Theorizing Data, Information and Knowledge constructs and their inter-relationship for effective Data Analytics

Good explanatory constructs for Data, Information and Knowledge, and related theory of their interaction, are central to efforts to generate valuable insights from the significant, evolving growth in Data. The central role of Knowledge within such a theory has been highlighted recently, as well as the importance of Learning and Research frames for Data Analytics. Building on these ideas, this paper briefly reviews several related literatures, for relevant ideas to enrich IS theory building. A consensus is found as to the complex, socially constructed nature of Knowledge or Knowing, and the importance of human sensemaking for theorizing how new insight or Knowledge is generated. The paper argues for an intuitive conceptual and practical distinction between Data (which exists as an independent, reified resource), and Information and Knowledge (both of which are embodied or embrained). It also highlights specific areas for further inter-disciplinary engagement and research within the context of Analytics

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers

Ghost in the Machine: Theorizing data knowledge in the Age of Intelligent Technologies

AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS literature. Our preliminary findings demonstrate unveiling data, balancing between intuition and data, acknowledging external and internal capabilities, and realizing data, as the four main concepts of data knowledge

Useful Products in Information Systems Theorizing: A Discursive Formation Perspective

Although there is a growing understanding of theory building in the information systems (IS) field, what constitutes IS theory remains the subject of intense debate. Following Weick recommendation to focus on the products of theorizing rather than on what theories are, we assemble and analyze 12 products (question, paradigm, law, framework, myth, analogy, metaphor, model, concept, construct, statement, and hypothesis) that are rarely discussed together in any depth in the IS field and combine them into a coherent theorizing framework. Drawing on Foucault thesis of discursive formation we characterize the unique role of each product in IS theorizing and illustrate the usefulness of the framework in relation to both classical IS theorizing in the form of media richness theory as well as next-generation theorizing

The Use of Business Analytics Systems: An Empirical Investigation in Taiwan’s Hospitals

This paper aims to develop a research model to examine the mechanisms by which business analytics capabilities in healthcare units are shown to indirectly influence decision-making effectiveness through a mediating role of absorptive capacity. We employed a survey method to collect primary data from Taiwan\u27s hospitals. Structural equation modeling (SEM) was used for path analysis. This study conceptualizes, operationalizes, and measures the business analytics (BA) capability as a multi-dimensional construct formed by capturing the functionalities of BA systems in healthcare. The results found that healthcare units are likely to obtain valuable knowledge as they utilize the data interpretation tools effectively. Also, the effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity

The Perils and Promises of Big Data Research in Information Systems

With the proliferation of “big data” and powerful analytical techniques, information systems (IS) researchers are increasingly engaged in what we label as big data research (BDR)—research based on large digital trace datasets and computationally intensive methods. The number of such research papers has been growing rapidly in the top IS journals during the last decade, with roughly 16% of papers in 2018 employing this approach. In this editorial, we propose five conjectures that articulate the potential consequences of increasing BDR prevalence for the IS field’s research goals and outputs. We discuss ways in which IS researchers may be able to better leverage big data and new analysis techniques to conduct more impactful research. Our intent with these conjectures and analyses is to stimulate debate in the IS community. Indeed, we need a productive discussion about how emerging new research methods, digital trace data, and the development of indigenous theory relate to and can support one another

Serving the IS Customer in Good Times and Bad: Pathways to Satisfaction and Value

Serving and satisfying customers are everlasting goals for organizations. This two-essay dissertation delves into two innovative ways in which a company and its information systems (IS) service providers can better serve internal and external customers. The first essay examines the concept of Data Monetization, whereby a company sells its customer data to upstream suppliers to ensure that the source company receives optimized inventory levels and unique consumer insights. In today’s era of big data, business analytics, and cloud computing, this case demonstrates that the elusive goal of data monetization has become achievable. In a second essay, we build on service marketing and social capital literature to understand factors that influence IS service recovery satisfaction following an IS service failure. This empirical study advances our theoretical understanding of internal customer satisfaction by theorizing that the success of IS service recovery depends on the way the IS Function (ISF) responds to an IS service failure and the ISF’s investment in building social capital with its internal customers. Essay 1 is a case study of a Fortune 500 drug store chain that has been successfully monetizing its data by selling it to its upstream suppliers. We present a four-stage model that illustrates the stages the retailer went through on its data monetization journey. We identify the characteristics of each stage that differ in the technical and analytical capabilities required, the type of trust built, the focus of the retailer’s information strategy, governance mechanisms, and the costs incurred and benefits achieved by various stakeholders. It was shown that a company could gain new revenue streams by selling its customer data while exploiting its suppliers’ technical and business analytical resources to ultimately serve the retailer’s customers. In Essay 2, we recognize that when IS service failures are encountered, IS service providers have to respond with an IS service recovery. Internal customers’ (employees) satisfaction with a recovery after a failure is important to restore an employee’s overall satisfaction with the ISF. We empirically examine the effect of the social capital shared between the ISF and employees as well as the dimensions of recovery procedures, interactions, and outcomes on IS service recovery satisfaction and overall satisfaction. Our results indicate that following a service failure, the recovery satisfaction has a direct effect on overall satisfaction with the ISF. We find that recovery procedures (effort and fairness) and the recovery outcomes (speed and level of recovery) influence recovery satisfaction. We do not find support that social capital dimensions affect recovery satisfaction; however social capital has a direct effect on overall satisfaction with the ISF. Moreover, we find that recovery interaction (apology and explanation) does not affect recovery satisfaction. These findings paint the picture whereby the ISF must continually build social capital to sustain overall satisfaction among employees but in the case of a IS service failure, employees are mainly concerned with being treated fairly and earnestly in getting their problem fixed fast and reliably, and they do not consider social capital or recovery interaction as factors that will make them more satisfied with the failure’s recovery

Agility and Resilience as Sources of Competitive Advantages a Theoretical and Empirical Investigation

Today’s hypercompetitive global climate makes lasting competitive edge unsuitable. Firms face increasing complexity due to the rapid entry and growth of internationalizing firms from emerging markets, technological breakthroughs, discontinuous innovation, and the uncertainties surrounding unexpected shocks transmitted across world markets, such as the Covid-19 pandemic. In this research, I examine how firms have built and applied two adaptive abilities (agility and resilience) to respond to environmental changes and disruptions to create sustainable competitive advantage. An agile organization is simultaneously a resilient organization. Despite agility’s increased relevance in the academy and practitioners\u27 publications, its epistemological and ontological analyses are superficial at best. Specifically, supported by inductive and deductive analysis, I bring clarity to agility’s concept and its boundary conditions. Thus, I propose an integrative multilevel framework of the antecedents, the enablers, and the outcomes of the process of agility performance. Moreover, through in-depth interviews with executives, I explore how agility and resilience manifested in emerging market multinational firms (EMNEs) enhance their competitiveness by using both adaptive abilities in their international operations. The findings reveal that all organizations possess some degrees of agility and resilience simultaneously as two faces of the same coin. Furthermore, agility and resilience are interdependent, comprising five common domains

Business Value of Big Data Analytics:A Systems-Theoretic Approach and Empirical Test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms