Towards More Convenient Services: A Text Analytics Approach to Understanding Service Inconveniences in Digital Platforms

2023 | HICSS | Citations: 0

Authors: Amat-Lefort, N.; Barnes, S.

Abstract: In today's fast-paced world, where time is our most valuable asset, convenience ... Expand

Abstract: In today's fast-paced world, where time is our most valuable asset, convenience is on the rise. This trend has led to a huge growth in digital on-demand services, which target convenience-oriented consumers. Using big data and text analytics, we examine the impact of service inconveniences on customer satisfaction in the context of on-demand food delivery. Building on the Model of Service Convenience and Attribution Theory, we analyze 235,147 user-generated reviews via a combination of keyword-assisted topic modelling and cumulative link model analysis. We introduce the concept of Remote support inconvenience and identify the key topics related to each inconvenience. We find that all service inconveniences negatively influence satisfaction (especially cancelled orders and remote support incidences), and the effects are exacerbated when moderated by stability or controllability attributions. These insights contribute to our theoretical understanding of service inconvenience and can help platforms identify and improve critical areas of their services. Collapse

Topics: user satisfaction customer satisfaction mobile application user-generated content crowdsourcing

Methods: natural language processing topic model exploratory data analysis ANOVA parametric test

Theories: attribution theory

Optimizing Substance Use Treatment Selection Using Reinforcement Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Baucum, Matt; Khojandi, Anahita; Myers, Carole; Kessler, Larry

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in th ... Expand

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching. Collapse

Topics: electronic health record individual characteristic healthcare data missing data

Methods: hazard function machine learning random forest classification descriptive statistic propensity score method

Segmentation preference and technostress: Integrators’ vs segmenters’ experience of technology-induced demands and related spill-over effects

2023 | Information & Management | Citations: 0

Authors: Becker, Julia; Lanzl, Julia

Abstract: Remote work is becoming the “new normal”, and more people are working in the hom ... Expand

Abstract: Remote work is becoming the “new normal”, and more people are working in the home office due to the COVID19 pandemic. Boundary management and the individual preferences to segment work and private life are a current topic of research as digital technologies have the potential to aggravate segmentation due to their invasive effect. In this context, we add to a current research stream on technostress, investigating technologydriven spill-over in a longitudinal study based on data assessed during the pandemic. The use of communica­ tion technologies leads to work-to-family stress due to the occurrence of techno-stressors interruptions, invasion, and overload. Differences between “segmenters” (people with a strong wish for separation) and “integrators” (who rather integrate life domains) were found. They experience techno-stressors differently in dependence on their technology use. Our paper offers interesting theoretical insights into boundary transcending effects of technology use. Recommendations for employers on how to shape the “new normal” are discussed. Collapse

Topics: information system use technostress pandemic self efficacy IT supported collaboration

Methods: longitudinal research survey structural equation modeling data transformation confirmatory factor analysis

Pricing in Nonconvex Markets: How to Price Electricity in the Presence of Demand Response

2023 | Information Systems Research | Citations: 0

Authors: Bichler, Martin; Knörr, Johannes; Maldonado, Felipe

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices ... Expand

Abstract: A Walrasian competitive equilibrium defines a set of linear and anonymous prices where no coalition of market participants wants to deviate. Walrasian prices do not exist in nonconvex markets in general, with electricity markets as an important real-world example. However, the availability of linear and anonymous prices is important for derivatives markets and as a signal for scarcity. Prior literature on electricity markets assumed price-inelastic demand and introduced numerous heuristics to compute linear and anonymous prices on electricity markets. At these prices, market participants often make a loss. As a result, market operators provide out-of-market side-payments (so-called make-whole payments) to cover these losses. Make-whole payments dilute public price signals and are a significant concern in electricity markets. Moreover, demand-side flexibility becomes increasingly important with growing levels of renewable energy sources. Demand response implies that different flexibility options come at different prices, and the proportion of price-sensitive demand that actively bids on power exchanges will further increase. We show that with price-inelastic demand there are simple pricing schemes that are individually rational (participants do not make a loss), clear the market, support an efficient solution, and do not require make-whole payments. With the advent of demand-side bids, budget balanced prices (no subsidies are necessary) cannot exist anymore, and we propose a pricing rule that minimizes make-whole payments. We describe design desiderata that different pricing schemes satisfy and report results of experiments that evaluate the level of subsidies required for linear and anonymous prices on electricity spot markets with price-sensitive demand.History: Xiaoquan (Michael) Zhang, Senior Editor; Pallab Sanyal, Associate Editor.Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grant BI 1057/1-9].Supplemental Material: The online supplement is available at https://doi.org/10.1287/isre.2022.1139. Collapse

