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The purpose of this study was to develop a measurement scale to assess work motives for hospitality employees utilizing McClelland’s theory of needs as the theoretical underpinning.

Both qualitative and quantitative methods were applied to achieve the study purpose. First, individual interviews were conducted to explore employee work motives and develop work motive scale items. Second, data from a self-administered paper questionnaire, completed by 388 respondents, were used to validate the developed scale.

Four themes were identified which reflect employees’ perspectives of hospitality jobs and culture: the job itself, need for affiliation, need for achievement and need for power. The developed scale, consisting of 22 items, was found to be reliable and valid in assessing work motives.

The majority of participants were entry-level employees; therefore, the developed scale may not be useful when assessing work motives of individuals not in front-line positions. Future research could extend the measurement model to investigate work motives of individuals in managerial positions. In addition, future research could assess work motives as antecedents of employee organizational behaviors and attitudes.

The developed scale could be used as a selection tool to assess applicants’ work motives, thereby assisting employers in making effective hiring decisions.

This study contributes a new reliable and valid measurement scale developed specifically to address the unique work motives desirable for individuals seeking employment in the hospitality industry.

Discusses the concept of networking structure for the co‐ordination in the corporate decision‐making process in Japan. Looks at networking at a micro level based on empirical and anecdotal evidence.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required.

The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website.

The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach.

Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

Dysfunctional customer behavior (DCB) is costly and problematic for organizations. This research seeks to understand how DCB spreads and how businesses can effectively deal with it through employee intervention.

This research conducted a survey study and an experimental study to examine the proposed model.

Through two studies, we discovered that when an employee intervenes to stop DCB and is perceived as having high coping ability, observing customers learn from the employee’s action, resulting in reduced empathy toward the dysfunctional customer and diminished intentions to engage in DCB. Conversely, if they perceive the employee as having low coping ability, the intervention backfires, enhancing the observers’ empathy toward the dysfunctional customer and consequently leading them to engage in more DCB.

This research unveils an additional mechanism that explains the spread of DCB. It also contributes to the employee intervention literature by shedding light on when employee intervention can backfire. Further, our application of social learning theory along with the person-situation interaction literature offers a fresh perspective in explaining service exchanges.

This paper aims to explore the current stage of biodiversity preservation in higher education institutions (HEIs), using samples from German universities and investigating three different dimensions, namely, existing approaches, challenges and future developments.

Semistructured expert interviews were conducted with employees from 14 German Universities. To analyze the data, both deductive and inductive methods of content analysis were conducted to systematically code the results.

This research shows that efforts for biodiversity preservation are frequently made in the areas of student and staff initiatives, green space management, as well as in research and teaching. However, German HEIs face numerous hurdles such as interest conflict, lack of prioritization and financial restrictions, some of which are difficult to overcome. In the future, HEIs should seek a deeper integration of biodiversity into curricula to further raise biodiversity awareness.

This study provides practical implications by providing good practices, which can serve as a guide for implementing measures for biodiversity preservation. By understanding the struggles and future expectations, stakeholders and policymakers could promote targeted measures and relevant policies to advance biodiversity initiatives at and beyond universities. Moreover, the research shows the importance of involving students and staff actively in the planning and executing stage for biodiversity preservation through initiatives to achieve green campuses.

The paper presents an essential research field which is still in its earlier stage. To the best of the authors’ knowledge, it is the first study that focuses on German Universities in this context. This study adds value in providing in-depth insights into this topic to raise awareness of biodiversity preservation in both practice and research.

The purpose of this paper is to examine whether firms use different earnings management approaches when facing financial difficulties and the effects of industry-specialist auditors in constraining those choices. The empirical results suggest that (1) firms with lower risk of business failure but with stronger incentives to adjust earnings upward tend to use real earnings management (REM) income-increasing approaches while (2) at the same time, using discretionary accruals for income-decreasing earnings management, due to constraints imposed by specialist auditors on the use of accrual-based earnings management (AEM). This is consistent with the findings of Chi et al., and the authors do not find similar evidence for the firms with higher risk of failure. Also, (3) regardless of the level of failure risk, firms turn to REM while interestingly, such REM behavior is effectively curbed by industry-leading specialist auditors (specialist auditors with the highest client market share) on financially distressed firms. These results extend the findings of Chi et al. (2011), suggesting that industry-specialist auditors have different tolerance levels for earnings management approaches by firms with different levels of business failure risk. That is, when auditing clients with higher risk of failure, specialist auditors are more likely to maintain higher audit quality through more stringent audit testing and use of more audit staff time to prevent an occurrence of audit failure.

