Consumer Preferences for Privacy Management Systems

2023 | International Conference on Information Systems | Citations: 0

Authors: Hanneke, Björn; Baum, Lorenz; Schlereth, Christian; Hinz, Oliver

Abstract: This work presents insights into consumer preferences regarding Privacy Managem ... Expand

Abstract: This work presents insights into consumer preferences regarding Privacy ManagementSystems in the context of the General Data Protection Regulation (GDPR). The authors perform a Choice-Based Conjoint experiment with consumers (n = 589) to elicit preferences over four attributes and compute usage likelihoods for all product configurations. Results show that data sharing for marketing purposes and discounts are the most important attributes for consumers. Furthermore, consumers prefer digital access to privacy-related information, detailed rights management for data sharing and no data sharing for marketing purposes. Moreover, a cluster analysis reveals differing importance weights across clusters. The study concludes that incorporating consumer preferences into the design and development process of Privacy Management Systems could increase their use and effectiveness, ultimately strengthening consumers' privacy rights and companies' legal compliance. The authors suggest researching legal, business, and consumer requirements more holistically to converge these perspectives to improve Privacy Management Systems adoptions. Collapse

Topics: privacy legal context General Data Protection Regulation marketing management digital dashboard

Methods: experiment cluster analysis survey literature study computational algorithm

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Barriers Driving Nonuse of Online Medical Records: Latent Class Analysis of Chronic Patients

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Mukhopadhyay, Surma; Basak, Ramsankar; Reithel, Brian

Abstract: Online Medical Records (OMR) platforms can provide benefits to chronic disease ... Expand

Abstract: Online Medical Records (OMR) platforms can provide benefits to chronic disease patients. Yet, OMR use among them is suboptimal. The study identifies clusters among nonusers of OMR among chronic patients. The Health Information National Trends Survey (HINTS) iteration 5, Cycle 3 data were used to analyze 1071 respondents. Latent Class Analysis was run on the six reasons for nonuse (no record, speaking directly, privacy or security of the website, no Internet, login issues, and no need to access) and resulted in 3 clusters. About 19% subjects expressed multiple reasons and 69% just one strong reason. Demographic and clinical attributes were partially associated. For electronic wearable/tracking device use or electronic communication, differences among clusters were noted; persistent resisters showed lower propensities to use. Interventions to improve patient use of Internet-based health technologies should be customized and help produce patient-generated data facilitating healthcare decision-making. Collapse

Topics: health information system healthcare data electronic health record smart wearable device internet technology

Methods: latent class analysis survey regression analysis method cluster analysis

Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Mingwen; Zheng, Zhiqiang (Eric); Mookerjee, Vijay; Chen, Hongyu

Abstract: We study how firms respond to online customer reviews in a competitive market w ... Expand

Abstract: We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm's effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip's tours that include each tour's sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour's rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct "what-if" analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy. Collapse

Topics: application programming interface online learning system Twitter innovation management service quality

Methods: longitudinal research time series analysis generalized autoregressive conditional heteroskedasticity model natural language processing autoregressive moving average model

Theories: co-evolution theory

Feeling Positive, Negative, or Both? Examining the Self-Regulatory Benefits of Emotional Ambivalence

2022 | Organization Science | Citations: 0

Authors: Gabriel, Allison S.; Butts, Marcus M.; Chawla, Nitya; da Motta Veiga, Serge P.; Turban, Daniel B.; Green, Jeffrey D.

Abstract: According to self-regulation theories, affect plays a crucial role in driving go ... Expand

Abstract: According to self-regulation theories, affect plays a crucial role in driving goal-directed behaviors throughout employees’ work lives. Yet past work presents inconsistent results regarding the effects of positive and negative affect with theory heavily relying on understanding the separate, unique effects of each affective experience. In the current research, we integrate tenets of emotional ambivalence with self-regulation theories to examine how the conjoint experience of positive and negative affect yields benefits for behavioral regulation. We test these ideas within a self-regulatory context that has frequently studied the benefits of affect and has implications for all employees at one point in their careers: the job search. Adopting a person-centered (i.e., profile-based) perspective across two within-person investigations, we explore how emotional ambivalence relates to job search success (i.e., interview invitations, job offers) via job search self-regulatory processes (i.e., metacognitive strategies, effort). Results illustrate that the subsequent week (i.e., at time t + 1; Study 1) and month (Study 2) after job seekers experience emotional ambivalence (i.e., positive and negative affect experienced jointly at similar levels at time t), they receive more job offers via increased job search effort and interview invitations. Theoretical and practical implications for studying emotional ambivalence in organizational scholarship are discussed. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1553. Collapse

