Business intelligence and organizational learning: An empirical investigation of value creation processes
Introduction
The business value of information technology (IT) has been demonstrated repeatedly in the past decades [13], [36], [83]. However, a major shortcoming of this field of research has been its disposition to study the business value of overarching IT concepts instead of the value gained by specific classes of information systems. The primary goal of those general IT studies has been to capture the organizational effects attributed to all IT assets and capabilities available to the organization. Those studies have been complemented by specific IT studies, aimed at understanding the business value of specific platforms and systems, such as electronic commerce [105] and enterprise resource planning (ERP) [62], [66]. The contribution of the latter studies is based on the often implicit rationale that different technologies bring about different processes of value creation. Therefore, it is essential to understand the unique value creation mechanisms at play for each technology.
In terms of value creation, business intelligence (BI) appears to be among the most promising technologies in recent years, at least as reflected in the attitudes of IT executives [61]. However, despite this dramatic shift in investment patterns and value perceptions, little empirical research has addressed the value creation processes unique to BI systems (e.g., Refs. [33], [124]). Although some efforts have been made to capture how BI generates business value, it is safe to conclude that there is much to learn about the value creation processes induced by this dominant IT domain.
Against this backdrop, we seek to answer the following research question: What are the distinct mechanisms of value creation underlying the business value of BI? We argue in this paper that understanding the business value of BI requires the integration of general knowledge about the processes of IT value creation and specific knowledge about the features unique to BI deployment. We integrate the two by taking stock of well-established findings about IT value creation and adapting them to the context of BI value creation. In particular, general IT findings include the relationships observed in previous research among IT assets, IT capabilities, organizational resources, and business value [64], [79], [82], [102], whereas specific BI adaptations involve the distinction between operational and strategic BI capabilities and the moderating role of organizational learning [74]. We demonstrate that organizational learning is an important theoretical lens for understanding how BI creates business value, especially given that BI systems are deployed to facilitate decision support, environmental adaptation, and organizational innovation. Specifically, the framework of exploration and exploitation in organizational learning [76] is applied because of the conceptual fit between the two types of BI capabilities (operational and strategic) and the two mechanisms of organizational learning (exploitation and exploration). In concert with the resource-based view (RBV) of the firm [8], [122], these theoretical bases allow us to formulate a comprehensive research model of how the deployment of BI resources creates business value.
In this paper, we adopt the process view prevalent in the literature on general IT business value [64], [79], [80], [98], and we develop a research model that considers BI capabilities (operational and strategic) as mediating the effects of BI assets (BI infrastructure and BI team) on business value (operational and strategic). The research model also accounts for the moderating effects of exploration and exploitation on the relationships between assets and capabilities. The development of the research model is based on a comprehensive literature review, which shows that the diffusion of knowledge from the literature on IT value to that on BI value has been sporadic and inconsistent. A preliminary assessment of our research model relies on qualitative data collected in 11 interviews in three organizations. Subsequently, the model is tested with cross-sectional data collected from managers. Data analysis, using structural equation modeling (SEM), generally confirms the research model and shows that the lens of organizational learning and the distinction between operational and strategic BI capabilities are critical to understanding BI value creation.
The contribution of this study is attributed to our dual approach, which integrates insights gained from both general IT and specific BI research. The study therefore contributes to both streams of research. First, it contributes to BI research by providing a model of value creation specific to this domain, which, despite being a high-priority investment in many organizations, suffers from a lack of empirical grounding. BI research also suffers from insufficient theoretical development, and the present study demonstrates that organizational learning is a useful theoretical lens to further our understanding of BI value. Second, the study contributes to IT value research by showing that “opening the grey box of IS business value creation” [102,p. 149] may not be entirely possible unless value creation processes are grounded in a specific technological context. Finally, our dual approach is easily transferable to other technological domains, offering a promising avenue to advance research on domain-specific value creation processes.
This paper proceeds as follows: the next section provides the theoretical background, which leads to the development of the research model. The third section describes the research methodology used to test the research model, and the fourth section describes the data analysis and results. Finally, the concluding section discusses the key findings, contributions, limitations, and directions for future research.
Section snippets
Theoretical background and research model
The theoretical analysis begins with a short introduction of research on the business value of BI systems. We then present three consistent observations about the business value of IT; we also demonstrate, through a structured literature review, that the implications of these observations on the business value of BI have yet to be fully studied. This theoretical background is the foundation upon which the research model is constructed in the rest of this section.
Research methodology
The methodology for developing and testing the research model included two stages, exploratory and confirmatory. The first stage involved an exploratory analysis of the theoretical premises underlying the research model, in particular the relationships among BI assets, BI capabilities, and business value, as well as the distinction between operational and strategic BI capabilities. The moderating role of organizational learning was not explored at this stage because of the intricate nature of
Results
To analyze the collected data, we used covariance-based SEM techniques with the AMOS 20 software and maximum likelihood estimation (MLE). Although our sample size was close to the lower bound of the recommended sample size for covariance-based SEM [51], we preferred covariance-based SEM techniques over the partial least squares (PLS) techniques, because the former allows the assessment of the plausibility of the hypothesized research model through goodness-of-fit tests [46], [47]. The data were
Key findings
The findings of this study validate the reasoning that operational and strategic BI capabilities should be considered separately and that organizations may become ambidextrous in their BI capabilities in the same way they can become ambidextrous in their approach to organizational learning. The study advances the perception that business value is generated from BI assets via two parallel mechanisms, operational and strategic, based on two orthogonal sets of respective capabilities. This dual
Lior Fink is an associate professor at Ben-Gurion University of the Negev. He holds a bachelor’s degree in psychology and economics, a master’s degree in social-industrial psychology, and a Ph.D. degree in information systems from Tel Aviv University. Lior’s articles have been published in numerous journals including MIS Quarterly, European Journal of Information Systems, Information & Management, Information Systems Journal, Journal of the Association for Information Systems, Journal of
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Lior Fink is an associate professor at Ben-Gurion University of the Negev. He holds a bachelor’s degree in psychology and economics, a master’s degree in social-industrial psychology, and a Ph.D. degree in information systems from Tel Aviv University. Lior’s articles have been published in numerous journals including MIS Quarterly, European Journal of Information Systems, Information & Management, Information Systems Journal, Journal of the Association for Information Systems, Journal of Information Technology, and Journal of Strategic Information Systems. Lior currently serves as a Senior Editor for The Data Base for Advances in Information Systems.
Nir Yogev is a professional BI consultant for a company specializing in BI software and services. He holds a bachelor’s degree in industrial engineering and management and a master’s degree in information systems from Ben-Gurion University of the Negev. Nir’s research focuses on the business value of BI systems.
Adir Even received his DBA degree from Boston University School of Management and serves as a senior lecturer at Ben-Gurion University of the Negev, Israel. He explores the contribution of data resources to value-gain and profitability from both theoretical and practical perspectives, and studies implications for data warehousing, business intelligence, and data quality management. His research has been published in journals such as IEEE/TDKE, CACM, CAIS, DSS, IJBIR, and Database.