Privacy calculus and its utility for personalization services in e-commerce: An analysis of consumer decision-making

Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications

When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market research efforts. This study uses machine learning algorithms and mobile usage data from 235 demographically diverse users to examine the accuracy of predicting their sociodemographic attributes (age, gender, income, and education) from mobile usage metadata, filling the gap in the current literature by quantifying the predictive power of each attribute and discussing the practical applications and privacy implications. According to the results, gender can be most accurately predicted (balanced accuracy = 0.862) from mobile usage footprints, whereas predicting users' education level is more challenging (balanced accuracy = 0.719). Moreover, the classification models were able to classify users based on whether their age or income was above or below a certain threshold with acceptable accuracy. The study also presents the practical applications of inferring demographic attributes from mobile usage data and discusses the implications of the findings, such as privacy and discrimination risks, from the perspectives of different stakeholders.

Benchmarking sustainable E-commerce enterprises based on evolving customer expectations amidst COVID-19 pandemic

The 2019 coronavirus disease (COVID-19) pandemic has seriously impacted the performance of all types of businesses. It has given a tremendous structural boost to e-commerce enterprises by forcing customers to online shopping over visiting physical stores. Moreover, customer expectations of the digital and operational capabilities of e-commerce firms are also increasing globally. Thus, it has become crucial for an e-commerce enterprise to reassess and realign its business practices to meet evolving customer needs and remain sustainable. This paper presents a comprehensive performance evaluation framework for e-commerce enterprises based on evolving customer expectations due to the COVID-19 pandemic. The framework comprises seven primary criteria, which are further divided into 25 sub-criteria, including two sustainability factors, namely, environmental sustainability and carbon emissions. The evaluation approach is then practically demonstrated by analyzing the case of three Indian e-commerce firms. The results are obtained using a multi-criteria decision-making (MCDM) method, namely, Fuzzy VIKOR, to capture the fuzziness of the inherent decision-making problem. Further, numerical analysis is conducted to evaluate and rank various e-commerce enterprises based on customer expectations and satisfaction benchmarks. The findings explain the most important criteria and sub-criteria for e-commerce businesses to ensure customer expectations along with their economic and environmental sustainability.

On Students' Willingness to Use Online Learning: A Privacy Calculus Theory Approach

Online learning platforms frequently collect and store learners' data to personalize content and improve learning analytics, but this also increases the likelihood of privacy breaches which may reduce learners' willingness to use online learning. This study aims to examine how perceptions of benefits, privacy, risk, and trust affect students' willingness to use online learning. We used the Privacy Calculus Theory as a theoretical framework for this study. To test the model, we surveyed 203 undergraduate students who used online learning. The results of the AMOS analysis revealed that students' risk perception has a significant negative effect on their willingness to use online learning, while their benefit perception and trust perception have positive effects. Furthermore, the study found that improved trust can reduce perceived risk and improve willingness to use online learning. Interestingly, privacy perception is not a significant predictor of students' willingness to use online learning, although it is a high concern factor. Discussion and conclusion are discussed at the end.

Examining the Prediction of COVID-19 Contact-Tracing App Adoption Using an Integrated Model and Hybrid Approach Analysis

COVID-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. The Kingdom of Saudi Arabia (KSA) is among the countries that have developed a CTA known as the Tawakkalna application, to manage the spread of COVID-19. Thus, this study aimed to examine and predict the factors affecting the adoption of Tawakkalna CTA. An integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TTF) model was hypothesized. The model is used to understand better behavioral intention toward using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia. The findings revealed that perceived ease of use and usefulness has positively and significantly impacted the behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task-technology fit, and significantly affect the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.

Balancing Privacy Risk and Benefit in Service Selection for Multiprovision Cloud Service Composition

The popularity of cloud computing has fueled the growth in multiprovision cloud service composition (MPCSC), where each cloud service provider (CSP) can fulfill multiple tasks, i.e., offer multiple services, simultaneously. In the MPCSC, users would rather disclose some private data for more benefits (e.g., personalized services). However, the more private data is released, the more serious the privacy risk faced by users. In particular, the multiservice provision characteristic of MPCSC further exacerbates the privacy risk. Therefore, how to balance the privacy risk and benefit in service selection for MPCSC is a challenging research problem. In this paper, firstly we explore the service selection problem of balancing privacy risk and benefit in MPCSC (SSBM), then we propose an improved Kuhn–Munkres (KM) algorithm solution to the SSBM problem. Furthermore, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the proposed approach is both efficient and effective for solving the SSBM problem.

