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Graduate employability is a key concern for many observers particularly at a time when education is increasingly available for the masses. The purpose of this paper is to examine the impact of graduate perceived employability on job search by integrating theory of planned behavior and to identify how job search self-efficacy, subjective norms, intention and intensity change over time.

Data were collected from a six-wave survey study with a sample of Chinese university graduating students.

Results show that perceived employability has a positive and significant effect on job search self-efficacy, attitude, intention and intensity; and that all the repeated measuring variables (except job search attitude) decreased over time.

The study is useful for educators, employers and prospective students. It prompts discussion of reforms in the curriculum to increase graduate awareness of the complexity of the job search process and existing opportunities. The study could also help to explain how job search behavior changes over time.

The findings carry implications for both higher education research and the measures of improving graduate employability. The study fills the gap in the literature by integrating employability and the theory of planned behavior into one framework in order to analyze the process of Chinese university graduates’ job search behavior.

Self-leadership’s positive interpersonal influence is rarely considered in empirical research despite its significance to organizational social dynamics. Thus, this study aims to investigate self-leadership’s interpersonal effects and identify the underlying mechanisms and boundary conditions.

The theoretical model is validated through a two-point time-lagged survey.

Coworkers’ self-leadership positively impacts employees’ knowledge sharing through admiration and relationship desire. The chain mediation effect is moderated by perceived competitive climate: the higher the perceived competitive climate, the stronger the positive indirect effect will be.

Organizations should prioritize fostering employee self-leadership to facilitate knowledge sharing, especially in highly competitive environments.

By identifying the interpersonal effects of self-leadership, this study provides a fresh perspective to the literature on self-leadership, enriching the consequences of self-leadership.

This research aims to examine the relationship of personal innovativeness, perceived value (consisting of perceived utilitarian and hedonic value) and continuance intention in the context of ride-hailing apps and to investigate consumer behaviour differences between metro and non-metro consumers.

The survey sample included 402 consumers of popular ride-hailing apps in Indonesia to test the research framework. Then, PLS-SEM-based software was utilised to examine the hypothesised relationship.

The findings indicate that the effect of personal innovativeness on continuance intention in using ride-hailing apps will operate through the full mediation role of perceived hedonic and utilitarian value, respectively. The findings also show that there are consumer behaviour differences between metro and non-metro consumers, in which the cognitive belief of consumers in the metro areas regarding the usage of ride-hailing apps is more related to hedonic value.

The variety of respondent demographic profiles in this research is limited in that most of the research respondents are students. In such a case, the study may face the issue of generalisation.

This research generates an extended idea of the information technology continuance model by validating the important role of perceived hedonic and utilitarian value as an integral part of the model and strengthens the insights that Indonesia has consumer behaviour differences regarding technology-based services, particularly ride-hailing apps, between metro and non-metro consumers.

This study aims to investigate the impact of several factors on customer satisfaction and intention of reusing ride-hailing services that is a new type of passenger urban transport service.

This research applied the Partial Least Squares Structural Equation Modeling analysis method to examine the measurement scale and to analyze the primary data collected from 388 passengers in Vietnam.

This study demonstrates that three dimensions of perceived value, namely, functional value, hedonic value and economic value, positively influence customer satisfaction. The other dimension of perceived value, which is social value, has an ambiguous effect on satisfaction. In addition, personal innovativeness promotes all dimensions of perceived value. In particular, this study highlights that customer satisfaction and corporate image positively impact reuse intention, and corporate image moderates the relationship between customer satisfaction and reuse intention.

This study enriches knowledge about customer behavior using services based on the sharing economy business model. In particular, theoretical and practical implications are provided for researchers and enterprises to find suitable strategies for business.

In the era of digitalization and technology, tremendous changes have taken place in the taxi industry worldwide. The traditional taxi service has transformed into the latest innovative technology-based e-hailing service. There are innumerable factors that drive the user adoption of e-hailing apps. This study aims to primarily concentrate on identifying, analyzing and ranking these factors which have an impact on the user intention toward using e-hailing apps.

