Software-Vendor Diversification: A Source of Organizational Rigidity in Adversity?

2023 | Journal of Management Information Systems | Citations: 0

Authors: Gong, Jing; Liang, Yi; Ramasubbu, Narayan

Abstract: Firms often assemble digital infrastructures using continuously evolving softwar ... Expand

Abstract: Firms often assemble digital infrastructures using continuously evolving software applications sourced from a multitude of vendors. Using the theoretical lens of the threat-rigidity thesis, we raise the possibility that during adverse environmental conditions, software-vendor diversification can be a source of organizational rigidity that may dampen firm performance. Empirical analysis using data on 918 large public U.S. firms operating during two severe environmental shocks, the global financial crisis and the burst of the dot-com bubble, lends strong support to our thesis. Results indicate that a variety of firm performance indicators (e.g., stock return and operating income measures) are negatively associated with software-vendor diversification during crisis periods. Mediation analysis highlights the role of IT-related material weakness in firms' internal controls in transmitting threat-rigidity effects that decrease performance. These results underscore the importance of software portfolio optimization for countering the dysfunctional effects of software-vendor diversification during adverse environmental shocks. Collapse

Topics: organizational productivity accounting computer hardware information technology infrastructure database system

Methods: mediation analysis structural equation modeling cross sectional research propensity score method longitudinal research

Exploration of Health Technology Nonuse: The Case of Online Medical Records

2023 | International Conference on Information Systems | Citations: 0

Authors: Mukhopadhyay, Surma; Basak, Ramsankar; Bentley, John; Mukhopadhyay, Saurav

Abstract: Online Medical Records (OMR) platforms remain a key enabler to health managemen ... Expand

Abstract: Online Medical Records (OMR) platforms remain a key enabler to health management. Yet, how beliefs toward OMR and its subsequent nonuse are related is not understood. Applying the status quo bias (SQB) theory and the privacy paradox paradigm the study examines OMR nonusers and contributes to the health technology use literature. Using the Health Information National Trends Survey (HINTS) iteration 5, Cycle 1 and 3 data, mediation analysis reveals that inertia expressed as preference for speaking directly with healthcare providers predicts perceived need for OMR and partially mediates the relationship between perceived privacy concerns and need; having a chronic disease partially moderates such relationships. Thus, not all nonusers are created equal. Attaining benefits that come with capabilities and functionalities of OMR necessitates meaningful use of OMR by individuals. Healthcare providers or policymakers should intervene to dispel inertia or patient concerns to expand OMR use to facilitate healthcare decision making. Collapse

Topics: privacy information privacy concern health information system information system use healthcare data

Methods: survey structural equation modeling mediation analysis experimental group cross sectional research

Editor's Comments: Causality Meets Diversity in Information Systems Research

2022 | Management Information Systems Quarterly | Citations: 15

Authors: Mithas, Sunil; Xue, Ling; Huang, Ni; Burton-Jones, Andrew

Abstract: As outlined in our March 2022 editorial, we are using this year's editorials to ... Expand

Abstract: As outlined in our March 2022 editorial, we are using this year's editorials to help authors and reviewers understand how editors at MISQ think about key issues in manuscripts they handle. We have identified several issues that we believe would benefit from guidance. 1 This one is dedicated to the topic of causality in quantitative IS research. There are many views on this topic, and we hope that the editorial spurs further dialogue.This editorial is a call to appreciate diverse views of causality. Our remarks are necessarily brief. We simply cover three views for thinking about causality that we consider particularly relevant to IS research. We make no claim that they are the only views, and recognize that there are continuing debates about their relative merits. There are also diverse perspectives within each view. We sidestep these complexities to focus on our main theme, but we provide references that cover these details. Overall, we hope the editorial will help you to appreciate the diversity of causal thinking relevant to our field and the possibilities that such diversity offers for continued growth in understanding. History and Background on Causality"I would rather discover one causal relation than be King of Persia." -Attributed to Collapse

Topics: enterprise resource planning organizational productivity

Methods: Rubin causal model machine learning experiment

Theories: set theory

The Best of Two Worlds – Using Recent Advances from Uplift Modeling and Heterogeneous Treatment Effects to Optimize Targeting Policies

