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This paper aims to make it convenient for those who have only just begun their research into Community Question Answering (CQA) expert recommendation, and for those who are already concerned with this issue, to ease the extension of our understanding with future research.

In this paper, keywords such as “CQA”, “Social Question Answering”, “expert recommendation”, “question routing” and “expert finding” are used to search major digital libraries. The final sample includes a list of 83 relevant articles authored in academia as well as industry that have been published from January 1, 2008 to March 1, 2019.

This study proposes a comprehensive framework to categorize extant studies into three broad areas of CQA expert recommendation research: understanding profile modeling, recommendation approaches and recommendation system impacts.

This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, it was found that conflicting and contradictory research results and research gaps in the existing research, and then put forward the urgent research topics.

Tourism is one of the upcoming service industry in India with high potentials for future growth, particularly in rural areas. Many potential barriers are affecting the growth of tourism in rural India. Therefore, it is essential to explore and prioritize the barriers to tourism growth in rural India.

Qualitative and quantitative responses from “16” experts related to tourism and hospitality management from central India are collected for this study. An integrated Multi-Criteria Decision Making (MCDM) based framework is adopted to identify and relate significant barriers to tourism growth in India.

The result of the study identified many significant barriers and their importance to tourism growth in rural India.

The findings of this study add to the knowledge base of tourism research in line with the previous literature. This study offers an in-depth understanding of barriers focusing on rural tourism growth and devising both the plan of action and the suggestive measures in dealing with rural tourism.

The study provides a robust framework by integrating Interpretive Structural Modelling(ISM) and Decision Making Trial and Evaluation Laboratory (DEMATEL) to explore and prioritizing the critical barriers to rural tourism growth in India. The results of this study can help the decision-maker to fundamentally improve the economy of India through the growth of rural tourism.

This paper aims to identify the interaction of different intervention strategies implemented in Malaysia towards flattening the curve of COVID-19 cases. Since the outbreak of COVID-19, many approaches were adopted and implemented by the Malaysian government. Some strategies gained quick wins but with negative unintended consequences after execution, whereas other strategies were slow to take effect. Learning from the previous strategies is pivotal to avoid repeating mistakes.

This paper presents the cause, effect of and connection among the implemented COVID-19 intervention strategies using systems thinking through the development of a causal loop diagram. It enables the visualisation of how each implemented strategy interacted with each other and collectively decreased or increased the spread of COVID-19.

The results of this study suggested that it is not only essential to control the spread of COVID-19, but also to prevent the transmission of the virus. The Malaysian experience has demonstrated that both control and preventive strategies need to be in a state of equilibrium. Focusing only on one spectrum will throw off the balance, leaving COVID-19 infection to escalate rapidly.

The developed feedback loops provided policy makers with the understanding of the merits, pitfalls and dynamics of prior implemented intervention strategies before devising other effective intervention strategies to defuse the spread of COVID-19 and prepare the nation for recovery.

Over the past few decades, university reforms in line with management and enterprise ideals have been well documented. Changes in the ideals underlying the missions of universities have led to changes in their modes of governing and organizing, which in turn drive further transformation of their missions. One set of reforms in Swedish higher education has been the dissolution of collegial bodies and procedures. At the same time, in recent years, we have witnessed an increased interest in collegiality and a reintroduction of collegial bodies and procedures. New translations of collegiality appear not only in how universities are organized, but also in other core aspects of research and higher education. We review examples of peer reviewing, research assessment, and direct recruitment of professors and ask: Can these new translations of collegiality be understood as a revitalization of collegiality, or is it – to draw a parallel with greenwashing – rather a matter of collegiality-washing?

This study focuses on managers' perceptions of employees' communicative role in social media, and explores the changes in the contractual nature of employment relations in mediatized workplaces in which the boundaries of professional and private life are becoming more fluid.

A qualitative approach was employed to explore this relatively new phenomenon. The data, comprising 24 interviews with managers responsible for corporate communication and human resources in knowledge-intensive organizations, was thematically analysed.

The analysis shows that employees' work-related social media use creates new types of exchanges and dependencies between an organization and individual employees, which relate to employees' representation, knowledge and networks.

The study is among the first to examine the exchanges and dependencies in an employment relationship that emerge from increased use of social media for professional purposes.

