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This paper aims at comparing and contrasting the Ao Man-long scandal with the Ho Chio-meng case in Macau, drawing lessons from the two events and casting lights on the literature on corruption scandals.

The study used documentary research and interpretative and analytical approaches.

The two cases show considerable administrative discretion on the part of the principal officials involved, and remedial measures along the line of having more rigorous and frequent internal auditing may be necessary.

Original analyses were conducted together with literature review and documentary research. This paper would be of interest to scholars and practitioners concerned with how Macau combats corruption.

Environmental emissions are increasing in the urban areas. Much of the emissions arise from public procurement activities given that public sector firms are major customers to many supplying firms. Given the tremendous contribution, this study aims to examine the adoption of environmentally friendly urban freight logistics practices among public sector firms through assessing the impact of urban environmental governance, government environmental communication and organizational environmental governance.

Data for the study were collected in a single time period from central procuring and disposing entities (public sector firms) in the urban areas. A sample of 105 public sector firms in were used. One procurement officer and one member of the contracts committee were the key informants in the study. AMOS SPSS version 26 was used to obtain the results for the structural model and measurement model, respectively.

The findings indicate that the adoption of environmentally friendly urban freight logistics practices among public sector firms is significantly influenced by government environmental communication, organizational environmental governance and urban environmental governance. Urban environmental governance significantly influences organizational environmental governance. Urban environmental governance fully mediates the relationship between government environmental communication and public sector firms’ adoption of environmentally friendly urban freight logistics practices. Also, urban environmental governance and organizational environmental governance mediate the relationship between government environmental communication and adoption of environmentally friendly urban freight logistics practices.

This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. However, the study was conducted in a public procurement setting rather than a private sector procurement setting. Also, the study examined the impact of government environmental communication on public sector firms’ adoption of environmentally friendly urban freight logistics practices ignoring the impact of internal communications made within the public sector firms on environmental issues.

This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. Freight logistics in public sector procurement has not been given significant attention in earlier research. Emphasis is placed on sustainable public sector procurement ignoring other aspects that would help curb environmental emissions that may arise during and after the delivery of public procurement requirements.

The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system.

The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly.

An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic.

In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.

This study examines the drivers of consumers’ intentions to adopt mobile wallets in Pakistan’s hospitality industry. Specifically, it proposes and tests a model of non-user consumer intention to adopt mobile wallets for hospitality in Pakistan.

A conceptual framework grounded in the mobile technology acceptance model (MTAM) integrating personal innovativeness in IT, mobile perceived compatibility, perceived critical mass, perceived enjoyment, mobile perceived risk and mobile perceived wireless trust was used as a theoretical model of the study. Using structural equation modeling, we tested the research model and its relevant hypotheses on a sample of 310 mobile wallet nonusers.

Findings from the expanded model demonstrate that only four of the suggested hypotheses were insignificant in this study and require additional examination. Overall, the modified model explained 63% of the variance in the behavioral intention to adopt mobile wallets. This paper concludes with key implications and directions for future work concerning the limitations of this study.

This study contributes to a theoretical understanding of the factors that explain nonusers’ behavioral intention to use a mobile wallet in the hospitality context.

The purpose of this study is to investigate the robotic restaurant experience of travellers around the world and understand the components of robotic restaurant experience.

Travellers who had experienced a robotic restaurant were purposefully selected as a sample group for the study. As the robotic restaurants are limited around the world, multiple case study method has been chosen to gather richer data. A user-generated content technique which is a form of qualitative case study method has been benefited to gather data from travellers’ reviews.

The results reveal a model of components of robotic restaurant experience that include six main themes: attraction for kids, robotic system, memorable experience, ambience related attributes, food related attributes (economic value and gastronomic aspects) and deficiencies (in robotic system, in ambience related attributes and in food related attributes).

This paper is one of the first to investigate the robotic restaurant experience of travellers around the world. Moreover, it contributes to the research on restaurant experience and offers a model of components of the robotic restaurant experience.

This study aims to investigate the strategies that successful companies in the construction industry use to achieve systemic transformation within their value chains and broader ecosystems. This study focuses on understanding how these companies navigate the challenges of a mature and slow-changing industry through innovative approaches.