Topics: IT career anonymity electronic mail decision making accounting

Methods: qualitative interview survey theory development latent class analysis descriptive statistic

Theories: social cognitive theory information theory

Reconsidering consumer need for touch: Latent class analysis and purchase experience in a 360-virtual store

2022 | International Conference on Information Systems | Citations: 0

Authors: Hallikainen, Heli; Ruusunen, Nino; Laukkanen, Tommi

Abstract: Consumers differ regarding need for touch, and for some consumers haptic explor ... Expand

Abstract: Consumers differ regarding need for touch, and for some consumers haptic exploration plays a more important role in shopping. While online shopping lacks in haptic exploration, we focus on the possibilities of a 360-virtual store in compensating this deficiency. Using latent class analysis and a sample of 300 responses, we first identify four subgroups based on the need for touch scale, with two groups at the high and low ends of the need for touch continuum. Additionally, we identify a group that has a neutral need for touch and a group that is negative regarding autotelic need for touch, and positive regarding instrumental need for touch. To illustrate between-group differences, we examine how consumers with different levels of need for touch differ in the relationship between hedonic / utilitarian shopping experience and purchase intention stimulated by a 360-virtual store visit. Collapse

Topics: usability marketing management human computer interaction electronic commerce virtual reality

Methods: latent class analysis survey experiment SEM tool descriptive statistic

The Screening Role of Design Parameters for Service Procurement Auctions in Online Service Outsourcing Platforms

2022 | Information Systems Research | Citations: 2

Authors: Liang, Chen; Hong, Yili; Chen, Pei-Yu; Shao, Benjamin B. M.

Abstract: This paper provides a novel theoretical angle and robust empirical evidence demo ... Expand

Abstract: This paper provides a novel theoretical angle and robust empirical evidence demonstrating that the auction duration and item description length are two essential auction design parameters that can function as a screening mechanism for bidder quality on online service outsourcing platforms. These outsourcing platforms use buyer-determined reverse auctions to find providers of services (primarily IT services). Using data from a major online outsourcing platform that connects buyers with bidders, we examine the effects of the auction duration and the item description length on both bidder entry (i.e., the number of bids and bidder quality) and contract outcomes (i.e., whether a project is contracted and the buyer’s expected utility from the winning bid) based upon not only project-level, but also bidder-level analyses. Our results show that auctions with longer durations and item descriptions attract more bids (i.e., higher quantity of bidders), and they also attract disproportionately more bidders with lower completion rates (i.e., lower quality of bidders), creating a double whammy of higher evaluation costs and adverse selection for buyers. This, in turn, leads to contracting inefficiency in terms of less successful contracting as well as lower buyer utility. Our research shows strong support for the screening role of the auction duration and the item description length for buyers on online outsourcing platforms for service procurement: by shortening auction durations and item descriptions, buyers can expect higher quality bidders, increase contracting probability, and enhance utility. History: Ravi Bapna, Martin Bichler, Bob Day, and Wolfgang Ketter, Senior Editors; Benjamin Lubin, Associate Editor. This paper has been accepted for the Information Systems Research Special Section on Market Design and Analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1168. Collapse

Topics: outsourcing procurement management website application programming interface database system

Methods: robustness check descriptive statistic sentiment analysis natural language processing computational algorithm

Theories: auction theory lemon market theory

How Is Mobile User Behavior Different? A Hidden Markov Model of Cross-Mobile Application Usage Dynamics

2022 | Information Systems Research | Citations: 0

Authors: Wu, Shaohui; Tan, Yong; Chen, Yubo; Liang, Yitian (Sky)