Topics: price management anonymity accounting

Methods: experiment optimization model linear optimization descriptive statistic case study

Token Incentives in a Volatile Crypto Market: The Effects of Token Price Volatility on User Contribution

2023 | Journal of Management Information Systems | Citations: 0

Authors: Chen, Kun; Fan, Yifan; Liao, Shaoyi Stephen

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in bl ... Expand

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in blockchain systems to encourage user participation. Distinct from monetary incentives, token incentives are uncertain in reward magnitude due to the large swings in token prices on crypto markets. By focusing on token price volatility, this study investigates how the reward uncertainty affects user contribution in a tokenized digital platform. Our empirical setting is Steemit, a platform where bloggers write posts and share token rewards based on their posts' popularity. We find that while high token price volatility induces a large volume of blog posts, it diminishes post quality. The dichotomous effects are explained via two mechanisms: users' direct reactions to reward uncertainties and their indirect reactions, mediated by altered token preference amid volatility shocks. Deeply exploring this dynamic process, our results reveal that token price volatility facilitates a platform's network short-term effect but impairs long-term user creativity. Our empirical findings thus extend the literature on blockchain economics and cryptocurrencies and have practical implications for the design of incentive mechanisms on tokenized digital platforms. Collapse

Topics: blog blockchain digital platform cryptocurrency user behavior

Methods: longitudinal research instrumental variables estimation data transformation principal component analysis vector autoregression

Theories: motivation crowding theory

How much is too much? The nonlinear link between emotional arousal and review helpfulness

2023 | Decision Support Systems | Citations: 0

Authors: Chou, Yen-Chun

Abstract: Online reviews affect consumer purchase decisions and become prevalent in differ ... Expand

Abstract: Online reviews affect consumer purchase decisions and become prevalent in different sectors. Platforms elicit useful reviews by allowing consumers to vote for helpfulness. The link between review features and review helpfulness hence has been a topic of broad interest in information systems (IS) and Internet commerce. Psy­ chological science studies show that expressed emotions in information – valence and arousal significantly in­ fluence receivers’ engagement and valuation. Unlike valence (negative or positive) that is widely-analyzed, emotional arousal is a less-studied attribute of online reviews. This study presents a careful account of the nonlinear link between emotional arousal and review helpfulness. Following a rigorous procedure of theorizing and testing nonlinear relationships (Haans et al. 2016), we articulate latent mechanisms leading to an inverted Ushaped association and how valence would moderate the nonlinear effect of review arousal. We analyze reviews on experience goods from Yelp.com and find firm support for our hypotheses. The findings are triangulated using reviews on search goods from Amazon.com. While the nonlinear effect of review arousal and interactions with review valence are robust to both types of goods, an alternative moderation path of review valence on the inverted U-shaped relationship is observed. Our empirical study contributes to the literature by shedding lights on the dynamics among arousal, valence, review helpfulness, and product types, which illustrates a prototypical example to IS/e-commerce scholars on rigorously conceptualizing and estimating U-shaped links as well as their moderations. Collapse

Topics: tablet experience good online review user-generated content electronic commerce

Methods: theory development product review triangulation sentiment analysis reference modelling

Do you see what I see? A social capital perspective on microtask gig worker opportunity recognition within electronic networks of practice

2023 | International Journal of Information Management | Citations: 0

Authors: Di Gangi, Paul M.; Howard, Jack L.; Goh, Samuel H.; Thatcher, Jason Bennett

Abstract: Microtask gig workers (MGWs) rely on digital platforms to arrange work agreement ... Expand

Abstract: Microtask gig workers (MGWs) rely on digital platforms to arrange work agreements with requesters to complete well-defined microtasks. Many MGWs use an electronic network of practice (ENP) to facilitate information sharing about desirable and undesirable microtasks. This study uses social capital theory to theorize how social capital’s dimensions – structural, cognitive, and relational – shape the development of uncertainty-reducing and individualized-skill benefits. Based on survey data from 436 Amazon Mechanical Turk (MTurk) workers, the findings demonstrate that unique social capital dimensions affect specific ENP benefits. Understanding the communication style of an ENP (i.e., cognitive social capital) positively influences the uncertainty-reducing benefits of microtask information quality (MIQ) related to MTurk work. Combined with expectations of reci­ procity and trust in ENP members (i.e., relational social capital), MIQ shapes microtask opportunity recognition (MOR), whereby individual MGWs identify opportunities to complete financially beneficial microtasks. The present study demonstrates that contextual factors, based on the coopetive nature of microtask ENPs, affect the interrelated structure of social capital theory and its underlying dimensions. Lastly, post hoc findings demon­ strate the influence of MOR on MGWs’ financial performance, challenging previously held assumptions about the role of MIQ within the microtask literature. Collapse

