In the dynamic landscape of digital enterprises, cybersecurity has emerged as a critical determinant of organizational effectiveness. This study delves into the intricate realm of cybersecurity investment within ASEAN organizations, exploring the key facets that drive decision-making in this domain. Using a quantitative approach through structural equation modeling (SEM), we conducted an in-depth analysis based on a sample of 419 enterprises meeting cybersecurity criteria. Our findings reveal that cybersecurity strategy, financial considerations, and institutional and regulatory conditions are the primary factors influencing cybersecurity investments in the ASEAN region. In particular, financial resources emerged as the most critical determinant, underscoring the importance of adequate funding to address evolving cyber threats. Furthermore, our study highlights the crucial role of institutional and regulatory frameworks in shaping investment behavior, indicating a heightened awareness among firms regarding compliance with legal requirements. By unpacking these dynamics, our research provides deep insights into the intricate interplay of factors shaping cybersecurity investments in ASEAN organizations. This study contributes to the discourse by emphasizing the imperative nature of understanding the impact of risk aversion, organizational structures, and long-term practices on cybersecurity resilience. The implications of our findings extend to policy making, innovation, and future research directions in the cybersecurity domain, offering valuable insights to improve cybersecurity preparedness and resilience against evolving cyber threats.

Industry 4.0 encourages industries to digitise the manufacturing system to facilitate human-robot collaboration (HRC) to foster efficiency, agility and resilience. This cutting-edge technology strikes a balance between fully automated and manual operations to maximise the benefits of both humans and assistant robots (known as cobots) working together on complicated and prone-to-hazardous tasks in a collaborative manner in an assembly system. However, the introduction of HRC poses a significant challenge for assembly line balancing since, besides typical assigning tasks to workstations, the other two important decisions must also be made regarding equipping workstations with appropriate cobots as well as scheduling collaborative tasks for workers and cobots. In this article, the cobot assembly line balancing problem (CoALBP), which just initially emerged a few years ago, is thoroughly reviewed. The 4M1E (i.e., man, machine, material, method and environment) framework is applied for categorising the problem to make the review process more effective. All of the articles reviewed are compared, and their key distinct features are summarised. Finally, guidelines for additional studies on the CoALBP are offered.

Managing blood inventory is challenging due to the perishable and unstable nature of the product needed for transfusions in healthcare facilities. In this paper, we consider a periodic review blood inventory model with two priority demand classes, namely emergency and regular patients. We propose a dynamic programming model for determining the optimal ordering policy at the hospital given the uncertainty regarding received donated blood units. The optimal policy deals with placing orders for blood units that will expire within a fixed period. The objective is to minimize total expected costs within a planning horizon while maintaining a specified expected service level. Our model considers uncertain demands and donated blood units with discrete probability following known distributions. A tabu search algorithm is developed for large-scale problems. The performance of these ordering policies is compared against the optimal fixed order quantity and the order up-to-level policies using real-life data. The numerical results show the benefit of our model over the optimal fixed order quantity and the order up-to-level policies. We measure the total expected cost and the expected service level obtained from the optimal and near-optimal policies and provide a sensitivity analysis on parameters of interest.

Given the rapid global economic development and continuous technical advancements, a key strategy for companies trying to improve their competitive edge and accomplish sustainable development is digital transformation. This study's goal was to investigate how the supply chain capabilities of Yunnan Province's fresh-cut flower business are affected by digital transformation. To collect data, the study employed a survey methodology based on a questionnaire. Ultimately, 402 flower businesses in Yunnan Province produced valid du-plicates. PLS-SEM was utilized in the study to evaluate the data, and the results showed that digital transformation greatly improved the enterprises' capacity for both innovation and supply chain management; innovation capabilities positively impacted supply chain capabilities and acted as a mediator in the relationship between supply chain capabilities and digital transformation. Furthermore, organizational readiness largely exhibits a positive moderating effect on the relationship between digital transformation and supply chain capabilities, whereas technology readiness not only fosters but also reinforces the relationship between digital transformation and innovation capabilities. These results offer an effective reference suggestion for fresh-cut flower companies in Yunnan Province and nationwide on how to enhance supply chain capabilities through digital transformation to improve their competitive advantages.

The goal of this research is to test the mediating effect of product symbolism on the relationship between conspicuous consumption and social identity formation in the branded clothing sector of young adults. Young adults between the ages of 20-35 who wear branded clothes in Sri Lanka were considered the target population. The data was collected through a survey to find answers to the identified research questions through a quantitative approach. The Snowball sampling method was used as the sampling method for this study. The Sobel Test was carried out using an online calculator to measure the statistical significance of the mediation. The results showed that the effect of conspicuous consumption on social identity formation mediated through product symbolism could be judged as statistically significant. The findings can help with market segmentation and brand positioning. Knowing these symbolic value attachments to branded clothes, marketers can create their marketing mix strategies to provide solutions for self-image enhancement and sustainable competitive advantage from both consumer and company points of view.