Abstract: Mobile application use has become an essential activity in many people’s daily l ... Expand

Abstract: Mobile application use has become an essential activity in many people’s daily lives in the mobile Internet era. Driven by its ubiquity and strong context dependence, Internet companies are in a race of cross-industry expansion to build a seamless ecosystem incorporating various contexts. Amid such trends, a better understanding of cross-app uses and the impact of contexts becomes critical and imperative. Yet, research on cross-app uses in information systems and marketing is scarce. In this paper, we aim to fill this gap. We develop a hidden Markov model to study cross-app uses (choice and duration), capturing their interdependence and the impacts of contextual factors. We calibrate it using a consumer panel that contains real-time app use information. Our key findings are as follows. (1) In addition to the utilitarian and hedonic states behind consumer decisions identified in prior literature, we uncover a novel social state behind mobile user behavior. App use behavior exhibits large differences across three states. (2) Within-state app interdependence is strongest in the hedonic state, followed by the social and utilitarian states. (3) Social state is the most transient (i.e., mostly likely to switch away), followed by the hedonic and utilitarian states. (4) Contextual factors—in particular, location and time of day—influence the state dynamic. Compared with the intrinsic state transition, being at home or on the way (versus office) and in the morning or evening (versus night) leads to higher volatility. We discuss the managerial implications for a variety of mobile digital strategies. Collapse

Topics: mobile application user behavior wireless LAN mobile system internet technology

Methods: theory development hidden Markov model latent class analysis computational algorithm descriptive statistic

Trajectories of Repeated Readmissions of Chronic Disease Patients: Risk Stratification, Profiling, and Prediction

2020 | Management Information Systems Quarterly | Citations: 0

Authors: Ben-Assuli, Ofir; Padman, Rema

Abstract: The problem of recurrent, unplanned readmissions, where some patients return sho ... Expand

Abstract: The problem of recurrent, unplanned readmissions, where some patients return shortly after discharge from the hospital and are readmitted for the same or a related condition, has become a challenge worldwide due to care quality, health outcomes, and financial concerns. Predicting frequent, preventable readmissions and understanding the contributing factors is a critical problem that is being widely studied. However, few studies have examined longitudinal risk stratification, profiling, and prediction of multi-morbid, heterogeneous patient populations. We examine how readmission risk may progress over multiple emergency department visits of chronic disease patients, their early stratification into distinct trajectories with related frequencies, and the relationship of these trajectories to patient characteristics. We further extend this analysis to investigate the impact of time-stable and time-varying covariates in predicting future readmission conditional on latent class membership. Results indicate that longitudinal risk stratification can enable early identification of specific patient groups following distinct trajectories based on their presentation for emergency care. Prediction models that incorporate latent classes perform well and demonstrate the promise of trajectory modeling methods combined with advanced prediction models for longitudinal risk assessment in addressing readmission challenges. The methodology and insights from this study are generalizable to other important Information Systems problems. Collapse

Topics: logistics management analytical information system healthcare data IS problem missing data

Methods: machine learning longitudinal research cluster analysis robustness check latent class analysis

Engagement Patterns of Participants in an Online Professional Development Programme

2020 | HICSS | Citations: 0

Authors: Deshmukh, Ketan S; Chand, Dr Vijaya Sherry; Shukla, Dr Kathan D; Laha, Dr Arnab K

Abstract: Unhindered communication capabilities, in the form of internet, led us to believ ... Expand

Abstract: Unhindered communication capabilities, in the form of internet, led us to believe that the difficult goal of “Education for All” is within our grasp. Recent studies have shown mixed results for learning over the internet, indicating that we are still far away from our desired goal. Online environments provide freedom to a large number of learners to learn at their own pace. Understanding the various ways in which participants engage with online content could help explain the mixed outcomes. This paper presents the results of an exploratory study on engagement patterns of 4567 elementary school teachers, in an online professional development programme. Using mixture modelling techniques, we identified five latent profiles of online engagement and seven latent classes based on offplatform activities. We present our findings followed by discussion and implications for online courses. Collapse

Topics: online learning system internet technology information system use

Methods: survey case study latent class analysis likelihood-ratio test latent profile analysis