Delineating the Business Value of Data-driven Initiatives in Organizations – Findings from a Systematic Review of the Information Systems Literature

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies: (1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives

Big Data Research in Information Systems: Toward an Inclusive Research Agenda

Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects

an important partnership for decades

Graesch, J. P., Hensel-Börner, S., & Henseler, J. (2021). Information technology and marketing: an important partnership for decades. Industrial Management and Data Systems, 121(1), 123-157. https://doi.org/10.1108/IMDS-08-2020-0510Purpose: The enabling technologies that emerged from information technology (IT) have had a considerable influence upon the development of marketing tools, and marketing has become digitalized by adopting these technologies over time. The purpose of this paper is to demonstrate the impacts of these enabling technologies on marketing tools in the past and present and to demonstrate their potential future. Furthermore, it provides guidance about the digital transformation occurring in marketing and the need to align of marketing and IT. Design/methodology/approach: This study demonstrates the impact of enabling technologies on the subsequent marketing tools developed through a content analysis of information systems and marketing conference proceedings. It offers a fresh look at marketing's digital transformation over the last 40 years. Moreover, it initially applies the findings to a general digital transformation model from another field to verify its presence in marketing. Findings: This paper identifies four eras within the digital marketing evolution and reveals insights into a potential fifth era. This chronological structure verifies the impact of IT on marketing tools and accordingly the digital transformation within marketing. IT has made digital marketing tools possible in all four digital transformation levers: automation, customer interaction, connectivity and data. Practical implications: The sequencing of enabling technologies and subsequent marketing tools demonstrates the need to align marketing and IT to design new marketing tools that can be applied to customer interactions and be used to foster marketing control. Originality/value: This study is the first to apply the digital transformation levers, namely, automation, customer interaction, connectivity and data, to the marketing discipline and contribute new insights by demonstrating the chronological development of digital transformation in marketing.authorsversionpublishe

Adaptive Big Data Analytics for Deceptive Review Detection in Online Social Media

The explosive growth of user-contributed reviews in e-Commerce and online social network sites prompts for the design of novel big data analytics frameworks to cope with such a challenge. The main contributions of our research are twofold. First, we design a novel big data analytics framework that leverages distributed computing and streaming to efficiently process big social media data streams. Second, we apply the proposed framework that is underpinned by a novel parallel co-evolution genetic algorithm to adaptively detect deceptive reviews with respect to different social media contexts. Our experiments show that the proposed big data analytics framework can effectively and efficiently detect deceptive reviews from a big social media data stream, and it outperforms other non-distributed big data analytics solutions. To the best of our knowledge, this is the first successful design of an adaptive big data analytics framework for deceptive review detection under a big data environment

Social Media Marketing Evaluation Decision Making Processes and the Agency-Client Relationship

Evaluation of social media marketing is central to its success. This thesis seeks to contribute to our understanding of social media marketing evaluation processes and outcomes, together with an exploration of the dynamics of agency-client relationships. It contributes to knowledge across three major themes: strategy development, evaluation, and agency-client relationships and is one of the first studies to consider the role of the agency-client relationship in social media marketing. In particular, the study addresses a gap in current knowledge by revealing the significant influence of agency-client relationships on the processes and outcomes of social media marketing strategy development and evaluation. Adopting the ontological and epistemological position that reality is socially constructed, a qualitative study of twenty social media marketers provided a specialist digital agency perspective of social media campaigns. Data was collected through semi-structured interviews with key practitioners, supported by a cognitive-mapping elicitation technique. The findings generate knowledge of the first two major themes: strategy and evaluation through the development of two process models: the ‘Cycle of Social Media Marketing’ for strategy, and the ‘Cycle of Social Media Marketing Evaluation’ for evaluation. Findings for the second theme reject the traditional view of agency-client relationships, and instead offers a fresh perspective on these relationships in social media marketing, identifying three sub-themes: context, conflict and co-creation. The findings reveal key techniques for enhancing client relationships, including client account management strategies; the impact of conflict on trust between both parties; the crucial role of mutual participation in strategy development of strategy and evaluation; and the importance of co-creation, largely facilitated through collaborative learning workshops. This study has implications for scholars as it contributes to our understanding of evaluation in relation to strategy development in a rapidly developing area of modern marketing practice, affirming the importance of social media data analysis to decision-making. This study has implications for practice as it extends knowledge through conceptualisations of processes and offering insights into the influence and dynamics of agency-client interactions in social media marketing. Finally, a key contribution to knowledge is the development of two conceptual frameworks: The Contextualised Conceptual Framework of Social Media Marketing Evaluation in Strategy Development, and The Conceptual Framework of Agency-Client Dynamics in Social Media Marketing which encapsulate the multi-layered nature of this study and the vital importance of evaluation in social media marketing

Using data mining to repurpose German language corpora. An evaluation of data-driven analysis methods for corpus linguistics

A growing number of studies report interesting insights gained from existing data resources. Among those, there are analyses on textual data, giving reason to consider such methods for linguistics as well. However, the field of corpus linguistics usually works with purposefully collected, representative language samples that aim to answer only a limited set of research questions. This thesis aims to shed some light on the potentials of data-driven analysis based on machine learning and predictive modelling for corpus linguistic studies, investigating the possibility to repurpose existing German language corpora for linguistic inquiry by using methodologies developed for data science and computational linguistics. The study focuses on predictive modelling and machine-learning-based data mining and gives a detailed overview and evaluation of currently popular strategies and methods for analysing corpora with computational methods. After the thesis introduces strategies and methods that have already been used on language data, discusses how they can assist corpus linguistic analysis and refers to available toolkits and software as well as to state-of-the-art research and further references, the introduced methodological toolset is applied in two differently shaped corpus studies that utilize readily available corpora for German. The first study explores linguistic correlates of holistic text quality ratings on student essays, while the second deals with age-related language features in computer-mediated communication and interprets age prediction models to answer a set of research questions that are based on previous research in the field. While both studies give linguistic insights that integrate into the current understanding of the investigated phenomena in German language, they systematically test the methodological toolset introduced beforehand, allowing a detailed discussion of added values and remaining challenges of machine-learning-based data mining methods in corpus at the end of the thesis