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This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

This paper reviews the literature on the business models of small and medium-sized enterprises (SMEs). It seeks to examine the profile, conceptual and intellectual structure of the literature whilst leveraging the findings to suggest promising future paths to advance our knowledge on business models of SMEs.

The study resorts to a systematic literature review that conducts descriptive, bibliometric (i.e. co-word occurrence analysis and bibliographic coupling of documents analysis) and content analyses to review the literature on business models of SMEs. The research protocol included 301 articles collected in the Web of Science (WoS) database in the descriptive and bibliometric analyses. The bibliometric analysis was performed using the VOSviewer software.

The descriptive analysis portrayed the profile of this research stream. The systematisation of the co-word occurrence analysis describes the four clusters that comprise the conceptual structure of this research field. The content analysis of the bibliographic coupling of documents’ clusters portrays the seven clusters that involve the intellectual structure of this research area.

The integrated and holistic approach adopted in this study provides a detailed overview of the literature on business models of SMEs. We propose an integrative framework for the literature that bridges the main themes that form the conceptual and intellectual structure of this field of research. A comprehensive agenda for future research is suggested and implications for theory, policy and practice are stated.

This study aims to examine the effect of application architecture and application efficiency on the intention to continue using Islamic bank with data security as a moderator. The investigation was situated within the framework of a hacker attack that compromised the security of customer data at one of Indonesia’s largest Islamic bank.

A survey questionnaire method was used, and the sample population comprised users of Islamic bank in Indonesia. The respondents were then selected purposively with the criteria of individuals who were using mobile banking services. Furthermore, data collection in this study was carried out by distributing questionnaires online. To validate the questionnaire, consultation and validation were conducted by engaging four experts and conducting a pilot study. Hypothesis testing was performed using the structural equation modeling method based on partial least squares.

The results of the partial least square structural model assessment showed that application efficiency and data security positively influenced the intention to continue using Islamic bank, while application architecture had no effect. Furthermore, data security could not moderate the relationship between application architecture and efficiency toward the intention to continue using Islamic bank.

The results of this study suggested that Islamic banking practitioners must prioritize the enhancement of digital banking services, with a specific focus on improving application efficiency and ensuring robust data security. These two dimensions were critical determinants influencing the intention to continue using Islamic bank.

This study addressed the issue of data security as a moderator, particularly in the context of hacker attacks targeting a major Islamic bank in Indonesia. Furthermore, this current report expounded on the study conducted by Mir et al. (2022) by introducing novel dimensions to the e-service quality of internet banking.

E-procurement has the advantage of improving the overall performance of construction project delivery. The purose of this study is to uncover the factors influencing e-procurement acceptance in the public sector of Ghana’s construction industry (GCI). Using an extended unified theory of acceptance and use of technology (UTAUT 2), variables that promote the acceptance of e-procurement in GCI were explored.

Data was collected using a mixed method strategy. In the first stage, a semi-structured interview was used to collect, assess and optimize the UTAUT2 model. Thematic analysis was used on the qualitative data, leading to modification of the proposed UTAUT2 model. The study subsequently used a questionnaire survey using the extended UTAUT2 model. Survey data was analyzed using structural equation modelling (SEM), leading to the identification and validation of factors that facilitate e-procurement acceptance in Ghanaian construction as well as the impact of these factors.

The findings of the study reveal that five independent constructs of the proposed UTAUT2 model significantly affects the behavioural intention of practitioners to accept and use e-procurement in the construction industry in Ghana.

The study will be of utility to government agencies, contracting organizations and other construction stakeholders in developing policy and programmes to support e-procurement acceptance within the sector.

This is a new extended UTAUT2 model that is applicable to technology acceptance within the public sector in sub-Saharan Africa.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Within food industry several changes and innovations are affecting the management of the entire supply chain (production, logistics, etc.). As strategy for the survival and competition, digitalization has assumed a crucial role during the pandemic emergence by causing the reconfiguration of traditional chains and business models. Framed in these premises, the research analyses how digital technologies have innovated the sub-chains of bakery products and pasta within food industry with reference to customers' interactions, delivery and marketing during the COVID-19 pandemic emergence.

Moving from a critical literature review about the perspectives of digital technologies within the tradition of food industry, action research has been adopted to analyze in deep a case study of the start-up “ArteBianca Delivery” located in South Italy. Through this method, researchers have been deeply involved within the start-up to face the challenge of transforming the marketing and customer care into digital ones due to the COVID-19 restriction.

Findings provide empirical evidence about the reconfiguration of the traditional business model of a family firm in the food sector into a digital one with the start-up “ArteBianca Delivery”. The marketing, delivery, e-commerce and customer care components of the business models have been supported and enhanced through the adoption of digital tools, such as mobile applications and social technologies useful both for users and for a more urgent digitization of company.

Implications for practice can be identified into the pattern of digital transformation implemented as well as in the opportunity of replication and contextualization of the results to other companies looking for setting up a digital strategy.

Elements of original contribution can be identified into: (1) the exploration of digital transformation in food family firms caused by the pandemic emergence, (2) the contextualization of the digital transformation to the sub-chains of bakery and pasta and (3) the geographical location of the case.

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.