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This study aims to develop a software risk prioritization model using analytic hierarchy process (AHP) and further validate the usability attributes of the model in prioritizing operational, technical, technological, strategic and environmental software risks.

Questionnaire was used to collect data from software practitioners to evaluate the usability attributes of the AHP-software risk prioritization model. Accordingly, partial least square-structural equation modeling was used to analyze the collected data.

Results reveal that the developed AHP-software risk prioritization model is efficient and effective in facilitating software risk factor prioritization. In addition, results suggest that the experts are satisfied with the learnability, accessibility and navigation capability of the model. Besides, results indicate that the model provides a useable interface and system design for content availability of information needed by software practitioners in evaluating and prioritizing operational, technical, technological, strategic and environmental risk. Furthermore, results show that the experts intend to adopt the model to prioritize identified software risk in their firm.

Methodologically, the developed AHP-software risk prioritization model is faced with issues such as inconsistency in judgments, weakness of confronting ambiguities and uncertainties of high complexity. Empirically, data were collected from software practitioners in Malaysia to validate the AHP-software risk prioritization model. Hence, results from this study cannot be generalized to other software practitioners in different countries.

This study developed a software risk prioritization model to evaluate and prioritize software risks that occur in software organizations by deploying AHP to carryout risk factor priority selection. Moreover, the model provides risk knowledge as guidelines for evaluating software risks in software organizations.

The developed AHP-software risk prioritization model computes risk prioritization factor priority selection and further supports software practitioners and evaluates risks and associated risk factors. Besides, this study develops an instrument that can be used in project risk management to validate the usability attributes of software risk approaches.

This research designs use case and class diagram to show how the AHP-software risk prioritization model evaluates and prioritizes software risks factors by using risk evaluation questions. Additionally, the AHP-software risk prioritization model computes, evaluates and prioritizes software risk factors using risk factor priority selection for software project management.

The convenience of online shopping enables the manufacturer to develop direct channels. To counter manufacturer encroachment, the retailer tends to provide presale service to attract more customers. Meanwhile, the service provided by the retailer also has a positive impact on the manufacturer's sale volume, which is usually called the showrooming effect or free-ride. The purpose of this paper is to explore the dynamic game of pricing and service strategy in a dual-channel supply chain with risk attitudes and free-ride.

This paper considers the risk attitude, characterized by mean-variance theory. First, the optimal pricing and service strategy of two static models under two scenarios are derived. Second, dynamic games are then considered to explore the evolution of the decisions. The classical optimization method is used to solve the problem, and numerical experiments are done to analyze the complex characteristic of the system.

The result shows that the retailer is willing to provide a higher level of service if his risk preference is higher. The offline retail price and online retail price are positively related to the retailer's risk preference. Besides, the free-ride behavior can reduce the offline retail price and the level of service provided by the retailer. Furthermore, the study indicates that the system is more likely to enter chaos if the retailer's risk preference is higher. Additionally, consumers' service sensitivity and cost coefficient affect the stability of the system.

The study provides a different perspective on supply chain management considering risk attitudes and free-ride The findings of the study can offer theoretical and practical guidance for enterprises to choose adjustment measures according to their risk preference.

The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.

The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.

The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.

The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.

This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

As the novel coronavirus 2019 (COVID-19) impacts the world, software practitioners are collaboratively working remotely from home. The pandemic has disrupted software practitioners’ productivity forcing changes to agile methodology adopted by software practitioners in software organizations. Therefore, this study aims to provide implication on the issues and recommendations for improving software practitioners’ productivity and also examine the impact of the COVID-19 pandemic on agile software development.

This paper adopts a narrative literature review to provide early assessment based on secondary data from the literature and available document reports from studies published from 2019 to 2022 to explore software practitioners’ productivity and agile software development during the working from home directive amidst the COVID-19 pandemic. A total of 60 sources which met the inclusion criteria were used to provide preliminary evidence grounded on secondary data from the literature. Descriptive analysis was used to provide qualitative findings from the literature.

Findings from this study present the significance of working from home directive on agile software development and software practitioners’ productivity. More importantly, findings from the secondary data shed light on software practitioners’ productivity adopting agile software development amidst the COVID-19 pandemic. Additionally, the findings present virtual collaborative platforms used by software practitioners, technical and social barriers of agile software development during the pandemic and recommendations for remote agile software development.

This study explores the significance of working from home directive on software practitioners’ productivity during COVID-19 pandemic and further investigates how are software practitioners’ productivity adopting agile software development practices amidst the COVID-19 pandemic. Besides, this study discusses the challenges software practitioners currently face and offers some strategies to bridge the gaps in agile software development to help software practitioners, system developers, software managers and software organizations adapt to the changes caused by the pandemic.

In the past, a plethora of studies has investigated the organizational and individual outcomes of high-performance work systems (HPWS). However, less is known about the mechanism through which HPWS impacts employees’ behavior, particularly intrapreneurial behavior (IB). Drawing on the social exchange theory, this study aims to fill this gap by investigating the mediation effect of perceived organizational support (POS) on HPWS-IB linkages.

Data were collected with the help of structured questionnaires from employees working in service industries such as banking and insurance.

HPWS was conceptualized as a higher-order measurement model that includes four lower-order dimensions, namely, information sharing, decision-making participation, job security, training and development. Partial least squares structural equation modeling technique was used to test the hypothesized relationships. Results showed a positive and significant effect of HPWS on IB. Moreover, POS significantly mediated the HPWS-IB link.

