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Article
Publication date: 15 September 2021

Yang Lei, Qiang Zhou, Jifan Ren and Xiling Cui

This study aims to examine how job satisfaction (JS) affects two types of knowledge sharing (KS), in-role KS and extra-role KS. It also investigates the mediating effect of…

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Abstract

Purpose

This study aims to examine how job satisfaction (JS) affects two types of knowledge sharing (KS), in-role KS and extra-role KS. It also investigates the mediating effect of knowledge sharing self-efficacy (KSSE) and the moderating effect of team collaborative culture (TCC) between JS and two types of KS.

Design/methodology/approach

This study applies attribution theory to develop a cross-level model and validate it through paired data collected from 322 information technology professionals nested within 80 teams. Hierarchical linear modeling is used to test the hypotheses.

Findings

JS positively influences in-role and extra-role KS via KSSE and TCC positively moderates the relationship between JS and extra-role KS.

Originality/value

This study is one of the first to investigate the mechanism underlying the influence of JS on two types of KS. It also identifies the mediating and moderating effects of this mechanism.

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Article
Publication date: 14 May 2018

Yuanyuan Lai, Huifen Sun and Jifan Ren

Based on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to…

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Abstract

Purpose

Based on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.

Design/methodology/approach

Drawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.

Findings

The empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.

Research limitations/implications

Given the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.

Practical implications

This paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

Social implications

This research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.

Originality/value

This paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

Details

The International Journal of Logistics Management, vol. 29 no. 2
Type: Research Article
ISSN: 0957-4093

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