A multiple case study design is used to analyze the systemic innovation dynamics across construction subsystems, through the lens of Complex Adaptive Systems theory. This research involves an empirical examination of nine case companies, selected based on their innovations in at least two construction subsystem areas and markers of success such as growth, profitability or invested capital. This methodology facilitates a detailed understanding of the company-level transformation.

This study identifies four distinct development paths toward systemic transformation within the construction industry: product-driven, product platform-driven, process integration-driven and business model-driven. These paths are characterized by unique strategies integrating technology, optimizing processes and innovating business models, demonstrating how companies can effectively adapt to and evolve within the construction ecosystem.

This research contributes original insights into company strategies and how they achieve systemic transformation. This study expands existing knowledge on systemic transformation in the construction industry by integrating empirical evidence with theoretical frameworks, offering a novel perspective on how successful companies innovate within a traditionally slow-changing sector.

Augmented reality (AR) adoption has boomed globally in recent years. The prospective of AR to seamlessly integrate digital information into the actual environment has proven to be a challenge for academics and industry, as they endeavor to understand and predict the influence on users' perceptions, adoption intentions and usage. This study investigates the factors affecting consumers’ behavioral intention to adopt AR technology in shopping malls by offering the mobile technology acceptance model (MTAM).

This conceptual framework is based on mobile self-efficacy, rewards, social influence and enjoyment of existing MTAM constructs. A self-administered questionnaire, constructed by measuring questions modified from previous research, elicited 311 usable responses from mobile respondents who had recently used AR technology in shopping malls. This analysis was performed using SmartPLS3.0.

Grounded on the findings of the study, it was found that, aside from factors such as mobile usefulness, ease of use and social influence, the remaining independent variables had the most significant impact on adopting AR technologies. Considering the limitations of this study, the paper concludes by discussing the significant implications and insinuating avenues for future research.

To better investigate mobile AR app adoption in Pakistan’s shopping malls, the researchers modified the newly proposed MTAM model by incorporating mobile self-efficacy theory, social influence, rewards and perceived enjoyment. However, the extended model has not been extensively studied in previous research. This study is the first to examine the variables that affect an individual’s intention to accept mobile AR apps by using a novel extended MTAM.

This paper aims to inform the promotion of sustainable modes of transport. For this purpose, it deploys a means-ends framework as a type of second-order cybernetics and uses it to explore cognitive transport mode choice structures.

The empirical study relies on a purposive sample and a qualitative research methodology known as laddering. It is aimed at the identification and comparative analysis of the cognitive means-ends structures of transport users.

The results reveal more positive and complex associations for the car than for public transport. Two main positive means-ends structures are identified for public transport, one related with the relaxation and the other with doing useful things while travelling. Dominant positive structures for the car are related with self-confidence, satisfaction and personal freedom. Negative means-ends structures in addition reveal important justifications and rationalizations for car use.

Based on the identified distinct means-ends elements and structures, this study holds important implications for developing a communications strategy and policy interventions seeking to promote public transport.

Means-ends theory is proposed as an integrative cybernetic framework for the study of stakeholders’ (customers’) mental models. The empirical study is the first to concurrently and comparatively examine positive and negative means-ends chains for the car and for the public transport modes.

This paper aims to provide a systematic review of extant literature on supplier selection by identifying drivers of integration of Environmental Sustainability (ES) into supplier selection, with the aim of classifying them under broad categories for the development of a framework showing the interrelationships among them.

A search was ran for selected keyword themes using three powerful and rigorous search engines: Scopus, Google Scholar and Science Direct, to identify relevant articles from 12 peer-reviewed journals. These were desk reviewed through manual filtering to select drivers of integration of ES into supplier selection.

Thirty-one drivers identified from 41 relevant articles as propelling integration of ES into supplier selection were then classified into five categories: strong policy direction, high level of commitment, desire for high reputation, robust technology and availability of green products. This yielded a framework showing the interconnectedness among the drivers.

The study contributes to the body of knowledge by developing a classification of drivers of integration of ES into supplier selection. The interconnectedness brought to the fore a more subtle appreciation of the drivers of green integration, which could help expand current knowledge outside the narrow scope of isolated drivers. This study provides detailed analyses of drivers of green integration into supplier selection.

This paper provides a comprehensive review of drivers of integrating green into supplier selection, which is lacking in the body of knowledge on ES.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.