Despite an increasing number of studies on the role of human resource management (HRM) practices in enhancing innovation and creativity, there has not been enough research on how HPWS affects IB at the individual level in the presence of POS. Thus, this research is the first of its kind to investigate the mediating role of POS in HPWS-IB linkages in the Malaysian context.

The purpose of this study is to develop a model to understand the relationships among technology, organizational and environmental (TOE) contexts, intention to adopt cloud computing (IACC) and actual usage of cloud computing (AUCC) in small and medium enterprises (SMEs) in Malaysia as a developing country. More specifically, this paper seeks to explore the mediation effect of IACC on the relationship between TOE context and AUCC.

A positivist research approach was selected for this study. Drawing largely upon the TOE framework, this study uses survey data from 209 Malaysian SMEs. Structural equation modelling (SEM) based on partial least squares (PLS) was used to assess the structural relations of the research model.

The results of the structural model show that data security, technology readiness, top management support, competitive pressure and innovativeness are the most significant factors in predicting the adoption of cloud computing in Malaysian SMEs. Further, the results indicate that intention to adopt cloud computing can play a mediating role between TOE factors and the actual usage of cloud computing.

The focus upon Malaysian SMEs may diminish the generalizability of the findings. This study provides profound insight into the management and foundation of cloud computing, different types of cloud services and deployment models that could facilitate the management of enterprise strategic resources and contribute to the performance improvement. This study also provides another important implication for practitioners regarding the absolute necessity of value drivers’ identification within enterprise and understand the causal relationships, which are vital in driving those values.

This study provides several practical guidance for practitioners in deploying cloud services which are most suitable option for their specific technology requirement in their enterprise to enjoy the full benefits of their intangible assets. Another significant implication of this study lies in the fact that it may require a different emphasis on nature and adoption design when there is a higher level of stress on technology-related and cloud computing resources.

This study contributes to the extant literature by developing an integrative model to identify how a wide set of contextual factors can determine the intention to adopt cloud computing and, in turn, influence the actual usage of cloud computing in SMEs in Malaysia as a developing country.

Data breaches in the US healthcare sector have more than tripled in the last decade across all states. However, to this day, no established framework ranks all states from most to least at risk for healthcare data breaches. This gap has led to a lack of proper risk identification and understanding of cyber environments at state levels.

Based on the security action cycle, the National Institute of Standards and Technology (NIST) cybersecurity framework, the risk-planning model, and the multicriteria decision-making (MCDM) literature, the paper offers an integrated multicriteria framework for prioritization in cybersecurity to address this lack and other prioritization issues in risk management in the field. The study used historical breach data between 2015 and 2021.

The findings showed that California, Texas, New York, Florida, Indiana, Pennsylvania, Massachusetts, Minnesota, Ohio, and Georgia are the states most at risk for healthcare data breaches.

The findings highlight each US state faces a different level of healthcare risk. The findings are informative for patients, crucial for privacy officers in understanding the nuances of their risk environment, and important for policy-makers who must grasp the grave disconnect between existing issues and legislative practices. Furthermore, the study suggests an association between positioning state risk and such factors as population and wealth, both avenues for future research.

Theoretically, the paper offers an integrated framework, whose basis in established security models in both academia and industry practice enables utilizing it in various prioritization scenarios in the field of cybersecurity. It further emphasizes the importance of risk identification and brings attention to different healthcare cybersecurity environments among the different US states.

Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment.

We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking.

The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers.

The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information.

This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Limited funding to maintain and preserve short‐line railroad (SLRR) bridge infrastructure requires that important priority decisions be made on an annual basis. The compartmentalized, dispersed, and diverse nature of many SLRR owners and operators is such that there is a need for a coordinated and centralized effort to evaluate the state‐wide system as a whole, to ensure the most effective overall resource allocation and also identify assets that either outperform predictions or consume disproportionate levels of resources for maintenance and operation, allowing for review of design and construction practices. The purpose of this paper is to examine the state of the art for railroad bridge population management and resource allocation decisions and to develop a state‐wide SLRR bridge prioritization methodology, to be used as a tool by a state agency to assist in allocating limited public funding for bridge maintenance, rehabilitation and replacement activities.

A literature review examining the state of the art of railroad bridge population management and resource allocation decisions was conducted, which provided the foundation for the development of a bridge prioritization algorithm. A state‐wide survey was conducted to develop a bridge database. A detailed evaluation of a statistically significant sample of bridges was conducted, to determine the structural and maintenance needs and preservation status of sub‐populations. The research team developed methodologies, applicable to the entire population, to develop a ranking of bridge preservation candidates.

A risk‐based prioritization algorithm is proposed to assign a relative risk score to each bridge in the population. The algorithm provides a management tool for making more effective maintenance and preservation decisions. Additionally, the bridge database allows managers to examine sub‐populations according to structural parameters to evaluate performance.

The revisable, modular framework of the prioritization algorithm provides a simple, effective and versatile tool for asset management and evaluation. The present proposal of this new prioritization methodology for SLRR bridges is a valuable tool for agencies faced with making rational decisions with limited information. Such a methodology does not currently exist in the literature and is of significant interest to short‐line owners/operators and state transportation agencies.