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The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.

This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.

For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.

The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

High performance of new hires is of imminent interest to organizations in the hospitality sector. Yet, limited studies have focused on the relevant traits of new hires that improve on-job performance. This study aims to identify and understand a few critical traits that predict high performance across multilevel roles within the hospitality sector.

Drawing on the human capital theory, through a time-lagged field study spanning 16 months, this study used multisource data for 540 active job opportunities and 205 new hires within the hospitality industry. This study used partial least squares-based structural equation modeling and analyzed the various traits that predict high on-job performance.

This study finds that humility is a significant predictor of job performance and wholly mediates the effect of interpersonal understanding, self-confidence and flexibility on new hires’ performance.

This study enhances talent management research for the hospitality sector by determining the critical traits of new hires that can predict superior on-job performance.

This paper examines the impact of founder ownership concentration (FOC) on risk disclosures. It further investigates the moderating role of risk governance in the association between FOC and risk disclosures.

We use data from the top 200 Indian listed firms as our sample and rely on ordinary least squares (OLS) for our results. In addition, we use the propensity score matching, Heckman selection model and instrumental variable estimates for robustness checks.

We find that FOC decreases the risk disclosures. However, the effectiveness of risk management committee composition (risk governance) mitigates the negative influence of FOC on risk disclosures.

The paper is built on the agency theory. Based on the agency theory, the ownership concentration has two implications: first, it reduces the conflicts between managers and shareholders. Here, the managers act in favour of shareholders and therefore, brings more risk disclosers. Second, it invites conflicts between controlling and minority shareholders. The study is, therefore, interesting to see the cost and benefits of FOC on risk disclosures.

The study has practical implications for the regulatory bodies to encourage risk disclosures and benefit the outsiders of the firm. It also has implications for the companies to see the benefits of risk management committee as improved risk governance.

It contributes to the literature of risk disclosures and risk governance in emerging economies. It is the first study to investigate the role of risk governance in mitigating the adverse effects of founder’s ownership on risk disclosures in developing economies. It also contributes to the theory of agency cost and information asymmetry.

This study aims to explore supply chain resilience (SCR) and provides a unique resilience index. The work measures the resilience status of 37 organizations across 22 industries and provides insight into accessing the supply chain (SC) vulnerability in an uncertain environment.

This study involves measuring the resilience status of 37 organizations across 22 industries based on a subjective decision-making approach using fuzzy logic. Experts from industries rated the importance and level of implementation of 33 attributes of SCR, which are used to develop a fuzzy index of implementation that explains the resilience status of organizations.

A novel coexistent resilience index is computed based on mutualism to exhibit the proportion of contribution or learning of each attribute of an organization in an industry. The research will enhance the response plans and formation of strategic alliances for mutual coexistence by industry.

Evidence-based interpretations and suggestions are provided for each industry to enhance resilience through coexistence.

The work uniquely contributes to academic literature and SC strategy. The novel coexistent resilience index is computed based on mutualism, facilitating researchers to access SC resiliency.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

Websites, search engines, recommender systems, artificial intelligence and digital libraries have the potential to support serendipity for unexpected interaction with information and ideas which would lead to favored information discoveries. This paper aims to explore the current state of research into serendipity particularly related to information encountering.

This study provides bibliometric review of 166 studies on serendipity extracted from the Web of Science. Two bibliometric analysis tools HisCite and RStudio (Biblioshiny) are used on 30 years of data. Citation counts and bibliographic records of the papers are assessed using HisCite. Moreover, visualization of prominent sources, countries, keywords and the collaborative networks of authors and institutions are assessed using RStudio (Biblioshiny) software. A total of 166 papers on serendipity were found from the period 1989 to 2022, and the most influential authors, articles, journals, institutions and countries among these were determined.

The highest numbers of 11 papers were published in the year 2019. Makri and Erdelez are the most influential authors for contributing studies on serendipity. “Journal of Documentation” is the top-ranking journal. University College London is the prominent affiliation contributing highest number of studies on serendipity. The UK and the USA are the prominent nations contributing highest number of research. Authorship pattern for research on serendipity reveals involvement of single author in majority of the studies. OA Green model is the most preferred model for archiving of research articles by the authors who worked on serendipity. In addition, majority of the research outputs have received a citation ranging from 0 to 50.

To the best of the authors’ knowledge, this paper may be the first bibliometric analysis on serendipity research using bibliometric tools in library and information science studies. The paper would definitely open new avenues for other serendipity researchers.

Despite the potential of virtual fitting rooms (VFRs) to enhance the consumer experience, their adoption is in the preliminary stages. Little is known about inherent reasons why consumers would adopt VFRs. As consumers' attributional processes can be influenced by their enduring chronic traits, this study aims to investigate the influence of chronic regulatory focus on consumers' VFR adoptions via consumers' perceptions of value provided by VFRs. Additionally, the mediating effects of perceived functional and experiential values were examined. Further, the moderating effect of prior VFR experience was tested to allow for variations in consumer experiences.

Data were collected via an online survey of 480 consumers who have at least heard of VFRs via convenience sampling. Established measures were utilized to develop the survey questionnaire. Data were analysed using structural equation modelling to test the main model with mediation effects as well as multi-group comparisons to test the moderating effect.

Empirical results revealed that respective chronic regulatory foci, as preconceived factors that drive consumers' differences in processing, exerted significant influences on consumers' perceptions of VFRs, which, in turn, positively influenced their adoption intention. Also, perceived values mediated the relationship between regulatory foci and consumers' adoption intention. Further, prior VFR experience moderated the relationship between regulatory focus and perceived value.

The paper empirically tested the importance of chronic regulatory foci in understanding consumers' cognitive and affective attributional processes, explaining inherent psychological reasons why consumers would (not) adopt VFRs.