The Agglomeration Mechanism of Network Emerging E-Eommerce Industry Based on Social Science

Network emerging e-commerce refers to the development of wireless broadband technology, smart terminal technology, near-field network, etc. as the driving force. It is the emerging e-commerce represented by the continuous development of modern e-commerce and the integration of commerce. This paper proposes to use Michael Porter’s cluster theory method, income increasing algorithm, and spatial Gini coefficient method to sort out and analyze the research results of industrial agglomeration problems, further study the relationship of e-commerce industry agglomeration mechanism, and build agglomeration simulation model , the construction of the centripetal force model of the industrial agglomeration area, through the analysis of the production factors of the e-commerce industry, and then study the influence of each factor on the development of the e-commerce industry. Finally, this paper selects and uses 16 standard mechanical data sets to investigate and analyze the agglomeration mechanism of the e-commerce industry, which verifies the accuracy and overall applicability of the method.

Do individuals disclose or withhold information following the same logic: a configurational perspective of information disclosure in social media

Purpose

This paper aimed to explain why individuals still tend to disclose their privacy information even when privacy risks are high and whether individuals disclose or withhold information following the same logic.

Design/methodology/approach

This study develops a configurational decision tree model (CDTM) for precisely understanding individuals' decision-making process of privacy disclosure. A survey of location-based social network service (LBSNS) users was conducted to collect data, and fuzzy-set qualitative comparative analysis (fsQCA) was adopted to validate the hypotheses.

Findings

This paper identified two configurations for high and low disclosure, respectively, and found that the benefits and the risks did not function independently but interdependently, and the justice would play a crucial role when both the benefits and the risks were high. Furthermore, the authors found that there were asymmetric mechanisms for high disclosure and low disclosure, and males focused more on perceived usefulness, while females concerned more about perceived enjoyment, privacy risks and perceived justice.

Originality/value

This paper further extends privacy calculus model (PCM) and deepens the understanding of the privacy calculus process from a configurational perspective. In addition, this study also provides guidance for future research on how to adopt the configurational approach with qualitative comparative analysis (QCA) to revise and improve relevant theories for information systems (IS) behavioral research.

Feel free to use my personal data: an experiment on disclosure behavior when shopping online

Purpose

The goal of the article is twofold: to determine the effectiveness of monetary incentives for disclosing personal information and to confirm the existence of a “bite the bullet” effect whereby people more easily accept providing personal data if they become aware of the requirement when the purchasing decision is almost taken.

Design/methodology/approach

An experiment in which participants made a real purchase on the AliExpress marketplace was carried out. They were asked to login either via the Facebook button or by creating a username and password. A different reimbursement of the price paid for their purchase was offered in each case. This information was presented either at the beginning of the purchasing process or just before completing the purchase order.

Findings

The monetary incentive proved to work well. The “bite the bullet” effect could not be assessed because many participants willingly gave their data to the company even if they had decided not to buy anything.

Practical implications

From a managerial perspective, this is good news. This is a calamity from a policy perspective. More experiments carried out in real settings are needed as a first step for reconsidering public action.

Social implications

While people continue to publicly declare that they have privacy concerns, their behavior could not be further removed from such concerns.

Originality/value

Experiment in a completely real setting, in which participants made a purchase using their own credit card.

Mitigating e-services avoidance: the role of government cybersecurity preparedness

Purpose

The purpose of this study is to understand why individuals choose to avoid using e-services due to security concerns and perceived risk when these factors are affected by the perceived degree of government cybersecurity preparedness against cyberattacks.

Design/methodology/approach

The authors adopt the information systems success model to predict the role of government security preparedness efforts in influencing the determinants of e-services avoidance. The conceptual model includes four variables: security concerns, perceived risk of cyberattacks, perceived government cybersecurity preparedness and e-services avoidance. Data from 774 participants were used to analyze our conceptual model.

Findings

First, the findings show that security concerns regarding personal information safety and perceived risk of cyberattacks are barriers to e-services use, with the former having a stronger effect. Second, the findings showed that perceived government cybersecurity preparedness significantly reduces security concerns and perceived risk of cyberattacks. Third, the post hoc group analysis between individuals with a bachelor’s degree or higher versus those without a bachelor’s degree showed that the effect of both security concerns and perceived risk of cyberattacks on e-services avoidance was greater for individuals without a bachelor’s degree. The same relationship between perceived risk of cyberattacks and e-services avoidance was not supported for individuals with a bachelor’s degree or higher.

Originality/value

Extant privacy research fails to adequately examine the role of institutional factors, such as government efforts, and how these mitigate or amplify cybersecurity concerns and risks related to e-services. This research takes the first step toward addressing this limitation by examining the influence of government cybersecurity preparedness efforts on the determinants of e-services avoidance.