In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

This paper utilizes an advanced Network Data Envelopment Analysis (DEA) model to examine the impact of mobile payment on the efficiency of Taiwan banking industry. Inheriting the literature, we separate the banking operation process into two stages, namely profitability and marketability. Mobile payment is then considered as the core factor in the second stage. Our paper discovers network DEA model can effectively enhance the analysis of banking industry’s efficiency, and mobile payment has a notable impact on Taiwan banking industry. Regarding the profitability stage, there is only one efficient bank in 2019 and 2022, respectively. These banks also perform better in terms of “mobile payment production”. In the marketability stage, there is also only one bank in 2021 and one bank in 2022, that can reach to unique efficiency score. This indicates many banks attempt to increase earnings per share through investing in mobile payment services. However, the achievement still needs more wait. This leads to the fact that no bank can reach the ultimate overall efficiency. Within our sample, we also find that regarding promoting mobile payment services, Private Banks outperform Government Banks.

Despite the proliferation of corporate social responsibility (CSR) studies, it is accruing academic interest since there still remains a lot to be further explored. The purpose of the study is to examine whether/how CSR perception affect employee/intern thriving at work and its mediator through perceived external prestige in the hospitality industry. Data from 501 hospitality industry employees and interns in China were collected using a quantitative survey consisting of 35 questions. Statistical findings showed that CSR perception and thriving at work were positively related. Additionally, perceived external prestige partially mediated the connection between CSR perception and thriving at work. Furthermore, the study found that hotel interns generally exhibited lower levels of CSR perception and thriving at work compared with frontline or managerial staff. The study underscores the importance of collaborative efforts between hotel practitioners and university educators to enhance CSR perception and promote thriving among hotel interns. By prioritizing the improvement of CSR perception and thriving at work, the hotel sector can potentially mitigate workforce shortages and reduce high turnover rates.

Weather is almost inevitable and plays an important role in determining the duration of construction projects. The construction industry ultimately thrives upon the physical input, put in by the labours. The majority of the construction projects are executed in the outdoor environment and hence face a high impact of weather conditions. This study therefore evaluated the influence of weather conditions on construction workers’ productivity in Jos, Plateau State and proceeded to make recommendations geared towards the improvement of construction workers’ productivity in Jos. The study was conducted through the direct observation method. Three hundred and ninety-six (396) works were purposively sampled in selected working sites. The outcome shows that during dry weather, there was considerably less significant productivity of manual excavation. In contrast, a large increase in blockwork and plasterwork productivity was observed with a percentage difference of 33%, 56.3% and 61%, respectively. On the other hand, during wet weather conditions, the labour productivity for manual excavation increases, whereas it decreases for block work and plasterwork with percentages difference of 58%, 40% and 47%, respectively. Besides, relative humidity and wind speed have no impact on labours’ productivity in dry and wet weather. Besides, the temperature has the most decisive impact on workers’ productivity. Moreover, wind speed and humidity have a lower influence on workers’ productivity. The construction industry stakeholder in Jos, Nigeria, would benefit from this study’s recommendations for reducing the influence of weather on the building sector. Besides, the output can be extended to other regions having similar characteristics.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.