The purpose of this study is to identify the core responsible leadership (RL) practices that are most relevant to small- and medium-sized enterprises (SME) restaurants. Furthermore, the authors adapt scales to measure these practices and conduct a pilot study to evaluate their impact on business performance in such establishments.

Exploratory sequential mixed methods are used to fulfill the research aims. In the first phase, a set of definitions and practices associated with RL are derived from a systematic literature review. Second, a projective method of data collection is applied, involving a panel of 16 experts. Third, a fuzzy cognitive map is developed, which captures the responses of 40 owners or general managers of SME restaurants.

Twenty-five practices are identified from the systematic literature review. The results show the five leadership practices that match the order of importance assigned by the experts: societal orientation, ethics, stakeholder involvement, power-sharing and environmental orientation. The relevance of those five practices is validated to explain SME restaurants’ financial performance and innovation performance.

Innovation is the key to advancing business sustainability and resilience, and the results identify the specific RL practices that enable improvements to be made in innovation performance among SME restaurants.

This paper identifies the RL practices that are particularly relevant to the tourism field (specifically, the restaurant industry), offers measurement scales for those practices and provides empirical evidence of the relationship between these RL practices and business performance in SME restaurants.

The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented.

The approach is based on measurement theory, following Tversky’s well-known paradigm.

A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values.

Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.

To continue to stay relevant in the era of Industry Revolution 4.0 (IR4.0) alongside the unprecedented disruption of COVID-19, the importance of lifelong learning is indisputable though this concept has existed for decades. In this context, open and distance learning (ODL) institutions are urged to re-think and re-design their online learning support systems that inculcate self-regulated lifelong learning (SR3Ls) in their learners to be adaptable and resilient for the post-pandemic economy. The purpose of this paper is to develop a SR3Ls model, namely SR3Ls model by utilising the collective opinions of a panel of experts to determine the key domains and attributes.

A 2-round Delphi consensus study was conducted with 39 experts from five countries. The mean, standard deviation (SD), inter-quartile range (IQR) and the ratio of experts assigned score of 4 or greater were used as the basis of consensus assessment with criteria set at mean = 3.0, SD = 1.5, IQR = 1, ratio on score 4 or greater at = 75%. The questionnaire consisted of 5-point Likert-type scale rating the importance level of each attribute combined with open-ended questions.

This paper presented the findings of the first round of Delphi consensus study. For the first round, the experts were asked to evaluate 31 key attributes of SR3Ls model under five domains. The findings revealed that there were five key attributes to be eliminated from the list, while there were seven attributes identified as the key attributes with highest consensus. There were additional attributes suggested by the Delphi panel to be added in the second round of evaluation.

This international consensus-based SR3Ls model serves as an important benchmark for ODL institutions across the regions in developing meaningful and relevant online learning support systems for their learners to adopt SR3Ls attributes in order to meet the dynamic market demands.

The purpose of this paper is to develop a structural relationship model to study the relationship between causes and effects of poor communication and information exchange in construction projects using Smart-PLS.

The first method of this research is to identify the causes and effects factors of poor communication in construction projects from the extant of literature. The data used to develop the model was collected using a questionnaire survey, which targeted construction practitioners in the Malaysian construction industry. A five-point Likert type scale was used to rate the significance of the factors. The factors were classified under their relevant construct/group using exploratory factor analysis. A hypothetical model was developed and then transformed into Smart-PLS in which the hypothetical model suggested that each group of the cause factors has a direct impact on the effect groups. The hypothesis was tested using t-values and p-values. The model was assessed for its inner and outer components and achieved the threshold criterion. Further, the model was verified by engaging 14 construction experts to verify its applicability in the construction project setting.

The study developed a structural equation model to clarify the relationships between causes and effects of poor communication in construction projects. The model explained the degree of relationships among causes and effects of poor communication in construction projects.

The published academic and non-academic literature introduced many studies on the issue of communication including the definitions, importance, barriers to effective communication and means of poor communication. However, these studies ended up only on the general issue of communication lacking an in-depth investigation of the causes and effects of poor communication in the construction industry. The study implemented advanced structural modeling to study the causes and effects. The questionnaire, the data and concluding results fill the identified research gap of this study. The addressed issue is also of interest because communication is considered one of the main knowledge areas in construction management.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.