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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.

Drawing on person–environment fit theory, this study aims to investigate how the relationships between service task types (i.e. utilitarian and hedonic service tasks) and perceived authenticity (i.e. service and brand authenticity) differ under different conditions of service providers (human employee vs service robot). This study further examines whether customers’ stereotypes toward service robots (competence vs warmth) moderate the relationship between service types and perceived authenticity.

Using a 2 × 2 between-subjects experimental design, Study 1 examines a casual restaurant, whereas Study 2 assesses a theme park restaurant. Analysis of covariance and PROCESS are used to analyze the data.

Both studies reveal that human service providers in hedonic services positively affect service and brand authenticity more than robotic employees. Additionally, the robot competence stereotype moderates the relationship between hedonic services, service and brand authenticity, whereas the robot warmth stereotype moderates the relationship between hedonic services and brand authenticity in Study 2.

Restaurant managers need to understand which functions and types of service outlets are best suited for service robots in different service contexts. Robot–environment fit should be considered when developers design and managers select robots for their restaurants.

This study blazes a new theoretical trail of service robot research to systematically propose customer experiences with different service types by drawing upon person–environment fit theory and examining the moderating role of customers’ stereotypes toward service robots.

Extant studies propose music sentiment as a novel measure of individuals’ sentiment. These studies argue that individuals’ choice of music reflects their emotional condition in real time and influences their cognitive ability, making it a powerful tool for assessing their mood. This study aims to use music sentiment as a proxy for auditors’ mood and explore its impact on audit quality.

A sample of the US firms from 2017 to 2020 is used in the study. The authors apply the ordinary least squares regressions and the logit regressions to the audit quality models. The authors use absolute discretionary accruals and the propensity to meet or beat earnings forecasts as proxies for audit quality and calculate a stream-weighted average sentiment measure for Spotify’s Top-200 songs of each day during the audit period of a client firm to capture the sentiment of auditors.

The authors find that music sentiment is positively associated with audit quality. The result is consistent with the mood maintenance hypothesis, which suggests that a positive mood can induce auditors to be more careful in risky situations. Furthermore, the result is robust to various sensitivity analyses.

The study contributes to the scarce literature that focuses on auditors’ emotional state and highlights the importance of monitoring auditor mindset during the audit period.

The purpose of this paper is to unravel the specific service quality dimensions that significantly influence startup survival, providing actionable insights for service managers and entrepreneurs.

Empirical data were collected from 372 service startups over a period of seven years. The Cox proportional hazard model was used to analyze the data.

The results indicate that the tangible dimension of service quality emerges as the most critical determinant of startup survival. Additionally, the reliability and responsiveness of the service also significantly affect startup survival. Furthermore, the assurance and empathy dimensions have a positive, albeit modest, influence on the survival prospects of service startups.

This study contributes to the service literature by investigating the relative importance of each dimension of service quality in relation to the survival of service startups.

The empirical findings empower service startups to make informed decisions, allocate resources judiciously and prioritize aspects of service quality that have a significant impact on their survival and success.

The social implications indicate the significance of service quality dimensions not only for the success of service startups but also for the overall well-being of customers, local economies and the competitive landscape of the service sector.

This study contributes to service science by uniquely highlighting the critical role of tangibles in startup survival, challenging conventional beliefs about the primacy of service reliability.

This study aims to examine the relationship between corporate social responsibility (CSR) and job pursuit intention (JPI), and the role of job seekers’ perception on employer prosocial orientation, value congruence and employer attractiveness in this relationship. CSR is measured based on internal and external CSR.

By adopting quantitative approach, data was obtained through survey questionnaire from 420 bachelor’s degree university fresh graduates from five universities in Malaysia who are actively seeking for jobs. Data was analysed using structural equation modelling technique.

Research findings show that internal and external CSR positively impact job seekers’ perception of employer prosocial orientation. Job seekers’ perception towards employer prosocial orientation has a significant positive impact on value congruence. Value congruence has a significant positive influence on employer attractiveness. Finally, employer attractiveness has a significant positive impact on JPI.

The findings are useful for human resources management. Organisations (employers) should focus on effective internal and external CSR practices through a prosocial orientation approach to attract the best talents and create a strong position in the job market.

This study extends the Signalling Theory and P-O Fit theory by applying them to an entirely different context of CSR and JPI, by incorporated the holistic job seekers’ psychological processes of the recruitment signals (internal and external CSR), signalling process and person-organisation fit (perception on employer prosocial orientation, value congruence and employer attractiveness) thoroughly.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

Cultivation of a learning culture and subsequent enhancements in employee performance can be translated through employee engagement. This study aims to examine the mediating role of employee engagement in the relationship between the learning culture and employee performance.

This research adopted a quantitative approach, wherein 450 questionnaires were distributed among employees in both public and private sectors in Nepal. A total of 389 questionnaires were returned, followed by two to three reminders. Convenience sampling was utilized, and the data was collected through a questionnaire survey. Descriptive analysis and Structural Equation Modeling – Path analysis was used to describe and hypotheses testing. Data was analyzed using SmartPLS 4.0 and SPSS 24v.

