Search results

Government organizations often store large amounts of data and need to choose effective data governance service to achieve digital government. This paper aims to propose a novel multi-attribute group decision-making (MAGDM) method with multigranular uncertain linguistic variables for the selection of data governance service provider.

This paper presents a MAGDM method based on multigranular uncertain linguistic variables and minimum adjustment consensus. First, a novel transformation function is proposed to unify the multigranular uncertain linguistic variables. Then, the weights of the criteria are determined by building a linear programming model with positive and negative ideal solutions. To obtain the consensus opinion, a minimum adjustment consensus model with multigranular uncertain linguistic variables is established. Furthermore, the consensus opinion is aggregated to obtain the best data governance service provider. Finally, the proposed method is demonstrated by the application of the selection of data governance service provider.

The proposed consensus model with minimum adjustments could facilitate the consensus building and obtain a higher group consensus, while traditional consensus methods often need multiple rounds of modifications. Due to different backgrounds and professional fields, decision-makers (DMs) often provide multigranular uncertain linguistic variables. The proposed transformation function based on the positive ideal solution could help DMs understand each other and facilitate the interactions among DMs.

The minimum adjustment consensus-based MAGDM method with multigranular uncertain linguistic variables is proposed to achieve the group consensus. The application of the proposed method in the selection of data governance service provider is also investigated.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

As global issues such as climate change, economic growth, social equality and the wealth gap are widely discussed, education for sustainable development (ESD) allows every human being to acquire the knowledge, skills, attitudes and values necessary to shape a sustainable future. It also requires participatory teaching and learning methods that motivate and empower learners to change their behavior and take action for sustainable development. Teachers have begun rating pupils based on peer assessment for open evaluation. Peer assessment enables students to transition from passive to active feedback recipients. The assessors improve critical thinking and encourage introspection, resulting in more significant recommendations. However, the quality of peer assessment is variable, resulting in reviewers not recognizing the remarks of other reviewers, therefore the benefits of peer assessment cannot be fulfilled. In the past, researchers frequently employed post-event questionnaires to examine the effects of peer assessment on learning effectiveness, which did not accurately reflect the quality of peer assessment in real time.

This study employs a multi-label model and develops a self-feedback system in order to use the AIOLPA system in the classroom to enhance students' learning efficacy and the validity of peer assessment.

The research findings indicate that the better peer assessment through the rapid feedback system, for the evaluator, encourages more self-reflection and attempts to provide more ideas, so bringing the peer rating closer to the instructor rating and assisting the evaluator. Improve self-evaluation and critical thinking for the evaluator, peers make suggestions and comments to help improve the work and support the growth of students' learning effectiveness, which can lead to more suggestions and an increase in the work’s quality.

ESD consequently promotes competencies like critical thinking, imagining future scenarios and making decisions in a collaborative way. This study builds an online peer assessment system with a self-feedback mechanism capable of classifying peer comments, comparing them with scores in a consistent manner and providing prompt feedback to critics.

Technological advancements have benefited businesses all over the world in how they set up production lines, create new products/services and trade goods. Multinational corporations can communicate instantly with their distant operations by utilizing information technology tools and communication networks. Businesses have taken a significant shift and new factors have emerged which affect company's competitiveness. In case of resorting to an outsourcing option, a comprehensive approach for valuing the essential criteria is often missing. While specifically focusing on the decisions that have a huge impact on company's performance, it is crucial to pay close attention to the ways of selecting suppliers. The purpose of research is to choose the optimal manufacturing alternative from a set of possibilities.

The current research utilizes the Delphi technique for collection of vital criteria such as “quality”, “cost”, “delivery”, “warranties and claims”, “supplier profile”, “relationship and communication” and their respective sub-criteria. The purpose of research is to choose the optimal manufacturing alternative from a set of possibilities. In this regard, Analytical Hierarchy Process (AHP) technique is employed.

The current research enlightens that outsourcing can yield promising beneficial results. The results highlighted that in Hi-tech public sector organizations, international alternative is found best in almost all criteria especially in vital criteria such as “Quality”, “Cost”, “Delivery”, “Supplier Profile,” etc. Similarly, in case the outsourcing is done to a Domestic alternative, still the Domestic alternative is found effective in comparison to in-house manufacturing setups. The research showed unexpected results. Because previously it was assumed that in-house manufacturing would be more beneficial. However, the current findings support the “NASA” strategy which moved toward outsourcing to private sector.

