Search results

The purpose of this study is to add to the currently limited research on individual level people analytics (PA) adoption by focusing on a vital resource in the implementation of PA, namely the technology used for PA. We draw on the unified theory of acceptance and use of technology (UTAUT) to examine antecedents of the use of technology for PA.

This study uses latent profile analysis to examine how different antecedents of PA (technology) adoption jointly act among 279 users of a specific PA technology.

This study identifies four user profiles, that we labeled the skeptic diplomats, the optimistic strugglers, the optimists, and the enthusiasts. These profiles relate to differences in user satisfaction and the frequency and versatility of PA technology use. This study demonstrates that performance benefits, social influence, required effort, and facilitating conditions jointly affect the use of PA technology, but that the latter two might be the most influential factors.

This study offers recommendations to practitioners and organizations on which actions by managers and the organization can be taken to support the use of PA technology.

Compared to previous research, we take a different approach by applying latent profile analysis to examine the combined effect of antecedents on user behavior and user satisfaction. In addition to a different analytical approach, we also extend existing research on individual PA adoption by focusing on actual behaviors and behavioral intention.

TikTok, a social media application (app), was originally positioned as a short music video community suitable for young users, and the app is user-generated content (UGC) short video of vertical music. Users can make their own creative videos. Following the rhythm of the music, users can shoot various video content, personal talents, life records, performances, dances, plot interpretations, etc. However, what are the profiles and preferences of TikTok users, whereby the social media app is mainly developed by UGC? What is the impact of TikTok on the development of social media? In addition, what is UGC's social media model for user interactions in social networks? The purpose of this paper is to address and study these proposed issues.

All questionnaire items are designed as nominal and ordinal scales (not Likert scale). The obtained data from questionnaires are put into the relational database (N = 2,011). This empirical study takes Taiwan TikTok users as the research object, implements data mining analytics to generate user profiles through clustering analysis and further uses association rules’ analysis to analyze social media apps in social network interaction and social apps’ development by proposing two patterns and several meaningful rules.

This study finds that social media apps is a valuable practical research topic on online social media development. In addition, besides the TikTok, the authors eagerly await subsequent research to provide more valuable findings of social media apps in both theory and practice.

This study presents the research evidences that social media apps such as TikTok will be able to transcend the current development pattern of social media and make good use of the media and technology innovation of apps in social development and social informatics.

Peer-to-peer (P2P) secondhand resale platforms (SRP) are competitive places where different value systems beyond market values interact. This study aims to investigate the conflicts that may arise in interactions between users on SRP and the extent to which these conflicts are (ir)resolved, by drawing on economies of worth theory.

The study takes a qualitative and interpretative approach to examine 22 active users on P2P resales platforms such as Vinted, including in-depth interviews. Following the Straussian view of grounded theory, the study uses constant comparison (open, axial and selective coding) to analyze data on SRP users’ experiences.

Drawing on the economies of worth theory, the study shows that SRP users rely on four different value systems or “worlds” when using the platforms (market, domestic, green and civic worlds) that come into conflict, at either an interactional (three conflicts identified) or an individual (two conflicts identified) level. The findings reveal that these conflicts are temporarily resolved at the interactional level and in a sustainable way at the individual level.

This study sheds further light on the relationship between consumers on SRP by offering a more nuanced perspective on these exchanges than market-oriented exchanges. It also analyzes the data through the economies of worth theory, which is an appropriate lens to better understand social interactions and conventions. Finally, the study offers recommendations on how managers can improve buyers’ and sellers’ experiences on these platforms and, thus, foster their satisfaction.

This study, anchored in Self-Determination Theory, explores the paradoxical effects of gamification on digital health promotion, focusing on user engagement, physical activity adherence, stress levels and social dynamics.

In a preregistered experimental design, participants interacted with one of three fitness apps: a non-gamified control app, a competition-based gamified app (GameFit) or a self-exploration-based gamified app (ExploreFit). Quantitative data were collected through scales measuring key variables, and qualitative insights were gathered from structured diaries.

Gamification significantly enhanced user engagement and physical activity adherence. However, competition-based gamification also increased stress and negative social dynamics, with gender-specific effects: females experienced higher stress and males encountered more negative social dynamics. These adverse effects were mitigated in the ExploreFit group, suggesting that self-exploration elements in gamification can reduce negative outcomes.

This research offers a comprehensive analysis of the complex interplay between gamification, competition, self-exploration and health outcomes. It provides valuable insights for the design of persuasive technologies, highlighting the need for a balanced approach to gamification that considers both its benefits and potential pitfalls.

