Purpose – Big data analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. The purpose of this paper is to explore information quality dynamics in big data environment linking business value, user satisfaction and firm performance. Design/methodology/approach – Drawing on the appraisal-emotional response-coping framework, the authors propose a theory on information quality dynamics that helps in achieving business value, user satisfaction and firm performance with big data strategy and implementation. Information quality from BDA is conceptualized as the antecedent to the emotional response (e.g. value and satisfaction) and coping (performance). Proposed information quality dynamics are tested using data collected from 302 business analysts across various organizations in France and the USA. Findings – The findings suggest that information quality in BDA reflects four significant dimensions: completeness, currency, format and accuracy. The overall information quality has significant, positive impact on firm performance which is mediated by business value (e.g. transactional, strategic and transformational) and user satisfaction. Research limitations/implications – On the one hand, this paper shows how to operationalize information quality, business value, satisfaction and firm performance in BDA using PLS-SEM. On the other hand, it proposes an REBUS-PLS algorithm to automatically detect three groups of users sharing the same behaviors when determining the information quality perceptions of BDA. Practical implications – The study offers a set of determinants for information quality and business value in BDA projects, in order to support managers in their decision to enhance user satisfaction and firm performance. Originality/value – The paper extends big data literature by offering an appraisal-emotional response-coping framework that is well fitted for information quality modeling on firm performance. The methodological novelty lies in embracing REBUS-PLS to handle unobserved heterogeneity in the sample.

Introduction

Big data has emerged as a new frontier for business in either establishing competitive advantages or exploiting untapped opportunities (Frisk and Bannister, 2017; Dubey et al., 2018; Prescott, 2014; Fosso Wamba et al., 2017; Akter et al., 2016; Hazen et al., 2014; El-Kassar and Singh, 2018). In every part of the world, industries and organizations collect more data than ever before, seeking smarter business strategies to harness this big data revolution. The extant literature identifies “big data” not only as “the next management revolution” (Mcafee and Brynjolfsson, 2012), but also as “the new raw material for business” (Economist, 2010), or “the new science that holds the answers” (Gelsinger, 2012). As it clearly appears in both the academic and practitioner literature, the increased attention to big data, and thus to big data analytics (BDA), is eloquent proof that the benefits of BDA are well acknowledged in any environment: better understanding of business, markets and consumers; higher productivity linked with profitability; and improved performance measurement mechanisms (Lavalle et al., 2011; Swafford et al., 2008; Mcafee and Brynjolfsson 2012; Elisabeth and Frank, 2017; Michael, 2014), amongst others. And all of these are constantly reflected in Google, Amazon, Harrah’s, Capital One, and Netflix’s business models. Companies aiming to leapfrog competition are increasingly interested in BDA to transform their business models, notably by customizing consumers’ desiderata, including when and how many they want, and what incentives will make them want more in their lifetime (Langenberg et al., 2012). However, despite the widespread buzz around BDA, leveraging BDA-driven information to generate business value continues to be a challenge for many organizations. This is why consulting firms such as Gartner, IBM and McKinsey & Co. have started providing services to help firms capitalize on this opportunity. The extant literature highlights that, “[a]s big data evolves, the architecture will develop into an information ecosystem: a network of internal and external services continuously sharing information, optimizing decisions, communicating results and generating new insights for businesses” (Sun and Jeyaraj, 2013). However, there are growing concerns and confusion regarding analytics-driven information quality (IQUL), business value (BVAL), user satisfaction (USAT) and firm performance (FPER) (Goes, 2014; Sun and Jeyaraj, 2013). Clearly, despite the paucity of research in this spectrum, a better understanding of IQUL dynamics is required in order to address the research gap. Because, “[w]hile generating quality information is the primary purpose of any IS [information system], few studies have explored the variables that affect Information Quality. This is a significant gap in the IS research. Quality information is a foundation of good decision making and positive outcomes, yet we know little about the variables that lead to improved Information Quality. More research is needed in order to understand better how to influence Information Quality” (Petter et al., 2013, p. 30).