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Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

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 posit that managerial value would be one of the responsible factors for the difference in corporate social responsibility practice among businesses. It then empirically tested the effect of managerial value, with the moderation of organizational culture, on corporate social responsibility practice.

The authors have devised a “moderated micro-macro model” type of multilevel model, wherein managerial value took the micro (individual level) predictor variable role, stakeholder-based corporate social responsibility practice the macro (organizational level) outcome variable role and organizational culture the macro level moderating variable role. Because they need the attention of inquiry, large manufacturing firms in the Amhara region of Ethiopia, with a sample size of 53, constituted the organizational level units. The recent performance of the firms against corporate social responsibility practice and organizational culture have been judged by 473 randomly chosen employees. Managerial value has been rated by randomly picked managers, numbered 253. Analytically, Croon and van Veldhoven’s multilevel analytical package and Mplus software suited the designed model.

The study has revealed that managerial value, indeed, is a potential positive driver of CSR practice, the two managerial value dimensions demonstrated differential effects on corporate social responsibility practice and only one of the organizational culture dimensions, hierarchical culture, played a moderation role in managerial value – corporate social responsibility practice link.

The model and this empirical test have not been previously verified.

Current models of organizational excellence are appropriate for the private organizations. It is evident that if an appropriate model is not adopted, the process of excellence in the organizations fails and some dimensions of the organization get affected by unpredictable damages. This research aims to identify an appropriate excellence model for public organizations.

First, a comprehensive literature review was conducted to identify the excellence criteria and models. Second, the models were through an expert-oriented questionnaire, analyzed by the analytical hierarchy process (AHP) technique. Participants were experts in the two domains of excellence models and public sector management. A sample of 15 experts was selected using purposive sampling. In order to emphasize on reliability, 10 questionnaires were adopted for analysis.

The findings showed that the European Foundation for Quality Management (EFQM) model is the most appropriate model for excellence measurement in the public organizations based on the five selected indices.

The identification of a model for measuring organizational excellence for public sector can significantly contribute to existing literature on excellence measurement.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

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.

This study aims to clarify the following research questions: to what extent do people consider natural disaster risks as important for residential selection? what personal demographics and attitudes toward natural disaster risks are associated with the relative importance of natural disasters for residential selection? and to what extent do the associated personal attributes influence the relative importance of natural disasters for residential selection?

An internet-based survey was performed to collect 2,000 responses from residents of Osaka Prefecture, Japan, to gauge people’s relative importance of safety against natural disasters regarding residential preference. The obtained results were analysed using two types of statistical analysis, specifically chi-square test and multivariable logistic regression analyses.

It was found that 37.3% of the respondents in Osaka Prefecture, Japan, considered the “safety against natural disasters” relatively important when selecting a residential location. The statistical analysis also demonstrated that those having a relatively higher level of disaster awareness and preparedness were 1.41 times more likely to prefer to live in a place that is safer from natural disasters. Thus, it was suggested that disaster education aimed at raising the level of people’s disaster awareness could be effective to increase the number of people who choose to live in a safer place from natural disasters.

Living in an area that is safer from natural disasters can effectively minimize human and property damage. Recently, several measures have been taken in Japan to guide people to live in a safer place. The clarification of the extent to which people consider natural disaster risks as important for residential selection and the understanding of the categories of the people who are likely to do so is important to develop more effective natural disaster measures; however, there has been less attention on such investigation. Therefore, this study conducted an internet-based survey and examined it.

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.

Pricing is widely acknowledged as a market entry challenge for servitising companies. The purpose of this research is to ascertain the attributes that contribute to willingness to pay (WTP) for industrial services and suggest incorporating those attributes to a pricing model.

Three attributes (Quality of Service, Nearness of Service Provider and Brand Equity of Service Provider) were analyzed at three respective levels to ascertain their importance on WTP. Conventional conjoint analysis (CCA), using an orthogonal design, was the method used. The 346 respondents were decision-makers and top management professionals from various industries.

Brand Equity emerged as the most significant attribute contributing to WTP, having more than 45% importance – followed by the Quality and Nearness.

The scope of the study is limited to the industries and its Allies. However, the relative importance of the attributes may vary depending on the type of service.

The importance of attributes and their WTP preference helps future researchers create a pricing model involving these attributes. This helps service providers price their services rationally, thus succeeding in servitization.

Product life is extended because the manufacturers themselves are servicing it and also help recycle the product with their expertise. Servitization is also helpful for the Indian economy, as it is turning into a manufacturing economy.

This research investigates three attributes that contribute to WTP, in accordance with their level of contribution. It also provides a direction to establish an adequate pricing model for industrial services.

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.