The authors examine earnings management behavior across firms in Taiwan with different levels of business failure risk and the effects of audit partner industry specialization in constraining that behavior. Chi et al. (2011) studied low-risk firms with incentives to adjust earnings upward and found firms use REM when the auditors constrain AEM. The authors extend the work of Chi et al. and observe firms with different levels of failure risk.

The authors find (1) lower risk firms may use discretionary accruals to adjust earnings downward while the authors find no similar evidence for financially distressed firms, (2) lower risk firms may use REM when their industry-specialist auditors curb AEM and (3) the industry leaders among specialist auditors do the same for the financially distressed firms. The results demonstrate the extent to which industry-specialist auditors apply different tolerance levels for earnings management behaviors across firms with different levels of failure risk.

The study contributes to the literature in the following three ways: first, the authors fill a gap in the existing literature by comparing firms with higher risk of business failure to firms with lower risk of business failure to explore the possible difference in the two different kinds of earnings management behavior; second, the authors extend the findings of Chi et al. (2011) and examine whether specialist auditors, when auditing firms with higher risk of business failure, will input more audit effort to constrain their clients' use of REM and third, since business failures have a significant impact on the capital markets and any associated audit failures can have an even greater negative impact on investor confidence, the study provides information useful to auditors and regulators in the formation of salient policy regarding the use of REM by firms experiencing high risk of business failure.

The purpose of this paper is to investigate how and when used by employees influences witnessing customers’ willingness to spread positive word of mouth (WOM).

This research used a qualitative method to develop a typology of necessary evil using two pilot studies and an experimental study to test the theoretical model.

The results show that the necessary evil used by employees to manage dysfunctional customers positively influences witnessing customers’ perceptions of distributive, procedural and interactional justice and their subsequent deontic justice perceptions, resulting in their willingness to spread positive WOM. Moreover, the positive influence of necessary evil on witnessing customers’ responses is strengthened when dysfunctional customer behavior (DCB) targets another customer as opposed to an employee.

This research offers service providers a better understanding of how to manage DCBs.

This paper contributes to the existing literature by introducing necessary evil to the service literature, proposing a new typology of employee response strategies to DCB based on necessary evil and examining how necessary evil drives positive customer responses. Additionally, it is among the first to examine the relationship between deontic justice and traditional justice mechanisms.

This study proposes the novel construct of digital transformation anxiety and investigates its effect, which is mediated by absorptive capacity and dynamic capability, on digital innovation performance.

This study conducted a questionnaire survey among Taiwanese manufacturing and service companies to verify the research framework. A total of 130 valid responses were collected and analyzed using partial least squares structural equation modeling (PLS-SEM) and bootstrapping to test direct and mediation effects, respectively.

Digital transformation anxiety negatively affects absorptive capacity and dynamic capability, whereas absorptive capacity and dynamic capability positively affect digital innovation performance. Dynamic capability more strongly mediates the association between digital transformation anxiety and digital innovation performance than absorptive capacity. Additionally, digital transformation anxiety does not negatively affect digital innovation performance. Finally, manufacturing companies had significantly higher levels of digital transformation anxiety than service companies.

This study proposes the novel construct of digital transformation anxiety to address a gap in the literature. Digital transformation anxiety leads companies to adopt unnecessarily conservative practices, preventing them from flexibly responding to technological advances. This insight highlights the negative effect of such anxiety on absorptive capacity and dynamic capability, extending the application of path dependency theory to companies. The findings underscore the value of enhancing dynamic capability and reallocating resources to foster digital innovation. The study identified and explored the concept of digital transformation anxiety and extended the perspective of dynamic capability to include digital transformation and digital innovation.

The current findings indicate that digital transformation anxiety does not substantially affect digital innovation performance in Taiwanese companies. Consequently, Taiwanese companies should focus on developing their absorptive capacity and dynamic capability to enhance digital innovation.

The study proposes the novel construct of digital transformation anxiety and explores its effect on business units. It presents a pioneering framework derived from path dependence theory and the perspective of dynamic capability.