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Client-led service innovation has gained currency in academic libraries owing to ever-shifting client preferences. Library clients are interested in determining which services libraries should offer to meet their information needs. Several studies have investigated service innovation or improvement in libraries with an emphasis on client involvement. However, it is not clear whether the reward culture influences client-focused service innovation in the context of Uganda.

This multiple-case study triangulated data from a survey of 80 staff members, three semi-structured interviews conducted with three library heads, and data from institutional documents. Data were analysed quantitatively and qualitatively.

The results indicated that the financial reward culture did not influence client-led service innovation in any way; instead, non-financial rewards had an impact. Therefore, library managers need to appreciate that clients are no longer interested in financial rewards, but rather in non-financial rewards that have enduring value.

The study underscores the importance of client-led services in an information environment that has been invaded by industry 4.0 and calls upon librarians to use the opportunity to provide innovative services.

Social live streaming services (SLSS) have infused gamification into interface design and feature applications. Firms adopt gamification mechanisms to win customer loyalty in the live streaming and SLSS markets. Based on the mechanics-dynamics-aesthetics (MDA) framework and uses and gratifications 2.0 theory (UGT 2.0), this study aims to investigate the effects of game mechanics (mechanics) on enjoyment and user retention (aesthetics) through rewards and social interaction (dynamics) in the context of SLSS.

This study used an online survey via Google Forms, SurveyCake and social media platforms like Facebook, Instagram and Line to collect data from 232 SLSS users in Taiwan. Partial least squares structural equation modeling (PLS-SEM) was adopted to analyze the data.

The results validated the relationships between game mechanics and dynamic elements (rewards and social interaction) that triggered aesthetic elements (enjoyment feelings) among users. In addition, users experienced a sense of enjoyment that led to usage retention when using the gamified SLSS. Further, this study found enjoyment crucial for users to stay interactive with gamified services.

Driven by UGT 2.0, this study closed the gaps by integrating the MDA framework into the SLSS context and better understanding how game mechanics are connected to rewards and social interaction, leading to enjoyment and user retention when using SLSS. This study provides fresh insights into gamification-oriented SLSS practices. It offers significant theoretical and managerial implications and provides guidelines for SLSS platform operators on fostering user retention.

This study investigates theoretical relationships among personality characteristics, preferences for different types of rewards and the propensity to choose a job in auditing by management-related higher education students. Specifically, the authors consider motivation, locus of control (internal and external) and self-efficacy (SE) as personality characteristics and financial, extrinsic, support and intrinsic as types of rewards.

Data were collected through a questionnaire targeted at management-related higher education students in Portugal. Partial least squares structural equation modelling was used to analyse the data.

The full sample results show that different types of motivation, locus of control and SE are related to different reward preferences. The authors also find a positive association between a preference for extrinsic rewards and the propensity to choose a job in auditing. Moreover, when the authors consider the role of working experience in the model, the authors find that the reward preferences that drive the choice of an auditing job differ according to that experience.

This study enriches the literature by assessing preferences for different types of rewards, considering multiple personality characteristics and a comprehensive set of rewards. Furthermore, the authors identify the reward preferences that drive the choice of an auditing career. This knowledge empowers auditing firms to devise recruitment strategies that resonate with candidates’ preferences, which boosts the capacity of these companies to attract new auditors.

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

This paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.

The proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.

The study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.

The findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.

The proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.

This research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.

This paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.

Social interaction in comment sections has become a key factor for backers' decision making in crowdfunding platforms. However, current research on the two-way social interaction in crowdfunding is insufficient, and there exist inconsistent conclusions. This study focuses on the social interaction between creators and backers and explores its influence on the successful exit of crowdfunding projects.

The extended Cox model is used for the empirical analysis of 1,988 crowdfunding projects on the Modian (www.modian.com) platform, a crowdfunding platform for cultural and creative projects in China. The two-way social interaction is reflected in comment quantity and sentiment, as well as reply rate.

Results reveal an inverted U-shaped relationship between comment quantity/sentiment and the successful exit of crowdfunding projects. This relationship is strengthened by high reply rate.

