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This study aims to show that management with big data analytics capability can achieve more advantages of the global sourcing process. Furthermore, this study using its conceptual attitude model aims to show that big data analytics management capability leads to an increase in firm performance by the mediating role of integration.

Using an online questionnaire, 158 managers from 13 Iranian companies taking advantage of the global sourcing process were surveyed. The validity of the hypotheses was evaluated using partial least squares based on structural equation modeling (PLS method).

The results of the study showed that big data analytics management capability has a positive impact on global sourcing and firm performance directly, and by the mediating role of integration.

Previous studies have carefully addressed the role of big data and big data analytics in firms. However, this is among a few studies addressing the role of big data analytics capability, especially management capability, in improving firms’ performance. The results of this study shed light on the fact that how global sourcing takes the best advantage of big data analytics management capability for better accomplishment of organizations’ duties. The results of this study also disclose how big data analytics management capability helps organizations with their performance and bring benefits to their units.

This paper aims to identify the impact of demographical and organizational variables such as age, gender, experiences use of knowledge management system (KMS), education and job level on knowledge sharing (KS) performance of knowledge workers in knowledge activities of a KMS. Specifically, it seeks to explore that is there any relationship between the KS behavior patterns of high KS performance knowledge workers with their performance. Furthermore, this study using its conceptual attitude model aims to show that whether knowledge workers’ behavior patterns in sharing information and knowledge throughout a KMS have any specific effect or not.

This paper proposed a framework to mine knowledge workers’ raw data using data mining techniques such as clustering and association rules mining. Also, this research uses a case-based approach to a knowledge-intensive company in Iran that works in the field of information technology with 730 numbers of workers.

Findings suggest that demographical and organizational variables such as age, education and experience use of KMS have positive effects on knowledge worker’s KS behavior in KMSs. In fact, people who have lower age, higher education degrees and more experience use of KMS, have more participation in KS in KMS. Also, results depict that the experienced use of KMS has the most impact on the intention of KS in this KMS. Findings emphasize on the importance of the influence of the behavioral, organizational environments and psychological factors such as reward system, top management support, openness and trust, on KS performance of knowledge workers in the KMS. In fact, according to data, the KMS reward system caused to increasing participation of the users in KS, also in each knowledge activity that top managers participate in, the scores were higher.

This research helps top managers in designing policies and strategies to improve the participation of knowledge workers in KS and helps human resource managers to improve their membership policies. Also, assist Information Technology (IT) managers to enhance KMSs’ design to leverage with organization strategies in the field of improving KS and encourage people to participate in KMS.

This research has two key values. First, this paper applies a data mining framework to mining and analyzing data and this paper uses actual data of a KMS in a specialist company in Iran, with about 27,740 real data points. Second, this paper investigates the impact of demographical and organizational attributes on KS behavior, which little is empirically known about the impact of demographical variables on KS intention.