Judgement, Big Data-Analytics and Decision-making
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ItemPractical Wisdom and Big Data Dilemmas: The Case of the Swedish Transport Administration( 2021-01-05)Using big data in organizations has the potential to improve innovation, accuracy, and efficiency. Big data is also connected with risks for both the organization and society at large. It is therefore important to improve our understanding of potential consequences of implementing and using big data. We studied the Swedish Transport Administration to understand their attitude towards implementing big data for prediction of, for example, the need for road maintenance. The analysis identified four moral dilemmas that the organization deals with in connection to big data. We discuss these dilemmas from the perspective of practical wisdom. Practical wisdom is manifested in context-dependent actions connected to open-mindedness, reflection and judgment. It can be summed up as “the reasonable thing to do” in a unique situation where “not-knowing” is a helpful resource when making wise decisions. This paper seeks to shed light on the importance of practical wisdom when implementing big data.
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ItemInvestigating Insensitivity to Prior Probabilities in Merger and Acquisition (M&A) Decision Making( 2021-01-05)In this paper we investigate the high failure rates of Mergers and Acquisitions (M&As) over the last several decades, despite greater access to data, sophisticated business intelligence (BI) and data analytics (DA) tools, and work by industry professionals and academics to improve outcomes. We explore the possibility that the representativeness heuristic could play a role, and specifically, if prior probabilities are being ignored or discounted in M&A evaluations. We confirm our hypothesis using a regression discontinuity in time (RDiT) model and a two-way fixed effects model. By highlighting the negative consequences of this heuristic on management decisions, we promote the use of data-driven decision making and the role of analytics in formulating business strategy.
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ItemFactors Impacting the Influence of Analytic Capabilities on Organizational Performance in Higher Education( 2021-01-05)In response to changing fiscal needs and opportunities, higher education institutions have adopted new ways to use financial information for improved decision making. Drawing upon resource based theory we examine the connection between university level data analytic capabilities and organizational performance. We posit this relationship to exist through a serially mediated path of data-driven culture and data quality. The study provides empirical evidence that establishing a data-driven culture contributes to data quality which together result in increased organizational performance. The serial mediation pathway creates a positive effect between data analytic capabilities on organizational performance. This is critical information relative to both resource based theories and practical implications for higher education relative to beginning the investment cycle at the organizational culture level related to use of data.
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ItemCognitive biases in developing biased Artificial Intelligence recruitment system( 2021-01-05)Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people’s role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed.
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ItemBeyond the Hype: Why Do Data-Driven Projects Fail?( 2021-01-05)Despite substantial investments, data science has failed to deliver significant business value in many companies. So far, the reasons for this problem have not been explored systematically. This study tries to find possible explanations for this shortcoming and analyses the specific challenges in data-driven projects. To identify the reasons that make data-driven projects fall short of expectations, multiple rounds of qualitative semi-structured interviews with domain experts with different roles in data-driven projects were carried out. This was followed by a questionnaire surveying 112 experts with experience in data projects from eleven industries. Our results show that the main reasons for failure in data-driven projects are (1) the lack of understanding of the business context and user needs, (2) low data quality, and (3) data access problems. It is interesting, that 54% of respondents see a conceptual gap between business strategies and the implementation of analytics solutions. Based on our results, we give recommendations for how to overcome this conceptual distance and carrying out data-driven projects more successfully in the future.
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