2022 | International Conference on Information Systems | Citations: 0

Authors: Rößler, Jannik; Guse, Richard; Schoder, Detlef

Abstract: The design of targeting policies is fundamental to address a variety of practic ... Expand

Abstract: The design of targeting policies is fundamental to address a variety of practical problems across a broad spectrum of domains from e-commerce to politics and medicine. Recently, researchers and practitioners have begun to predict individual treatment effects to optimize targeting policies. Although different research streams, that is, uplift modeling and heterogeneous treatment effect propose numerous methods to predict individual treatment effects, current approaches suffer from various practical challenges, such as weak model performance and a lack of reliability. In this study, we propose a new, treebased, algorithm that combines recent advances from both research streams and demonstrate how its use can improve predicting the individual treatment effect. We benchmark our method empirically against state-of-the-art strategies and show that the proposed algorithm achieves excellent results. We demonstrate that our approach performs particularly well when targeting few customers, which is of paramount interest when designing targeting policies in a marketing context. Collapse

Topics: marketing management geographic information system mobile application privacy consumer behavior

Methods: random forest classification computational algorithm experimental group decision tree classification ensemble learning

Sharing is Caring: Using Open Data To Improve Targeting Policies

2022 | European Conference On Information Systems | Citations: 0

Authors: Rößler, Jannik; Tilly, Roman; Faska, Matthias; Schoder, Detlef

Abstract: When it comes to predictive power, companies in a variety of sectors depend on ... Expand

Abstract: When it comes to predictive power, companies in a variety of sectors depend on having sufficient data to develop and deploy business analytics applications, for example, to acquire new customers. While there is a vast literature on enriching internal data sets with external data sources, it is still largely unclear whether and how open data can be used to enrich internal data sets to improve business analytics. We choose a particular business analytics problem -designing targeting policies to acquire new customers -to investigate how an internal data set of a German grocery supplier can be enriched with open data to improve targeting policies. Using the enriched data set, we can improve the response rate of several well-established targeting policies by more than 30% in back-testing. Based on these results, we encourage firms and researchers to use, leverage, and share open data to enhance business analytics. Collapse

Topics: open data geographic information system marketing management analytical information system consumer behavior

Methods: company material survey experimental group computational algorithm statistical hypothesis test

A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects

2021 | Management Information Systems Quarterly | Citations: 8

Authors: McFowland, Edward; Gangarapu, Sandeep; Bapna, Ravi; Sun, Tianshu

Abstract: We define a prescriptive analytics framework that addresses the needs of a const ... Expand

Abstract: We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximizing context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and on the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution of this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices—i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits—in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility for the case where significant within- and between-treatment arm heterogeneity exists. Collapse

Topics: marketing management decision making economic surplus IS economics analytical information system

Methods: experiment computational algorithm machine learning causality analysis experimental group

The Role of Randomized Control Trials in Online Demand Generation: Exploratory Evidence from Facebook

2021 | International Conference on Information Systems | Citations: 0

Authors: Runge, Julian; Nair, Harikesh

Abstract: Observing the organically evolving Facebook advertising marketplace, this paper ... Expand

Abstract: Observing the organically evolving Facebook advertising marketplace, this paper sets out to study how the adoption of randomized control trials (RCTs) for policy evaluation impacts firms' behavior and performance on the platform. Findings indicate strong positive effects from the adoption of RCTs: After or in coincidence with RCT adoption, firms increase their investment in demand generation, at stable or higher levels of performance, as measured by customer purchases achieved per advertising dollar spent. A human expert provided to firms by the platform is also able to induce increases in investment but smaller ones, and, importantly, performance gains appear to exclusively materialize after the adoption of RCTs for policy evaluation, and not through human expertise alone. By means of the unbiased causal estimates obtained through RCTs, firms may be able to uniquely reduce their uncertainty about the environment and identify effective policies to exploit it with certainty. Collapse

Topics: advertising management Facebook electronic market electronic commerce online advertising

Methods: synthetic control method descriptive statistic factor analysis Rubin causal model robustness check

Uplift Forest for Multiple Treatments and Continuous Outcomes

2020 | International Conference on Information Systems | Citations: 0

Authors: Gubela, Robin M; Lessmann, Stefan

Abstract: Artificial intelligence (AI) models support digital commerce through enabling th ... Expand