Limitations of the proposed methodology also produce opportunities for further exploration of the topic. One key limitation of the research described in this study is that the parameters and their sub-parameters interdependency were not taken under consideration. This means that quality and cost are not dependent upon each other. However, in reality quality and cost are interlinked. This means if quality is increased, cost is also increased. Similarly, for products having zero percent of re-claim, the product would have to be manufactured with high quality.

The study is advantageous for both suppliers and purchasers, in any type of businesses where decision-making problem are under consideration. This model aids suppliers in revealing, how they can expand their profile, by focusing on the current research's selection criteria. In this way alternatives profile can now be perfected. Moreover, buyers can now rank suppliers on their quality management, financial status and other essential factors in order to conduct purchasing decisions. For the decision maker, the results illustrate which critical factors to evaluate when screening suppliers by applying current model techniques.

It is obvious that nearly almost every industry is forced to look for alternatives for all of its operations if outsourcing is an option. The study's findings have major benefits for all industries with an important role in manufacturing and supply chain operations. These objectives will serve the industries well and they will be able to prioritize their alternative selection criteria based on their operations. The findings of this study can assist any organization in their selection of vendors by providing a more detailed explanation of the impact that various criteria have on the decision-making process.

To the best of authors' knowledge, no previous study has used two approaches (AHP and Delphi study) to propose a model for making manufacturing decisions with domestic, in house and international alternatives in Hi-tech public sector organizations. The model not only benefits the manufacturers for choosing suitable suppliers but also aids suppliers to build their profile in an improved fashion by focusing on the vital attributes. This research benefits managers to improve their ability to make effective purchasing decisions, and also opens new avenues for researchers to further explore such findings in other areas as well.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

Social media has progressively upgraded an interactive domain via online sociability and information-sharing. This study aims to formulate an information-sharing intention model by identifying the decisive role of intrinsic and extrinsic motivations.

Empirical data from 508 participants were collected to examine the structural model using structural equation modeling.

Results indicate that information-sharing intention is strongly promoted by intrinsic and extrinsic motivations. Furthermore, perceived herding, perceived crowd and intrinsic motivation boost substantially extrinsic motivation. Perceived herding is of utmost importance to extrinsic motivation, whereas emotional appeal and informative appeal are of paramount importance to intrinsic motivation. Moreover, source trust and exhibitionism are underlying motivations for intrinsic motivation.

The findings provide useful guidelines for practitioners to urge users into information-sharing via social media.

This study contributes significantly to the current literature by developing an effective mechanism of information-sharing through social media based on the motivational theory.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

The current body of empirical research regarding the impact of trust in the cybersecurity commitment of institutions on digital payment usage has focused solely on a macro-level analysis, overlooking the intricate dynamics between institutions' cybersecurity commitments and the trust levels of digital payment users. In light of this limitation, this study aims to offer a more comprehensive understanding of this complex relationship.

A case study was conducted on digital payment users in India through the critical realist lens. To gather data, interviews and focus group discussions were conducted with digital payment users from various regions of the country.

The citizen-centric outcomes of the national cybersecurity commitment (performance and responsiveness) are the most prominent and impactful trust indicators. These outcomes play a crucial role in shaping digital payment users' perception and trust in the cybersecurity commitment of public institutions. Individuals' value positions also influence trust judgments, as it is essential to recognize the value tensions that may arise due to security implementation and their congruence with citizens' values.

The findings of this study have significant implications for policymakers. They are potentially an artifact of the security and perception of digital payment users and the cultural uniqueness of digital payment users in India.

The study proposes a holistic understanding of the relationship between institutions' cybersecurity commitments and the trust levels of digital payment users. It offers a qualitative evaluation of how digital payment users perceive and construe efficient information security management implemented by public institutions.

This paper proposed an adaptation of the theory of planned behavior (TPB) model to study the factors influencing organic food purchase behavior in an emerging market. This research introduced household norms as an important factor that reflected the influence of household activities and family pressure on individuals to perform organic food purchase behaviors. The role of trust in organic food as a direct and a moderating factor was examined in the proposed framework as well.

The study proposed a model with 10 hypotheses from the literature review. The hypotheses were tested using data collected from 407 organic food customers in Hanoi, Vietnam. The partial least squares structural equation modeling (PLS-SEM) approach was used for analysis.

The results indicated that household norms played an important role influencing purchase intention and behavior. This research also showed that trust in organic food directly affected purchase intention and played a moderating role on the attitude towards organic food and purchase intention relationship. However, trust in organic food did not show moderating effects on other relationships in the model.

More context-specific reasons may be incorporated into the research model to better explain consumer purchase behaviors.

The role of household norms and its impact under TPB has not been investigated for organic food purchase behaviors, particularly in emerging markets.