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1 – 10 of over 27000Jiangnan Qiu, Liwei Xu, Min Zuo, Jingxian Wang and Weadon Helen
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations
Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms.
Practical implications
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.
Social implications
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.
Originality/value
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.
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Although online brand communities (OBCs) are extensively demonstrated to be an important social media tool in building brand equity, they may have backfire effects under certain…
Abstract
Purpose
Although online brand communities (OBCs) are extensively demonstrated to be an important social media tool in building brand equity, they may have backfire effects under certain conditions. Drawing from the self–brand connection theory, the purpose of this study is to investigate the effect of group heterogeneity on brand commitment. The mediation effect of self–brand connection and moderation effect of brand symbolism has also been examined.
Design/methodology/approach
Data were collected using a survey of 498 users from a range of OBCs. Hierarchical regression and bootstrapping method were used to test the research model.
Findings
The findings indicate that group heterogeneity negatively affects brand commitment in which self–brand connection plays a role of mediation. Further, the negative effect is more pronounced for high-symbolic brands than low-symbolic ones.
Practical implications
Brand managers are advised to note the dark side of OBCs in general and alleviate the adverse effects of group heterogeneity in particular, especially for high-symbolic brands.
Originality/value
Previous research pays little attention to the adverse effect of OBCs. This study enriches the literature by revealing that the backfire effect of OBCs arises when users become heterogeneous and uncovering in what situations the negative effect is stronger.
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Junyun Liao, Defeng Yang, Haiying Wei and Yulang Guo
Despite the increasingly common view that online brand community (OBC) members are heterogeneous, knowledge concerning the impact of group heterogeneity on community and brand…
Abstract
Purpose
Despite the increasingly common view that online brand community (OBC) members are heterogeneous, knowledge concerning the impact of group heterogeneity on community and brand level outcomes is lacking. In response and drawing from organization research, this paper aims to study the consequences of two types of group heterogeneity (i.e. visible heterogeneity and value heterogeneity) on brand community commitment and brand commitment. The moderating role of tenure in a community is also examined.
Design/methodology/approach
A survey of 467 members of OBCs was conducted, and structural equation modeling was used to test hypotheses.
Findings
The results show that perceived visible heterogeneity positively affects brand community commitment, whereas perceived value heterogeneity has a negative effect on it. Brand community commitment positively relates to brand commitment; it also mediates the effect of perceived visible heterogeneity and perceived value heterogeneity on brand commitment. Further, the positive effect of visible heterogeneity on brand community commitment is stronger for short-tenure members, but the negative effect of value heterogeneity is stronger for long-tenure members.
Practical implications
The findings suggest that managers should make efforts to foster visible heterogeneity and reduce value heterogeneity. In addition, managers are advised to emphasize the characteristics that carry different appeal for members of different tenure.
Originality/value
This research is one of the first few quantitative studies to examine the influence of brand community heterogeneity on community, and especially brand level outcomes. It extends the literature on the effect of brand community on brands and adds to the emerging heterogeneity view of OBCs.
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Wayne S. DeSarbo, C. Anthony Di Benedetto and Michael Song
The resource‐based view (RBV) of the firm has gained much attention in recent years as a means to understand how a strategic business unit obtains a sustainable competitive…
Abstract
Purpose
The resource‐based view (RBV) of the firm has gained much attention in recent years as a means to understand how a strategic business unit obtains a sustainable competitive advantage. In this framework, several research studies have explored the relationships between resources/capabilities and firm performance. This paper seeks to extend this line of research by explicitly modeling the heterogeneity of such relations across firms in various different industries in exploring the interrelationships between capabilities and performance.
Design/methodology/approach
A unique latent structure regression model is developed to provide a discrete representation of this heterogeneity in terms of different clusters or groups of firms who employ different paths to achieve firm performance vis‐à‐vis alternative capabilities. An application of the proposed methodology to a sample of 216 US firms were provided.
Findings
Finds that the derived four group latent structure regression solution statistically dominates the one aggregate sample regression function. Substantive interpretation for the findings is provided.
Originality/value
The paper contributes to the understanding of the performance effects of investing in capabilities in the RBV framework, which has previously been lacking, especially in the areas of information technology capabilities.
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Michael Klesel, Florian Schuberth, Jörg Henseler and Bjoern Niehaves
People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can…
Abstract
Purpose
People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.
Design/methodology/approach
The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.
Findings
Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.
Research limitations/implications
Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.
Originality/value
This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.
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Min Zuo, Jiangnan Qiu and Jingxian Wang
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Jennifer M. George and Eden B. King
We propose that group affective tone may be dysfunctional for teams faced with complex, equivocal, and dynamically changing tasks and environments. Group affective tone (and in…
Abstract
We propose that group affective tone may be dysfunctional for teams faced with complex, equivocal, and dynamically changing tasks and environments. Group affective tone (and in particular, a positive affective tone) may exacerbate pre-existing tendencies of teams to develop a single-shared reality that team members confidently believe to be valid and to be prone to group-centrism. Alternatively, heterogeneity in member mood states within teams may lead to the development of multiple-shared realities that reflect the equivocality of the teams’ tasks and circumstances and other functional outcomes (e.g., multiple perspectives and minority dissent), which ultimately may enhance team effectiveness.
Danny Campbell, Stephane Hess, Riccardo Scarpa and John M. Rose
The presence of respondents with apparently extreme sensitivities in choice data may have an important influence on model results, yet their role is rarely assessed or even…
Abstract
The presence of respondents with apparently extreme sensitivities in choice data may have an important influence on model results, yet their role is rarely assessed or even explored. Irrespective of whether such outliers are due to genuine preference expressions, their presence suggests that specifications relying on preference heterogeneity may be more appropriate. In this paper, we compare the potential of discrete and continuous mixture distributions in identifying and accommodating extreme coefficient values. To test our methodology, we use five stated preference datasets (four simulated and one real). The real data were collected to estimate the existence value of rare and endangered fish species in Ireland.
Edward E. Rigdon, Christian M. Ringle and Marko Sarstedt
Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of…
Abstract
Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.