Abstract: Artificial intelligence (AI) models support digital commerce through enabling the personalization of communication and services. The paper focuses on predictive models for targeting marketing actions. Extracting information on customers’ preferences from big data, such models forecast the reaction of customers to marketing stimuli. Prior research uses models for treatment effect estimation and focuses on a single treatment. In practice, digital marketers have many options to approach customers with different messages and over diverse channels. The support of these decisions requires multiple treatment models. The paper proposes an algorithm for multiple treatments and continuous outcomes. The tree-based algorithm relies on a new splitting criterion, which identifies the optimal treatment by optimizing outcome heterogeneities between treatments. It supports different evaluation measures and experimental designs by incorporating propensity scores. Empirical results on two real-world marketing data sets confirm the effectiveness of our approach compared to state-of-the-art benchmarks. Collapse

Topics: electronic mail marketing management decision making missing data decision support

Methods: experimental group computational algorithm random forest classification experiment propensity score method

Organized Complexity of Digital Business Strategy: A Configurational Perspective

2020 | Management Information Systems Quarterly | Citations: 0

Authors: YoungKi Park; Mithas, Sunil

Abstract: How should firms configure organizational capabilities to achieve competitive ad ... Expand

Abstract: How should firms configure organizational capabilities to achieve competitive advantage in complex digital environments? To answer this question, we investigate parsimonious configurations for high firm performance in digital environments characterized by organized complexity. We adopt a configurational perspective accompanied by a fuzzy-set qualitative comparative analysis (fsQCA) to explicate complex nonlinear relationships among key digital and non-digital capabilities in the form of conjunction, equifinality, and asymmetry in producing the outcome. With this approach, we shift attention from individual capabilities to configurations of capabilities to develop a better understanding of the complex role of IT in the digital world. Our analyses, using a rare and unique dataset of 376 observations for organizations in healthcare, education, manufacturing, and service sectors in the United States, reveal three key findings. First, IT-enabled information analytics capability alone is neither necessary nor sufficient in any configuration for high performance; however, it is an important component of the configurations in which it plays multifaceted roles varying from an enabling role in some contexts, to no role or a counterproductive role in other contexts. Second, we document a few parsimonious configurations emergent from complex nonlinear interactions among six organizational capabilities. Interestingly, these configurations often have an isomorphic structure that produces both high financial performance and high customer performance simultaneously. Third, the structures of configurations for high performance differ from those of not-high performance, suggesting an asymmetric view of causality that underpins organizational performance. Together, the findings provide implications for further research on complexity theory in digital business strategy, and for managers to view and redesign digital business strategy as configurations of IT and organizational capabilities. Collapse

Topics: financial performance analytical information system digital business strategy strategic management organizational productivity

Methods: computational algorithm theory development robustness check sensitivity analysis regression analysis method

Theories: complexity theory set theory resource based view of the firm

How Information Management Capability Influences Firm Performance

2011 | Management Information Systems Quarterly | Citations: 406

Authors: Mithas, Sunil; Ramasubbu, Narayan; Sambamurthy, V.

Abstract: How do information technology capabilities contribute to firm performance? This ... Expand

Abstract: How do information technology capabilities contribute to firm performance? This study develops a conceptual model linking IT-enabled information management capability with three important organizational capabilities (customer management capability, process management capability, and performance management capability). We argue that these three capabilities mediate the relationship between information management capability and firm performance. We use a rare archival data set from a conglomerate business group that had adopted a model of performance excellence for organizational transformation based on the Baldrige criteria. This data set contains actual scores from high quality assessments of firms and intraorganizational units of the conglomerate, and hence provides unobtrusive measures of the key constructs to validate our conceptual model. We find that information management capability plays an important role in developing other firm capabilities for customer management, process management, and performance management. In turn, these capabilities favorably influence customer, financial, human resources, and organizational effectiveness measures of firm performance. Among key managerial implications, senior leaders must focus on creating necessary conditions for developing IT infrastructure and information management capability because they play a foundational role in building other capabilities for improved firm performance. The Baldrige model also needs some changes to more explicitly acknowledge the role and importance of information management capability so that senior leaders know where to begin in their journey toward business excellence. Collapse

Topics: information management organizational productivity business process management human resource management organizational value

Methods: SEM tool archival research survey structural equation modeling robustness check