Designing effective business analytics (BA) platforms that visualise data, provide deep insights and support data-driven decision-making is a challenging task. Understanding the elements shaping BA platform design is crucial for success. The purpose of this study is to explore the impact of visualisation on usability (UI) and user experience (UX) while emphasising the importance of insights understanding in BA platform design.

This paper presents a case study following a startup’s journey as it undergoes two redesign phases for its BA platform. A combination of quantitative and qualitative methods is used to assess UX/UI and insights understanding of the platform. Indicatively this included semi-structured interviews, observations, think-aloud techniques and surveys to monitor runtime per task, number of errors, users’ emotions and users’ understanding.

Our findings suggest that modifications in aesthetics and information visualisation positively influence overall usability, UX, and understanding of platform insights – a critical aspect for the success of the startup.

Our goal is not to make a methodological contribution, but to illustrate how companies, constrained by time and pressure, navigate platform changes without meticulous design and provide learnings on important elements while designing BA platforms.

This paper concludes with suggested methods for assessing BA platforms and recommends practical practices to follow. These practices include recommendations on important elements for BA platform users, such as navigation and interactivity, user control and personalisation, visual consistency and effective visualisation.

This study contributes to practice as it presents a real-life case and offers valuable insights for practitioners.

The purpose of this study was to examine consumer data acquired by branded prescription drug websites and the ethics of privacy related to the interconnected web of personal information accessed, packaged and resold by tracker technologies.

The research used the DMI Tracker Tool to collect data on the top 17 branded prescription drug websites, with a specific interest in the tracker technologies embedded in those websites. That data was analyzed using Gephi, an open-source data visualization tool, to map the network of trackers embedded in those branded prescription drug websites.

Findings visualize the interconnections between tracker technologies and prescription drug websites that undergird a system of personal data acquisition and programmatic advertising vehicles that serve the interests of prescription drug marketers and Big Tech. Based on the theory of platform ethics, the study demonstrated the presence of a technostructural ecosystem dominated by Big Tech, a system that goes unseen by consumers and serves the interests of advertisers and resellers of consumer data.

The 17 websites used in this study were limited to the top-selling prescription drugs or those with the highest ad expenditures. As such this study is not based on a random sampling of branded prescription drug websites. The popularity of these prescription drugs or the expanse of advertising associated with the drugs makes them appropriate to study the presence of tracking devices that collect data from consumers and serve advertising to them. It is also noted that websites are dynamic spaces, and some trackers within their infrastructures are apt to change over time.

Branded prescription drug information has over the past three decades become part of consumers’ routine search for information regarding what ails them. As drug promotion moved from print to TV and the Web, searching for drug information has become a part of everyday life. The implications of embedded trackers on branded prescription drug websites are the subject of this research.

This study has significant social implications as consumers who are searching for information regarding prescription medications may not want drug companies tracking them in a way that many perceive to be an invasion of privacy. Yet, as the Web is dominated by Big Tech, web developers have little choice but to remain a part of this technostructural ecosystem.

This study sheds light on branded prescription drug websites, exploring the imbalance between the websites under study, Big Tech and consumers who lack awareness of the system that operates backstage.

This paper aims to analyse the determinants of the intention to share a post about an environmental issue posted by a tourism destination on Facebook. The authors use the stimulus-organism-response (SOR) model as a theoretical framework and consider cognitive variables (destination social responsibility, tourist social responsibility and three types of congruence) as antecedents of emotions and of the tourists’ response (intention to share). Specific factors related to the social platform (attachment and active use of social media) are also included.

The model was tested for two destinations with different positioning (green tourism versus sun and beach). For the sampling strategy, the authors conducted an online poll targeting Facebook users who had undertaken at least one trip in the previous year. The sample consisted of 1,001 individuals.

The empirical evidence obtained indicates that consumer–cause congruence is the most important variable for explaining the intention to share the post for both destinations, with the destination–cause congruence being non-significant. The authors also observed that active participation on the social network stimulated the intention to share this specific content.

Unlike prior research, this paper examined consumer motivators for engaging with online corporate social responsibility content for tourism destinations, specifically focusing on destination social responsibility in sustainable tourism. The model also incorporates three types of congruence, revealing variations in their impact on explaining the intention to share sustainability-related posts.

China’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.

We constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.

The results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.

Studies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.

1. Construct a better health service requirements model for mHealth app users.

2. Obtain the prioritization of demand elements in the model.

3. Propose some management suggestions to improve mHealth apps.

Construct a better health service requirements model for mHealth app users.

Obtain the prioritization of demand elements in the model.

Propose some management suggestions to improve mHealth apps.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.