This study focuses on comment quantity and sentiment. The inverted U-shaped results reconcile previous conclusions. Replies from creators are regarded as a separate factor, and their moderating role is explained. The study research proves the importance of social interaction in crowdfunding platforms and provides suggestions for backers, creators and platform managers.

Little is known about how religious-related beliefs (i.e. karmic beliefs and beliefs in a just world) might affect consumers to engage in more environmentally friendly behavior. Drawing on the justice motive theory, this study aims to explore the individual, complementary, congruent and incongruent impacts of karmic beliefs and belief in a just world on consumers’ proenvironmental consumption.

A sample of 736 consumers was recruited by means of a mall-intercept survey in the most populus cities in Vietnam. An advanced technique – polynomial regression with response surface analysis – was used to test the hypothesized model, whereas the PROCESS macro approach was used to estimate indirect effects.

The findings indicate that while karmic beliefs do not directly affect proenvironmental consumption, they can inspire environmentally friendly behaviors through belief in a just world. High levels of both beliefs enhance proenvironmental consumption, but imbalanced levels erode such behaviors. Importantly, the findings revealed gender differences concerning the role of religious beliefs in influencing proenvironmental consumption among male and female consumers.

The findings of this study offered several theoretical and managerial implications for proenvironmental consumption.

The research provides new insights into how religious beliefs (karmic beliefs and belief in a just world) affect, interact, balance and imbalance with each other to trigger proenvironmental consumption. The research also contributes to the sustainable consumption literature by indicating the mediation roles of belief in a just world and proenvironmental consumption intention in the relationship between beliefs in karma and actual proenvironmental consumption behavior, as well as offering empirical evidence about the gender differences regarding these relationships.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study expanded previous studies on the relationship between POP (Perceptions of Organizational Politics) and OC (Organizational Commitment); and it examined the mediating effect of OCM (Organizational Concern Motive) and the moderated mediating effect of PS (Political Skills). Based on the uncertainty management and expectancy theories, the mediating role of OCM was assumed; also, the moderated mediating effect of PS was assumed, because of the effect of the POP on members' motives would vary depending on each individual's ability to respond.

To collect the required data for analyzing the research model of this study, a questionnaire was conducted on employees of South Korean companies and institutions. To verify the hypothesis of this study, the measurement and hypothesized models were analyzed sequentially.

The results of this study indicated that the OCM mediates the relationship between POP and OC. Furthermore, the mediating effect of OCM is moderated by PS. Specifically the negative effect of the POP on the OCM was mitigated in the group with proficient PS.

To mitigate the negative effects of POP, this study suggests that managers should provide fair evaluation and reward based on performance, and consider PS as an important factor in employee selection and education/training.

This study is original in that it presents the mechanisms of both variables in terms of an individual's motive towards the organization. Furthermore, this study is valuable in that it suggests ways to mitigate the negative effects of POP through PS.

This paper aims to develop and validate a scale to measure knowledge-sharing motives at work. It is aimed to construct a scale which is explicitly different from knowledge-sharing behavior and to develop a comprehensive and domain-specific scale for this special kind of work motivation.

The constructed scale was tested in two studies. Survey data (n = 355) were used to perform an exploratory factor analysis. Results were further tested on survey data from the core public sector (n = 314) and the health sector (n = 315). A confirmatory factor analysis confirms the results in both samples. The developed scale was further validated internally and externally.

The analysis underlines that knowledge-sharing motivation and knowledge-sharing behavior are different constructs. The data suggest three dimensions of knowledge-sharing motives: appreciation, growth and altruism and tangible rewards. While it is suggested that the developed scale works in the public as well as the private sector context, it is found that knowledge sharing of public employees is merely driven by “growth and altruism” and “appreciation of coworkers.”

No comprehensive and reproducible scale to measure knowledge-sharing motives, which is different from behavior and domain-specific as well, was available in the literature. Therefore, such a scale has been constructed in this study. Furthermore, this study uses samples from different organizational sectors to deepen the understanding of knowledge sharing in context.