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This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.

The study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.

Study results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.

Study findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.

This study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.

To overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.

Inter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.

To the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

Understanding the adoption of m-commerce in small and medium-sized enterprises (SMEs) is critical for their sustainable development. This study aims to investigate the adoption of m-commerce in Vietnamese SMEs, leading to the identification of the critical determinants and their relative importance for m-commerce adoption.

An integrated model is developed by combining the diffusion of innovation theory and the technology–organization–environment framework. Such a model is then tested and validated using structural equation modeling and artificial neural networks in analyzing the survey data.

The study indicates that perceived security is the most critical determinant for m-commerce adoption. It further shows that customer pressure, perceived compatibility, organizational innovativeness, perceived benefits, managers’ IT knowledge, government support and organizational readiness all play a critical role in the adoption of m-commerce in Vietnamese SMEs.

The findings of this study can lead to the formulation of better strategies and policies for promoting the adoption of m-commerce in Vietnamese SMEs. Such findings are also of practical significance for the diffusion of m-commerce in SMEs in other developing countries.

To the best of the authors’ knowledge, this is the first attempt to explore the adoption of m-commerce in Vietnamese SMEs using a hybrid approach. The application of this approach can lead to better understanding of the relative importance of the critical determinants for the adoption of m-commerce in Vietnamese SMEs.

Prior research examining cultural effects on customer service expectations has primarily used more generic Western cultural theory on an aggregate scale or with only a single variable to draw conclusions on a customer’s underlying reasoning for buying a service. This study aims to focus on culturally distinct clusters within non-Western nations, specifically exploring within-cluster differences in service expectations within the Confucian Asia cluster.

This study developed a measurement model of Chinese cultural values and service expectations, consisting of a three and five-factor structure, respectively. Data from a sample of 351 diners were analysed using SmartPLS software. The data was compared with similar studies within the Confucian Asia cluster to understand the culture effect on service expectations and within-cluster variations.

The findings underscore the varying importance of cultural values in shaping customer service expectations, emphasizing their relative, rather than equal, significance. The study provides insights into potential within-group differences in customer service expectations within the same cultural cluster – without losing sight of the fundamental cultural heterogeneity of the Confucian culture.

Managers should leverage the distinct cultural values of their operating country to gain insights into diverse customer groups, predict their behaviours and meet their needs and expectations.

This study offers valuable insights to both service management scholars and practitioners by focusing on culturally distinct clusters of non-Western nations and exploring their effects on variation in service expectations within these clusters.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.

Nutrition labels provide a cost-effective method of conveying nutrition information to consumers. This study aimed to assess the use of nutrition facts panels, knowledge of traffic light labelling (TLL) and perceived healthiness of food items using TLL among consumers.

A web-based cross-sectional study was conducted among adults in the United Arab Emirates (UAE) (n = 1,322). TLL knowledge score was derived for each participant. Conjoint analysis was used to calculate the utilities and relative importance of the perceived healthiness scores for four attributes (fat, saturated fat, total sugar, salt) at the aggregate level.

Participants had a positive attitude towards TLL but were less familiar with TLL than the nutrition facts panel (47.4 vs 85.8%). The mean TLL knowledge score was 3.6 out of 7 (51.6%). Younger age, higher education, higher income, and health-related qualifications were associated with higher scores. Conjoint analysis showed that participants tend to choose products with greener labels, especially for sugars (80.1%) and avoid red labels for fats. Sugars had the highest percentage value of relative importance compared to the other attributes (27.1%).

The study outcomes offer valuable insights into the extent of consumer awareness, comprehension and utilization of nutrition facts panels in the UAE. These findings contribute essential knowledge for a deeper understanding of the impact of nutrition labels on consumer behaviour and decision-making in the region.

This paper uses the complex proportionality assessment (COPRAS) method to examine the driving factors of Industry 4.0 (I4) technologies for lean implementation in small and medium-sized enterprises (SMEs).

Adopting I4 technology is imperative for SMEs seeking to maintain competitiveness within the manufacturing sector. A thorough understanding of the driving factors involved is required to support the implementation of I4. For this objective, the multi-criteria decision-making (MCDM) tool COPRAS was used to efficiently analyze and rank these driving elements based on their importance. These factors can help small and medium-sized firms (SMEs) prioritize their efforts and investments in I4 technologies for lean implementation.

This study evaluates and prioritizes the nine I4 factors according to the perceptions of SMEs. The ranking offers significant insights into the factors SMEs consider more accessible and effective when adopting I4 technologies.

The author's original contribution is to examine I4 driving factors for lean implementation in SMEs using COPRAS.

With the increasing use of social media in operation and supply chain management (OSCM), it is of great importance for managers to consider risks in advance and take precautions against the risks that might arise from social media usage among supply chain members. The aim of this research is to identify and evaluate the risks related to the use of social media in OSCM.

An initial research of the literature revealed that there is no detailed risk categorization in this area. Current taxonomies on the business risks of social media usage were examined and integrated with classifications identified in a Delphi study. The authors empirically demonstrate how the determined risks are prioritized and how decision-makers may decide to manage risks effectively based on the analytical hierarchy process (AHP) method.

The findings of the research showed that reputation-associated risks such as criticism, language and loss of confidence should be prioritized over human, content and technical-associated risks.

To date, a comprehensive approach to determine risks arising from using social media in OSCM is missing. With the Delphi and AHP techniques, the authors provide a novel insight for managers to mitigate risks. The outcomes of this study may assist executives in achieving successful management of social media usage in OSCM. Besides, the proposed AHP model may provide guidelines and direction in this regard.