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Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.

Growing evidence asserts that individuals are inclined to buy impulsively in the social commerce environment due to interactive elements. However, extant literature does not reveal the influence of emerging digital celebrities and their communities on impulse buying, although users may encounter them synchronously. Hence, this study explores the impact of parasocial relationships and social climate on impulse buying following the stimulus–organism–response framework with the incorporation of the urge to buy. Besides, this research investigates the role of hedonic and utility gratification-seeking behavior in parasocial relationships following uses and gratifications theory (UGT).

An empirical research study was conducted on Facebook, and data were collected from Pakistani users who followed digital celebrities. Partial least squares structural equation modeling (PLS-SEM) approach was employed to analyze the valid data of 231 respondents.

The results indicate that integrated constructs significantly influence impulse buying with complementary partial mediation of urge to buy. Besides, social climate significantly interacts the relationship of parasocial relationships and impulse buying. Further, passing time, enjoyment and information seeking has a significant impact on parasocial relationships, except for self-presentation.

This research provides key knowledge to comprehend the overall phenomenon of emerging digital celebrities through the integration of their parasocial relationships and the social climate of their communities, with potential intervening and interaction effects. This study also unveils the role of gratifications in building digital celebrities' parasocial relationships.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process.

Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia.

Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths.

Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms.

The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence.

We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups.

This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process.

Considering the rapid adoption of social media among consumers and organizations, this study intends to examine the impact of online bundle promotions and contextual interactions on impulse buying as consumers encounter them synchronously. Hence, a research model is proposed with the integration of perceived transaction value, perceived acquisition values, top reviews information, impulse buying tendency and emotional intelligence following the stimulus-organism-response framework, promotional framing effect, and theory of selective attention.

Data were collected from the active social media members of organization pages and selling groups by utilizing the self-administered questionnaire. This study employed the partial least squares structural equation modeling to evaluate the data of 358 individuals.

Results reveal the positive impact of targeted constructs on the urge to buy impulsively with complementary partial mediation of impulse buying tendency. Besides, emotional intelligence dissuades users' impulse buying tendencies, but unexpectedly, its moderating effect is insignificant. Further, importance-performance map analysis highlights the highest importance of impulse buying tendency and better performance of perceived transaction value for the urge to buy impulsively.

This research is one of the early studies to explore the influence of social media advertising and contextual social factors (e.g. bundle offers and top reviews information) on impulse buying with the moderation of emotional intelligence and mediation of impulse buying tendency. This research is imperative for scholars and managers with pertinent suggestions to arouse impulse buying.

The objective of this paper is to propose a practical and operable method to identify and fill organisational knowledge gaps during new product development.

From a microscopic view, this paper introduces the tree-shaped organisational knowledge structure to formalise the knowledge gaps and their internal hierarchical relationships. Based on the organisational knowledge structure, organisational knowledge gaps are identified through tree matching algorithm. The tree-edit-distance method is introduced to calculate the similarity between two organisational knowledge structures for filling knowledge gap.

The proposed tree-shaped organisational knowledge structure can represent organisations' knowledge and their hierarchy relationships in a structured format, which is useful for identifying and filling organisational knowledge gaps.

The proposed concept of organisational knowledge structure can quantify organisational knowledge. The approach is valuable for strategic decisions regarding new product development. The organisational knowledge gaps identified with this method can provide real-time and accurate guidance for the product development path. More importantly, this method can accelerate the organisational knowledge gap filling process and promote organisational innovation.

The purpose of this paper is to investigate the responsibility that should be taken by different pork supply chain participants to ensure pork quality and safety, with the aim of providing some guidance for strengthening the supervision of pork quality and safety.

The pig farmer survey and the pork consumer survey were conducted in Funing County, Jiangsu Province, using the best-worst scaling (BWS) and a mixed logit model.

The results showed that the designation of responsibility for ensuring pork quality and safety was of, in descending order, feed producers and suppliers, backyard farmers and farms of designated size, pork processing workshops and companies of and above designated size, slaughterhouses, supermarkets, farmer’s markets, pig transporters, and consumers. Both pig farmers and pork consumers believed that those involved in the initial pork supply chain should take greater responsibility for pork quality and safety.

Allocation of responsibilities across the entire pork industry chain was investigated from the perspective of pig farmers and pork consumers using the BWS and a mixed logit model. The results of this study might explain the unique problems that occur in pork supply chain management in large developing countries like China.

Fully formed knitting technology is a cutting-edge technology in the design and production of knitted apparel. Using this technology and its supporting design system, a new development mode of fully formed knitted apparel with double-layer structure and fake two-piece knitwear is proposed.

Based on the upper body structure feature points of human body characteristics and single-layer knitted garment prototype, a double-layer structure knitted garment pattern was established by pattern expansion method. The model was introduced into SDS-APPEX3 design system for process design, including three aspects consists: the inner vest, the outer blouse and double-layer joint part, analysis of the process and forming principle. Weaving on four-needle bed computerized flat knitting machine of MACH-2XS, through the setting of the machine parameters. Finally, a full-shaped fake two-piece knitted blouse was formed.

On the basis of single-layer knitted garment pattern, a double-layer garment pattern is constructed, and the design and weaving are completed on the four-needle bed computerized knitting machine of MACH-2XS and its supporting SDS-APPEX3 design system through the fake two-piece double-layer garment style design. The double-layer joint model is an effective reference for the construction of this kind of fake two-piece fully formed knitted clothing.

In this paper, a design and knitting method of fully formed double-layer structure fake two-piece knitted garment is proposed. The integrated knitting of fully formed double-layer structure sweater is realized for the first time, which provides ideas for the development of fully formed double-layer structure knitted clothing style and enriches the fully formed clothing style.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

This study investigates the effects of two types of self-disclosure by influencers (i.e. personal self-disclosure and professional self-disclosure) on followers’ parasocial relationships with them and online engagement with their content, which eventually affect followers’ purchase intentions.

This study collected data based on a cross-sectional survey of 823 social media users. Structural equation modeling analysis was used to test the overall structural model and the mediating roles of parasocial relationships and engagement.

This study reveals that influencers’ personal self-disclosure has a positive impact on followers’ parasocial relationships with them and online engagement with their content. Interestingly, the results indicate an inverted U-shaped relationship between influencers’ professional self-disclosure and followers’ parasocial relationships, as well as online engagement with the influencers’ content. Furthermore, followers’ parasocial relationships and engagement partially mediate the impact of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

This study contributes to the literature by revealing the underlying mechanisms of the differential effects of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

The findings will assist marketers in leveraging influencer-generated content to enhance influencer marketing effectiveness.

This research provides a better understanding of the potential linear and nonlinear effects of influencers’ self-disclosure on followers’ parasocial relationships and engagement in social media marketing.