Role of knowledge conversion and social networks in team performance

Determinants of alliance productivity and performance: evidence from the automobile industry

Purpose

This study aims to identify critical factors and examine their impact on alliance performance from an organizational learning point of view.

Design/methodology/approach

A modified total interpretive structural modeling (M-TISM) methodology was used in this study. The different paths/links in the developed M-TISM model were further validated by using the Mahindra-Ford alliance case study.

Findings

In this study, a total of seven critical factors were identified using an extensive literature review, and a hierarchical model was developed. Results show that prior alliance experience, inter-partner learning, knowledge transfer, absorptive capacity and knowledge internalization have a positive on the alliance productivity and performance. Furthermore, the findings indicate that prior alliance experience remains essential for alliance productivity and performance, while knowledge transfer and absorptive capacity can contribute to inter-partner learning and knowledge internalization in strategic alliances.

Research limitations/implications

This study can help managers and policymakers to understand the identified critical factors from an organizational learning perspective and understand their impact on the alliance performance in a competitive environment. The managers should know that previous alliance experience, learning from partner firms, building an absorptive capacity, etc., are necessary to achieve superior alliance productivity and performance. For academicians, the M-TISM methodology used in this study can provide a mechanism to perform exploratory research and build a hierarchical model in different management research fields.

Originality/value

The study fills research gaps by identifying key factors, developing a hierarchical model, and examining their impact on the performance of strategic alliances in the Indian automotive industry.

Estimating Designers’ Performance considering Personal Characteristics and External Factors Together

Design team performance evaluation can occur in different ways, all of them requiring considerations on interactions among team members; in turn, these considerations should count on as many pieces of information as possible about individuals. The literature already explains how personal characteristics and/or external factors influence designers' performance; nevertheless, a way to evaluate performance considering several personal characteristics and external factors together is missing. This research tries to fill the gap by developing the Designer’s Performance Estimator (DPE), a ready-to-use tool for researchers and practitioners who need to make information about team members as richer as possible.

Big Data Capabilities and Firm’s Performance: A Mediating Role of Competitive Advantage

Big data (BD) capabilities have now grabbed the attention of strategic management researchers because of their significant role in firm’s performance and success. Nonetheless, very little empirical evidence has so far been put forward about the contribution of BD capabilities towards a firm performance and Competitive Advantage (CA). This research examines the impact of Big Data Technological Capabilities (BDTC) and Big Data Personal Capabilities (BDPC) on firms’ performance with a mediating role of CA among the firms operating in the world’s top emerging economy such as China. Data were collected through structured questionnaires using a sample size of 312 firms. The hypotheses were tested in AMOS.21 using Structural Equation Modelling (SEM). The results indicate that BDTC and BDPC have significant positive impact on firm performance and CA and there is also significant positive relationship between CA and firm performance. The study highlights that CA partially mediates the relationship between BDTC and firm performance as well as between BDPC and firm performance. Hence, BD-oriented firms achieve CA which in turn leads to superior performance in dynamic markets. This study discussed implications for managerial practices.

A study of crowd-collaborative learning: an empirical study

Purpose

Information technology is widely applied for completing group tasks and enhancing learning in computer-supported collaborative learning (CSCL) environment. Group members not only complete tasks but also learn ideas from other members of the group. These ideas can be better than what individual could come up with. As these ideas are referred as an upward comparison in the perspective of social comparison theory. However, there are limited studies that provide evidence in investigating how social comparison orientation (SCO) perceived by individual learner impacts on learning and group performance. The paper aims to discuss these issues.

Design/methodology/approach

This study experimentally tackles this problem at both individual and group level. An experimental study was applied in this study. Structural equation modeling and hierarchical linear modeling approaches are used to validate the data.

Findings

Results of 168 subjects reveal that SCO does have associations with group performance and learning performance respectively. Discussions and implications for literature and practice are given at the end of the paper.

Originality/value

This study confirms that the social genesis occurs more effectively through social interactions in CSCL. It also extends our understandings about the impacts of SCO. Results reveal that the higher level of SCO adopted by group members, the higher level of social interactions at group level can be triggered. As a result, the group performance can be enhanced. On one hand, these findings bridge the research gap in terms of investigating the notion of SCT on CSCL. On the other hand, it provides a possible solution in alleviating the problem of social loafing as commonly observed in CSCL. Therefore, these findings fulfilled the two research objectives.