Topics: social capital financial performance platform worker data quality digital platform

Methods: survey post-hoc analysis structural equation modeling mediation analysis theory development

Theories: social capital theory lemon market theory

Recent Developments in PLS

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Evermann, Jöerg; Rönkkö, Mikko

Abstract: Partial least squares (PLS) path modeling is a widely used method in the inform ... Expand

Abstract: Partial least squares (PLS) path modeling is a widely used method in the information systems (IS) discipline for estimating linear structural equation models. At the same time, researchers have debated its relative merits compared to simple summed scores or to covariance-based estimation of structural equation models. In this paper, we comment on recent developments in PLS to ensure that IS researchers have up-to-date methodological knowledge and best practices if they decide to use PLS. In particular, we briefly review its mechanisms, its well-known properties, and its usage history in IS research. We briefly revisit a high-impact critique and debate a few years ago to identify the critical arguments around current PLS practices and use. That critique proved to be the driver for many advances in the PLS method and its applications which we discuss extensively and use to make 14 recommendations for how and when to use PLS or alternatives.Simply put, most IS researchers do not study research questions where predictive modeling would be applicable but focus on testing theory that requires explanatory models. If predictive modeling pertains to their study, researchers should clearly explain the predictions' purpose and usefulness and use operative prediction (i.e., mixed formative-reflective) to assess their models' out-of-sample prediction at the individual level as Shmueli et al. (2016) and Evermann and Tate (2016) recommend. R8: If researchers use PLS for prediction, they should adequately justify and reflect this motivation and assess out-of-sample prediction at the individual level. Recent Developments in PLS Volume 52 10.17705/1CAIS.05229 Paper 29 R13: Researchers should compare PLS composites to unweighted composites to demonstrate any possible advantage that the PLS composites might have. Recent Developments in PLS Volume 52 Collapse

Methods: partial least squares regression consistent partial least squares path modeling simulation structural equation modeling partial least squares path modeling

Theories: information systems theory

FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

2023 | European Conference On Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Kurka, Marco; Schoder, Detlef

Abstract: This paper explores the concept of Finfluencers: financial social network actor ... Expand

Abstract: This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe-art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud. Collapse

Topics: Twitter social network social influence cryptocurrency social media

Methods: longitudinal research time series analysis Granger causality descriptive statistic sentiment analysis

Triadic Advocacy Work

2023 | Organization Science | Citations: 0

Authors: Jackson, Summer Rachel; Kellogg, Katherine Cissel

Abstract: Scholars of street-level bureaucracy and institutional research focus primarily ... Expand

Abstract: Scholars of street-level bureaucracy and institutional research focus primarily on the relationships between advocates and their larger bureaucratic and social systems, assuming that advocates have little need to satisfy their beneficiaries. We find otherwise in our two-year ethnographic study of public defenders advocating for disadvantaged clients in interactions with district attorneys. In our analysis of 82 advocacy opportunities, we demonstrate that, when existing bureaucratic and social systems put beneficiaries at a disadvantage, advocates may be concerned about managing fraught relationships with their beneficiaries in addition to navigating barriers within the bureaucratic and social systems. We further show a tension between the two; ironically, engaging in advocacy work on behalf of beneficiaries can lead to beneficiary mistrust. As a result, advocates engage in triadic advocacy work—managing impressions with their beneficiaries while also influencing powerful actors within the system on behalf of these same beneficiaries. Understanding the process by which advocates navigate this tension is critical to understanding beneficiary outcomes. By reconceptualizing advocacy work as a triadic process among advocate, bureaucratic system, and beneficiary rather than as a dyadic process between advocate and bureaucratic system, this paper develops new theory about how advocates can attempt to garner benefits that advance the rights and opportunities of the disadvantaged. Collapse

Topics: IT career entrepreneurship

Methods: field study qualitative interview theory development ethnography business process modeling