Search results

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

We analyze the economic consequence of government intervention on the incidence of accounting fraud and audit fees of both Big 4 and local big auditors on Chinese audit market in the period 2006–2013. In 2009, Chinese government issued favorable polices to local big auditors and required certain Chinese companies to give priority to these auditors. We find that market share of Big 4 auditors is quite stable before and after government intervention, but market share of local big auditors increases at the cost of local small auditors after intervention. Although audit fee premiums of both local big and Big 4 auditors have increased after intervention, the positive effect of local big auditors on audit fee premiums has significantly decreased. Further, both Big 4 and local big auditors are not likely to reduce the incidence of accounting fraud in pre- and post-intervention period. Our results suggest that Chinese government support to local auditors does not significantly enhance these auditors’ competitiveness in terms of audit fee and audit quality.

The purpose of this article is to understand how academics in management deal with the concept of generation in the workplace. We begin by conducting an interdisciplinary literature analysis, thereby elaborating a conceptual framework concerning generational diversity. This framework consists of four levels of analysis (society, career, organisation and occupation) and three dimensions (age, cohort and event/period). We then conduct a meta-analysis using this conceptual framework to analyse papers from the management field. The results from this analysis reveal the existence of a diversity of generational approaches, which focus on the dimensions of age and cohort on a societal level. Four factors seem to explain these results: the recent de-synchronisation of generational dimensions and levels, the novelty of theoretical models, the amplification of stereotypes by mass media and the methodologies employed by researchers. In sum, this article contributes to a more realistic view of generational diversity in the workplace for both academics and practitioners.

To explore income inequality in urban China, this paper investigates disparities between- and within-urban locals and rural migrants from 2002 to 2013, using three waves of the China Household Income Project (CHIP) data. While the existing literature concentrates on the wage disparity between these two groups, our results show that the Gini among the migrants increased by 17.86% between 2007 and 2013 and that among the locals increased by 15.54% from 2002 to 2007. The urban–migrant average income gap decreased during the whole period mainly due to higher growth in migrants’ average income. Estimates based on Mincerian earnings functions for both groups reveal the significant role of the education, occupation and type of contract in determining the within-group inequality. In addition, using a recentred influence function (RIF), we observe that short-term and other types of contracts, duration of the job, in-system ownership, marriage and skill have inequality-enhancing effects for migrants. The variation of skills has a larger impact on the income disparity among migrants than on that among urban locals. The RIF-based Blinder–Oaxaca decomposition of the mean difference of incomes shows that labour market discrimination between the two groups is not significant; however, both pure explained and unexplained differences are significant when applying the RIF decomposition to the variance of the logarithms of incomes. While the type of contract significantly reduces the pure explained difference between migrants and urban locals, occupation has a positive impact on this difference between these two groups. The heterogenity analysis shows that the factors influencing incomes in these two groups are different. We recommend labour market intervention to reduce unreasonable occupational and sectoral disparities, especially in the net inflow provinces, to mitigate urban inequality in China effectively.

Purpose: The purpose of this study is to explore the role of internal branding (IB) in fostering branding citizenship behavior in the hospitality context as well as the mechanisms underlying the relationship.

Design/methodology/approach: This study obtained empirical evidence from 377 hotel employees in North Cyprus.

Findings: Our findings support the positive relationship between IB and brand citizenship behavior (BCB). The evidence was found for a dual and sequential mediating role of brand trust and brand commitment. Moreover, the organizational climate serviced as a moderator to influence the positive relationships between IB and BCB.

Practical implication: This study has shown that employees are rewarding firms involved in IB initiatives in the form of BCB – directly and indirectly –through trust and commitment. This finding can advance managers’ understanding, enabling them to better manage their IB initiatives to achieve the most effective outcomes.

Originality/value: The research advances convergence between IB and BCB research streams, which has been under-explored in the tourism context. Besides, it extends the IB and brand citizenship literature through a novel dual and sequential mediation mechanism and organizational climate as a novel moderator.