Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Business

Supervisor

Su, Ning

Abstract

For striving and thriving in the digital age, while firms are rushing to digitally transform their organizing practices as well as offerings, scholars are tasked with revisiting the assumptions of extant theories to unpack the phenomenon of organizing for- and in- the digital age. This thesis focuses on distinct facets of this phenomenon. In particular, I examine the firm strategies and work practices of practitioners in Indian banking and financial organizations (Essays 1, 2), as well as the work practices of academic researchers (Essay 3), as they engage with digital technologies.

The first essay elaborates on and tests the theory of discontinuity in trust in money and the resulting spillover effect, through the degree of association with the source of trust discontinuity, on the financial organizations’ ability to capture value through their digital offerings. Using India’s 2016 demonetization as an exogeneous policy shock, organizational-level digital currency transactions as data sources, and regression discontinuity as empirical strategy, this essay documents an unintended consequence of demonetization—value slipping from government- to private- organizations.

The second essay examines the work practices of data scientists as they strive to perform rationality in practice using a qualitative methodology based on observations, interviews, and archival records at three large Indian banks. The study documents the paradoxical tensions faced by the data scientists as they enact the informing practices of inscribing expertise and prescribing insights for rational decision-making in the organizations, and explicates some mechanisms through which the data scientists alleviate those tensions. This study contributes to the emerging literature on data science as a new profession (Part A), as well as offers recommendations for practitioners (Part B).

The third essay examines the work practices of academic researchers using AI-enabled analytical tools. Based on a systematic methodologic review of articles published in IS and management journals, this study documents the prevalent practices of applying topic modeling––namely, the lack of explicit description, contentious justifications, and polarized, partial, or no validation––that potentially threaten the reliability and validity of academic research. The study proposes a framework to help scholars in mindfully employing algorithmic intelligence in research.

Summary for Lay Audience

Organizations across industries are increasingly adopting digital technologies in their internal processes as well as in their market offerings. This phenomenon of digitization renders our understanding about organizations’ practices, processes, and strategies inadequate. In this thesis, I examine the effects of digitization on organizations with focus on the firm strategies and practices in the Indian banking industry (Essays 1, 2), as well as the research practices of academic scholars (Essay 3), as they engage with digital technologies.

In the first essay, I examine the impact of India’s 2016 demonetization policy by the government on the ability of banks and other financial organizations to profit from the use of their digital currencies by customers. Using data on various digital currencies and relying on quantitative techniques, I demonstrate that due to the negative impact on Indians’ trust in cash currency, Indians increased the use of digital currencies and preferred those financial organizations which had weaker or no ties with the government (which was responsible for demonetization) due to negative spillover effects.

In the second essay, I examine how data scientists generate insights from (big) data using advanced analytics tools in trying to help organizations adapt to data-driven decision-making. By observing and interviewing data scientists at their workplaces in large banks, I demonstrate the paradoxical nature of choices they need to make in their everyday tasks while using their expertise to generating insights, and prescribing those insights for rational decision-making. I also show some strategies the data scientists adopt to overcome the paradoxical tensions. In addition to theoretical contributions (Part A), I also offer recommendations for practitioners (Part B).

In the third essay, I examine research practices of academic scholars using AI-enabled analytical tools. Based on a systematic methodologic review of articles published in academic journals, this study highlights some potential challenges to the reliability and validity of research due to the prevalent research practices of applying topic modeling, which often lack transparency, provide inadequate justifications, and conduct insufficient validations of the findings. I propose a framework to help scholars to overcome some of the highlighted challenges in the prevalent practices.

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