It was found that employee engagement has a mediating effect on the relationship between the learning culture and employee performance. Learning culture also has a positive influence on employee engagement that eventually affects the performance of the employees. This conclusion suggests that fostering a learning culture within an organization should be focused on cultivating an environment that promotes active employee participation, thereby enhancing overall employee performance.

This article provides significant insights into the cultivation of a learning culture inside firms, with a specific focus on establishing an atmosphere that fosters active employee engagement to improve overall employee performance in the service sector. This tool has the potential to facilitate further investigation and progress within the area, while also promoting the adoption of evidence-based learning practices and their associated implications.

Land is the basis for economic development as well as tourism development. There is a close relationship between tourism development and the land market. However, research on the effect of tourism development on land prices is insufficient. This paper aims to investigate the effect and mechanism of tourism development on land prices.

The econometric paradigm is the main research method. Fixed effect models, instrumental variable models and mediating effect models are introduced to examine the impact of tourism development on land prices. The data include three types: land transaction data, city-level data and scenic spot data. More than 360,000 samples of land transactions for 284 prefecture-level cities in China from 2007 to 2021 are applied.

Tourism development can significantly increase land prices. This conclusion holds after using instrumental variables to address endogeneity and testing for robustness. Meanwhile, tourism development’s effect on land price is influenced by land type, city type, city tier and city location. The land price increase effect of tourism is more significant for tourism land, tourist cities, central cities and Western cities. The paper also reveals the mechanisms of the public service enhancement effect, infrastructure upgrading effect and environmental optimization effect in tourism development’s effect on land price.

The study contributes to the literature on the relationship between tourism development and land market. The generality and specificity of tourism development’s effect on land price are revealed from the micro and macrolevel research level. The findings enrich the literature on tourism price effects, point to rational ways to optimize and regulate land prices and provide new ideas for land-market development.

Criticality cognitions regarding the same workplace event often differ between leaders and employees. Nevertheless, its consequences on employee work outcomes remain unknown. In this study, we draw on cognitive dissonance theory to examine how and why leader–employee differences in cognitions of workplace event criticality impact employee job-related outcomes.

Wu used multilevel polynomial regression analyses from a time-lagged, multi-source field study with 145 leader–employee dyads to test our proposed model.

Leader–employee differences in cognitions of workplace event criticality can bring both benefits and perils to employees. Specifically, such differences can cause employee rumination, which in turn leads to an increase in both employee voice and fatigue.

This study contributes to the event and cognitive discrepancy literature in four ways. First, prior event studies largely adopted a singular employee perspective for investigation (e.g. Chen et al., 2021; Lin et al., 2021). By examining the impacts of event criticality from the dual perspective of leaders and employees, we attain a more comprehensive understanding of the implications of workplace events in organizational life. Second, extant studies have predominantly focused on the dark side of cognitive discrepancy (e.g. Bashshur et al., 2011; Erdogan et al., 2004; Grandey et al., 2013). Our study reveals that leader–employee differences in criticality cognitions can have both a bright and a dark side on employee outcomes, offering a more balanced and dialectical view of the consequences of cognitive discrepancy. Third, drawing on cognitive dissonance theory, we introduce employee rumination as an underlying mechanism to explain the impacts of leader–employee differences in criticality cognitions on employee voice and fatigue. Finally, while prior cognitive dissonance research has primarily employed an intrapersonal perspective (e.g. Sivanathan et al., 2008; Pugh et al., 2011; Grandey et al., 2013), our study adopts an interpersonal lens and underscores that interpersonal differences in cognitions can also serve as an example of cognitive discrepancy to instigate internal dissonance processes. By doing so, we enrich our understanding of cognitive dissonance theory.

This study aims to comparatively measure the impact factors of financial inclusion and their spillover effects for Belt and Road countries using panel data from 57 countries in 2011, 2014, 2017 and 2021 and relevant indicators from three dimensions: availability, usage and quality to construct a digital empowerment index of financial inclusion.

A spatial Durbin panel model is constructed to empirically test the impact mechanism of financial inclusion under digital empowerment.

Results reveal that improving a country’s quality of regulation, technology and residents’ financial literacy significantly contributes to the development of its financial inclusion, while improving its neighboring countries’ financial literacy also boosts its financial inclusion development. This study provides theoretical support for evaluating the development level of inclusive finance in “Belt and Road” countries, promoting the development of inclusive finance and alleviating the problem of financial exclusion.

This study is original as it creates a research paradigm for “Belt and Road” countries, enabling systematic testing and comparative analysis of inclusive finance development. It incorporates traditional and digital services, evaluating them based on sharing, fairness, convenience and specific group benefits. An inclusive financial index is constructed using the coefficient of variation and arithmetic weighted average methods. Additionally, it introduces a more rational analysis approach for the influence mechanism and spatial effect, using an economic geography nested matrix and spatial Durbin model to explore spatial effects in inclusive finance.