Abstract

Small and medium-sized corporations (SMEs) are a key component of their country's national having list as they play a big role in job creation, exporting promotion, GDP growth, and poverty reduction. County executive authorities that provide SMEs with the competitiveness and performance know-how to enable their local and international trade operations through knowledge and skills transfer. The Government of Pakistan happens to put in place one of such SME aid programs in line with the Common Facility Centre (CFC) program. The aim of CFC is to inform SMEs about modern production techniques and technical knowledge in order to improve their overall technological abilities and competitiveness by which the chances of a business growing can be achieved. For this study, the positivist approach will be chosen in the quantitative technique to identify the cause and effect relationship to get a conceptual view about CFC support program digital transformation and performance of SMEs. It may appear that the CFC program is likely to enhance SME performance. However, it is predicted that the role of sensing capability in digital transformation may be capable to moderate this liability where sensing ability may play a mediating position. Main research studies, with regard to effectiveness of SME support programs, remain focused on the aggregate outcomes, ignoring individual company's capacity to positively affect firms' performance. A quantitative research technique developed from the positivist paradigm is provided to gather data and investigate the linkages among CFC support programs, digital transformation, and SME success. It is advocated that objective and perceptual data be acquired by adopting a survey design via a self-administrated questionnaire. This research sample will include SMEs in Pakistan's leading industrial clusters employing the CFC assistance program. A survey questionnaire has been used to collect the data. Then data is coded, fed, and analyzed by SPSS, following proper validation through exploratory and confirmatory factor analysis.

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