The e-hailing app users in the state of Punjab and Chandigarh are the target population for the study. A fuzzy analytical hierarchy process technique has been applied to analyze and codify the determinants that influence the user intention of adopting e-hailing apps. The primary factors that have been considered for the study are social influence, perceived usefulness, facilitating conditions, perceived ease of use, self-efficacy, perceived risk, compatibility and trust.

The study revealed that “Perceived Usefulness” is the factor that influences user intention to use e-hailing apps the most, while “Perceived Risk” the least. The sub-criteria codified in the top priority was as follows: “Overall, I find the e-hailing app useful in booking a taxi (C15)”; “I do not need some people to use e-hailing apps (C52); “I believe e-hailing app is compatible with existing technology (C61).” The sub-criterion “E-hailing app service provider keeps its promise (C72)” was demonstrated to have the least impact on the user intention of adopting e-hailing apps.

The study has been confined to only eight factors selected from the extended technological acceptance model framework and some related technology acceptance theories. Some more other factors may have an impact on user adoption of e-hailing apps, which need to be added further. Also, the scope of the study should be enhanced by expanding the geographical area beyond the selected region.

The findings of the study enable the e-hailing service providers and marketers to understand the users’ intention in a better way, to make improvements in e-hailing apps and formulate strategies accordingly.

The previous literature provides the base to the present study for identifying the factors affecting user behavioral intention toward e-hailing apps and information technology. The findings and results of the present research make value addition to the existing knowledge base.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

The online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.

This research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.

This research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.

This research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.

This research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.

It also reveals the data convergence process of the social system and the technological system.

This research offers a new research method for the real-time regulation of the car-hailing platform.

This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.

This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.

Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.

Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.

Despite the perceived role of customer value in post-adoption behaviour in the context of ride-hailing apps such as Uber, there has been limited research on the subject. This paper seeks to enrich the understanding of the relationships between customer perceived value, particularly hedonic value and economic value, customer satisfaction and continued use intentions of ride-hailing apps.

This analysis is based on field data collected from 567 users of ride-hailing apps in Ghana. Data collected from the survey were analysed using the partial least square (PLS) approach to structural equation modelling (SEM).

The paper provides evidence that hedonic value, as well as economic value, positively predicts customer satisfaction and continued use intentions of ride-hailing apps. Further analysis reveals customer satisfaction directly predicts continued use intentions in addition to partially mediating the influence of customer perceived value on continued use intentions of ride-hailing apps. Finally, the findings suggest that hedonic value has a stronger impact on continued use intentions than economic value, while economic value has a greater impact on satisfaction than hedonic value.

The study contributes to post-adoption behaviour research by providing evidence on the relationships among the study constructs in a developing country context. Overall, the findings will stimulate future empirical debates on the subject and guide practitioners in decision-making concerning customers' usage of ride-hailing apps.

Mobile taxi booking apps (MTB) have revolutionalized the transportation industry. As taxis can be hired via smartphones, irrespective of any time or place, the business platform for taxi service has completely changed. Now customers are saved from the hassle of going to the designated taxi stands or waiting along the roadside. But, the long-term sustainability of this service depends on its continued use. Therefore, this study aims to explore factors that hedonically incline people toward continuance of MTB. To achieve the purpose, the unified theory of acceptance and use of technology (UTAUT) was extended with mediation effects of hedonic motivation.

The data were collected from existing users of MTB and analyzed through structural equation modeling and revalidated via artificial neural networks.

The statistical results show that the main factors of UTAUT substantially create hedonic motivation to use the apps and significantly mediate their effects on behavioral intention to continue using MTB. However, mediation between social influence and continuity intent was not statistically supported. The findings represent important contributions to the extended UTAUT.

This study adds value to the theoretical horizon and also presents M-taxi companies with useful and pertinent plans for efficient designing and effective implementation of MTB. Moreover, limitations and suggestions for future researchers are also discussed.

This study extends UTAUT with the mediating role of hedonic motivation to predict continued use of MTB, which further initiates the applicability of UTAUT in a new setting and a new perspective (post adoption). This, in turn, significantly expands theory by using hedonic motivation as an important attribute that could mediate impact of all main antecedents to shape customers loyalty toward system use.