Search

With the increasing digitization and networking of medical data and personal health information, information security has become a critical factor in vendor selection. However, limited understanding exists regarding how information security influences vendor selection. Drawing from the attention‐based view (ABV), this study examines the potential impact of data breaches on hospitals' selection of electronic medical record system (EMRS) vendors. To test our hypotheses, we compile a unique dataset spanning 12 years of observations from US hospitals. Utilizing a coarsened exact matching (CEM) technique combined with a difference‐in‐differences (DiD) approach, our study shows that hospitals tend to replace their EMRS vendors after experiencing data breaches. Moreover, breached hospitals tend to prioritize information security in such a vendor replacement process by switching to star vendors and migrating towards a single‐sourcing configuration. Further post‐hoc analyses reveal that these impacts of data breaches are mitigated as the relationship between breached hospitals and vendors matures or when hospitals belong to large healthcare systems. Additionally, we find that the effects of data breaches are contingent on the scale of the breach and are short‐term in nature. This research underscores the significance of information security as a crucial consideration in vendor selection for both academia and practitioners. Highlights: We find that hospitals tend to change their electronic medical record system (EMRS) vendors following data breaches. During this replacement, they are more inclined to select star vendors and migrate towards a single‐sourcing configuration.We also find that the impacts of data breaches can be mitigated as the relationship between breached hospitals and vendors matures, or when hospitals are integrated into larger healthcare systems.Additionally, we find that the effects of data breaches are dependent on the scale of the breach and are typically short‐term in nature. [ABSTRACT FROM AUTHOR]

Various multi-criteria decision-making methods have been utilized in literature to address vendor selection challenges in various contexts. This study reflects on these decision-making techniques for vendor selection in the software ecosystem during software outsourcing. For the same, firstly, a requirement framework for the decision-making process for software vendor selection is proposed. Afterwards, five selected different multi-criteria decision-making techniques are compared and critically analysed against this requirement framework. This study supports software practitioners and decisionmakers by providing information about which decisionmaking method to choose, considering the trade-off between the benefits and drawbacks of adopting each method or using hybrid approaches by combining two or more techniques with fuzzy logic. It is found that decision-making techniques generally lack in modelling the problem itself and handling the conflicts and uncertainties that may prevail during the decisionmaking process. Furthermore, it has been identified that artificial neural networks (ANN) and analytic network process (ANP) can support the dependencies between alternatives and criteria while handling complex interactions in the software ecosystem. On the other hand, due to its comparative ease of use, analytic hierarchy process (AHP) can serve better in scenarios where decision-makers are not experienced, or past data to train the ANN or ANP models is unavailable. [ABSTRACT FROM AUTHOR]

Vendor selection is a strategic activity in order to support the achievement of the company`s success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, evaluation is needed to see which vendors match the company's criteria. The purpose of this study was to evaluate and select the proposed vendor in a decision support system using the AHP and TOPSIS approaches. The AHP method is used to determine the importance of the criteria, while the TOPSIS method is used to rank alternatives. The results show that Provider 1 has the highest score compared to other alternatives with a value of 0.852. Sensitivity analysis shows that the proposed AHP and TOPSIS methods are robust, suitable for this problem, and have a low rate of change.

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions. [ABSTRACT FROM AUTHOR]

Supply Chain Management (SCM) has gained the attention of business organizations as it affects corporate efficiency and competitive advantage. Component outsourcing is an important element of SCM practices and is considered to be a multi-criteria decision-making problem (MCDM) in vendor selection processes. However, decision-making on the selection of vendors is crucial, as the outsourcing program is influenced by multiple technical and commercial criteria of vendors. Developing an Excel-based Analytic Hierarchy Process (AHP) model for vendor selection therefore helps to simplify selection processes. In this paper, a Visual Basic Application (VBA) Excel-based AHP model was developed, which multiple criteria, sub-criteria and vendors and simultaneously compared with multiple main criteria, sub-criteria and alternatives for vendor selection. The model was validated with a case study of selecting a vendor for module components of body-in-white (BIW) in automotive industry as industrial application. As a result, vendor A is the best vendor since it has the highest overall score of 0.35206. Whereas vendor B is 0.239, vendor C is 0.126, vendor D is 0.146 and vendor E 0.135. In addition, the model is capable of resolving other MCDM problems. This paper provides resources such as AHP methodology, validating method and VBA code programming to readers in developing their own VBA Excel-based model. [ABSTRACT FROM AUTHOR]

With the increased digital usage, web visibility has become critically essential for organizations when catering to a larger audience. This visibility on the web is directly related to web searches on search engines which is often governed by search engine optimization techniques liked link building and link farming amongst others. The current study identifies metrics for segregating websites for the purpose of link building for search engine optimization as it is important to invest resources in the right website sources. These metrics are further used for detecting websites outliers for effective optimization and subsequent search engine marketing. Two case studies of knowledge management portals from different domains are used having 1682 and 1070 websites respectively for validation of the proposed approach. The study evolutionary intelligence by proposing a k-means chaotic firefly algorithm coupled with k-nearest neighbor outlier detection for solving the problem. Factors like Page Rank, Page Authority, Domain Authority, Alexa Rank, Social Shares, Google Index and Domain Age emerge significant in the process. Further, the proposed chaotic firefly variants are compared to K-Means integrated firefly algorithm, bat algorithm and cuckoo search algorithm for accuracy and convergence showing comparable accuracy. Findings indicate that the convergence speeds are higher for proposed chaotic firefly approach for tuning absorption and attractiveness coefficients resulting in faster search for optimal cluster centroids. The proposed approach contributes both theoretically and methodologically in the domain of vendor selection for identifying genuine websites for avoiding investment on untrustworthy websites. [ABSTRACT FROM AUTHOR]

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions.

Selecting the right educational vendors in the Metaverse, particularly those utilizing extended reality (XR), is crucial for creating an engaging and immersive learning environment. Careful vendor selection ensures the delivery of high-quality XR educational content, considering factors such as seamless integration with different XR devices and addressing associated challenges, which can affect the comfort and well-being of learners.This paper proposes a decision-making model to address the complex task of selecting educational vendors within the Metaverse with a focus on extended reality (XR). Recognizing the challenges inherent in such selections, this work presents a consensus-based measurement of alternatives and ranking according to compromise solution (MARCOS) model, incorporating single-valued neutrosophic (SVN) information and SVN Dombi-Archimedean weighted aggregation operators. Attribute weights crucial for decision-making are determined through an optimization tool employing cross-entropy measures with partial weight information. The evaluation encompasses nine criteria, including cost, security, privacy, pedagogical alignment, configuration, maintenance, level of control, output tools, degree of immersion, availability of XR learning contents, and time. Four leading vendors including Victory XR, ARuVR, zSpace, and Varjo Technologies are assessed, with zSpace emerging as the most favorable alternative with the highest overall utility value of 0.6889. The paper further conducts sensitivity analysis and performance comparisons to establish the robustness of the ranking results, providing a comprehensive methodology for educational stakeholders addressing the evolving dynamics of the Metaverse. • A decision-support model is suggested for ranking XR educational vendors under metaverse. • Some robust Dombi-Archimedean weighted aggregation operators are presented under SVN context. • A consensus based SVN-MARCOS methodology is proposed with partial weights information of attributes. [ABSTRACT FROM AUTHOR]