Program: Textilekonomutbildningen

Program: Textilekonomutbildningen

Program: Textilekonomutbildningen

Program: Textilekonomutbildningen

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Authors have identified that there are a number of researches concerning on only individualdriving factor of the business network in the SME, mostly done in a single context. Apartfrom that, there is also a lack of researches focusing on the comparison of the businessnetwork‘s driving factors, especially for SMEs.The purpose of this study is to explore and investigate the driving factors that drive SMEsto engage with the business network by focusing on two different contexts, Cambodia andFinland.The thesis adopts the inductive approach and employs a quantitative research method. Inthis study, the survey questionnaire is chosen to obtain the relevant data. The authors alsoextend the method by including four interviews to have better understanding andinterpretation of the content of the collected data. The primary data are collected through asemi-structured interview with two Cambodian companies and two Finnish companies. Thesource of secondary data such as articles, textbooks, scientific journals and websites arealso used in this thesis.The thesis first concludes that the five chosen factors (obtaining resources, cooperation,internationalisation, cost reduction, and lobbying government policies) play an essentialrole in driving SMEs to take part in the business network. Secondly, the study alsoconfirms that there are some different perceptions on the level of importance of eachdriving factor which the SMEs in Cambodia and Finland conceive that drive them toengage with the business network. Cost reduction factor has been perceived more critical inFinnish context compared to the Cambodian; while in Cambodia SMEs discern thelobbying government policies factor has a higher degree of importance than that in Finland.Lastly, the study has identified the influential aspects of the differences such as the culturalaspect, the definition and size of SMEs, and the private sector development support in thesetwo contexts.For the managerial implications, first partaking in the business networks could providevarious benefits to firms rather than just single aspect. Second, management could decidein which direction they should drive their company by having a better understanding ofSMEs behaviour toward the participation in the business network. Last, different factorscan be crucial to a different kind of SMEs.

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The decision-making process (DMP) is based on two elements: Organizational and technical (Poleto et al., 2015). The organizational element is related to managers’ everyday decisionmaking based on the organization strategy (Poleto et al., 2015). Its aim is to set up specific actions for the planned objectives for the business (Rouse, 2018). The second element is the technical DMP. According to (Poleto et al., 2015), it is related the set of tools that are used as an aid in the DMP, which includes information technology and big data. Business intelligence (BI) is the decisionmaking helping system (Ali et al., 2017). Consequently, BI helps make better decisions, and it has become popular in many organizations. As a result, it is important to show BI’s power over DMPs and to show how the tools used in BI facilitate the DMP. “Higher education institutions worldwide are operating today in a very dynamic and complex environment” (Kabakchieva 2015, p. 104). As a result, universities that are within higher education are threatened because competition is serious (Barrett, 2010). Moreover, higher education is another area that will potentially impact big data research (Ong, 2016). Consequently, the application and use of big data in higher educational institutions may result in better quality education for students and a better experience for the university staff (Ong, 2016). As a result, HEI is adopting new technologies with the aim of sustaining its position on the market. DMPs at higher academic institutions require structured data from a sophisticated system, which can be only done through efficient and effective use of BI tools. This thesis will investigate how the BI system is used at Linkoping University (LIU) and how its benefits have changed DMPs. We studied the BI tool (Qlikview) that has been used at LIU for 10 years. 10 To answer the research question, a theoretical framework was developed that was based on two models: Simon’s (1997) and Huber’s (1980) DMP models. The two models were combined with the BI benefits that were based in El Bashir et al.’s (2008) model. The research is done through a qualitative method of data collection and data analysis. At LIU, seven interviews were conducted with BI users and with strategic decision-makers. The findings show that the BI system, alongside Qlikview, has a positive effect on DMPs at LIU as a public HEI. The factors affected are the information gathering time, the quality of data provided and the accessibility to information by all BI users.

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