outcomes but are rather characterized through basic affordances (MF5). This implies that the actor and structure dimension should be influenced by the task and technology dimension. Thereby, as discussed earlier, the system based business process is a key interface (MI5). Second, our results show that BA focuses primarily on the outcomes quality of strategic decisions, strategic planning effectiveness, strategic thinking, organizational performance, and dynamic capability. Consequently, the remaining outcomes are not as strongly supported by BA (MF6). Against this background, organizations should focus on the combination of BA with other methods which support the remaining outcomes more strongly, as also suggested by Kunc and O’Brien (2018). For example, collaboration tools or agile methods might be appropriate (MI6). The main findings as well as the main implications are presented in Table 6.

Coming from the affordance theory and STS, we conducted an analysis of 34 publications to identify BA affordances in strategic planning and decision making. The identified 20 affordances were linked to the outcomes of strategic planning according to Wolf and Floyd (2017) to derive the opportunities of BA in supporting the strategic planning and decision making. In this way, the question: To what extent can business analytics support strategic planning and decision making? is answered.

In addition to the general finding that BA improves strategic decision making (Cao and Duan 2015), we take a new perspective and show through our results more detailed and multi-layered relationships to the outcomes of strategic planning. Thus, based on our results, we come to the following conclusion:

Against this background, our work has multiple theoretical implications. Thus, to the best of our knowledge, we have chosen the first research approach to investigate affordance and socio-technology system theory in the context of BA in the strategic planning and decision making. We also respond to the call for qualitative research, which adds another perspective to the strong quantitative research base on the impact of BA on the organizational performance. At first, we provide an overview of affordance of BA in the strategic planning and decision making. Second, we provide an attempt to link these affordances to the outcomes of the strategic planning. Third, we assigned the affordances to the dimensions task, technology, actor and structure of the STS. Based on this, we were able to identify initial correlations between basic and higher-level affordances. Thus, we provide a framework for further research to better understand the affordances and success factors of BA in the strategic planning and decision making.

Our work also shows practical implications. First, we show how BA supports the strategic planning and decision making and contribute to the understanding that correlations exist between affordances. Furthermore, by providing an overview of the affordances and the connections to strategic planning results as well as to the dimension of socio-technical systems theory, we generate a first overview to support practitioners in gaining a first impression of the topic, on the basis of which new approaches can be developed and existing ones can be optimized. Table 6 provides an overview of the main implications and propositions.

Nevertheless, our research is not without limitations and a need for further research. Since our research is based on the literature, future research should also take into account experiences from practice by conducting single case studies or expert interviews. In particular, current literature lacks empirical evidence of the correlations and effects between affordances for example based on longitudinal studies. However, as in any literature review, there is a possibility that relevant data are missed due to the data collection and screening process. Especially since the terms in the context of strategic planning and business analytics are not clearly defined and have many common characteristics. Nevertheless, we consider our data base to be reliable and valid, as we conducted a two-step literature review with, in the first step, a narrower focus on affordances of BA and, in the second step, a broader focus on BA in the strategic planning and decision making. In combination with a forward and backward search we achieve a solid and both quantitative and qualitative data basis. Furthermore, the objectivity and validity of our research are supported by the standardized data collection process as well as the inclusion and exclusion criteria which support the research questions, the use of established frameworks and the interrelationships to related research. Additionally, the application of the frameworks of STS and outcomes of strategic planning as well support the reliability of our research. However, the assignment of affordances to the outcomes, dimensions of STS and level of affordances is not always clearly given. One reason for the difficulty of classification is that we are the first to consider these dimensions in combination. Against this background and since our results show that the correlations between the dimensions are a success factor for realizing the potential of BA, future research could for example investigate correlations between individual dimensions of the STS in more detail. In this context, it could be useful to consider, for example, the results of Gaver (1991) who distinguishes affordances into perceptible affordances, hidden affordances, correctly rejected affordances and false affordances on the basis of the criteria of whether an affordance exists and whether perceptual information is present. Furthermore, the investigation of other methods and approaches in context of strategic planning and the analysis of the implications on the outcomes of strategic planning would provide valuable insights and further support the strength of BA.