Vehicle Routing Problem (VRP) is important in the transportation and logistics industries. Vehicle Routing Problem with Time Window (VRPTW) is a kind of VRP with the additional time windows constraint in the model and is classified as an NP-hard problem. In this study, we proposed Stas crossover in Genetic Algorithm (GA) to solve VRPTW by developing the problem with K-mean clustering. The experiments use the standard Solomon’s benchmark problem instances for VRPTW. The results with K-mean clustering are shown to perform better for minimum distance and average distance than without K-mean clustering. In the case of location and dispersion characteristics of the customer, the paths with K-mean clustering are arranged into groups and are orderly, but the paths without K-mean clustering are disordered. After that, this paper shows the comparison of the crossover operator performance on instances of Solomon benchmark, and appropriate crossover operators are recommended for each type of problem. The results of the proposed algorithm are better than the best-known solutions from the previous studies for some instances. Moreover, our proposed research will serve as a guideline for a real-world case study.

The research investigated the dynamics affecting the implementation of target costing in startups in Thailand. Startups face turbulent and competitive environments, lack of market demand and regulatory hurdles, which require effective cost management strategies. The study used quantitative methodology to evaluate the effect of various factors - perceived environmental uncertainty, competitor influence, product diversity, firm revenue, and business strategy for the adoption of target costing. Primary data from a sample of 314 respondents were used. The constructs validity and reliability were analyzed using Confirmatory Factor Analysis while Multiple regression analysis was used to evaluate the study hypotheses. The findings indicated that adoption of target costing was positively and significantly influenced by perceived environmental uncertainty, competitor influence, firm revenue, and business strategy, while product diversity has an insignificant influence. The study recommended that startup managers should consider using complex cost management techniques, as a means of acquiring competitive market advantage, strategic alignment of cost management and using competitors as a benchmark to evaluate their market competitiveness.

With the rapid advancement of blockchain technology, SMEs face an opportunity to leverage decentralized ledger systems to address longstanding challenges related to financial transparency. This study aims to assess the implications of blockchain adoption for SMEs operating in the Jordanian context, focusing on its potential to improve accountability, trust, and efficiency in financial operations. Drawing on quantitative research methods, this paper examines the current state of financial transparency using a structural equation modeling approach of 215 surveys. The findings indicated that there is a positive impact of block-chain technology on enhancing financial transparency. The findings of this research contribute to both academic understanding and practical implications for policymakers, regulators, and SMEs in Jordan seeking to enhance financial transparency through blockchain technology. By shedding light on the positive impact of blockchain on financial transparency in the Jordanian SME sector, this paper aims to inform strategic decision-making and stimulate further research in this emerging field. Ultimately, it underscores the transformative potential of block-chain technology in promoting accountability, trust, and eco-nomic development among SMEs in Jordan and beyond.

There is a global trend towards adopting green energy, with solar energy being the primary source derived from solar panel technologies. Solar panels can generate enough power for general and household use. However, to effectively function and serve their purpose, they require regular cleaning and effective maintenance, and robotic cleaning is among the current applicable technologies. This research aims to determine the intention of using solar panel cleaning robots in Thailand for individual solar panel users. The study was hinged on the extended C-TAM-TPB model. The quantitative survey study design was employed using primary data collected from individual solar panel users in their households. 419 respondents were used to collect the data. The C-TAM-TPB model proposed using reliability, validity, and model fitness which employed confirmatory factor analysis (CFA). They adopted structural equation modeling (SEM) in the evaluation of the variables' relationships and study hypotheses. Subjective norms and trust in technology, individual control perception, and awareness of renewable energy significantly and positively affected behavioral intention to use solar panel cleaning robots as indicated by the study. Trust in technology, awareness of renewable energy, and environmental concerns were found to be pivotal mediators to the attitude effect on individual users' intention to act in using solar panel cleaning robots. The authors recommend that to improve the adoption of solar panel cleaning robots; the concerned stakeholders should consider, firstly, enhancing trust in the technology of these robots, which is crucial, focusing on aspects like reliability, privacy, security, and reputation. Secondly, considering the influence of subjective norms, including perceptions from family, friends, colleagues, and experts, is essential. Perceived behavioral control should also be a focal point, encompassing self-efficacy, resources, and complexity. Moreover, increasing awareness of renewable energy and environmental benefits is vital to encourage individual adoption. The research also recommended that to encourage the adoption and use of solar panel cleaning robots, the aspects that should be emphasized include subjective norm, perceived behavior control, trust in technology, and awareness of renewable energy.

Educational institutions generate a large amount of digital data in their daily operations, which is stored in servers, forming a substantial educational data set. Extracting valuable information through practical data analysis has become a critical problem that needs to be solved urgently. Students' examination results are an essential basis for evaluating their learning status, which reflects the effect of school education to some extent. Therefore, we propose a model based on the BP network and Pandas to construct a prediction model for Pandas' performance in the first year and their successful graduation to explore the potential relationship between Pandas' performance in the freshman year and graduation, thus realizing the principle of early guidance and improvement of teaching quality. Through the random prediction experiment of 9,424 scores data of 304 students in 2017 and 2018 majoring in network engineering at a university, the accuracy rate is 96.71% after the experimental data analysis and verification, which has proved that there is a potential correlation between the students' first-year course scores and graduation. Meanwhile, the improved BP network proposed in the present research exhibits reasonable practicability and extensibility in the college student achievement prediction model.