Xiangyu Liu

Digital transformation of traditional enterprises can better develop new customer relationships and help mitigate the business risk of their over-reliance on single-customer relationships. However, little research has been conducted on the internal mechanisms of how enterprise digitalization reshapes corporate customer relationships. In this manuscript, from the perspective of dynamic capability theory, we construct conceptual models of enterprise digital transformation, innovation capability, operational cost, and customer satisfaction, and explore the internal mechanisms of enterprise digital transformation to reduce the dependence of enterprises on large customers. The model is empirically studied by obtaining data on the degree of digital transformation of enterprises through “search statistics” of keywords in the annual reports of Chinese listed companies during 2011–2019. This manuscript finds that digital transformation significantly reduces the concentration of large customers and has become a powerful driver of business model innovation in the digital economy, and this finding remains robust to the use of PSM and instrumental variable methods to address endogeneity. Digital transformation reduces firms’ dependence on large customers through three mechanisms: improving firms’ innovation capabilities, reducing firms’ operating costs, and increasing customer satisfaction. The impact of digital transformation on reducing the dependence of non-state enterprises on large customers is greater than that of state-owned enterprises; the implementation of digital transformation strategies is more helpful for enterprises that have active interactions with customers to reduce their customer concentration; and the reduction of customer concentration is greater for enterprises in regions with higher levels of digital development compared to those in regions with lower levels of digital development. The economic consequence test finds that digital transformation diversifies customer structure and reduces business risks. The analysis of the innovation effect and customer satisfaction effect on reducing the concentration of large customers of enterprisesby implementing digital transformation enriches and expands the dynamic capability theory and provides important insights for enterprises to diversify their customer structure.

PurposeThis study aims to develop and validate a stigma scale for Chinese patients with breast cancer.MethodsPatients admitted to the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, for breast cancer treatment participated in this study. Development of the Breast Cancer Stigma Scale involved the following procedures: literature review, interview, and applying a theoretical model to generate items; the Breast Cancer Stigma Scale’s content validity was assessed by a Delphi study and feedback from patients with breast cancer ; exploratory factor analysis was used to assess the construct validity; convergent validity was assessed with the Social Impact Scale ; internal consistency Cronbach’s α, split-half reliability, and test–retest reliability were used to identify the reliability of the scale.ResultsThe final version of the Breast Cancer Stigma Scale consisted of 15 items and showed positive correlations with the Social Impact Scale. Exploratory factor analysis revealed four components of the Breast Cancer Stigma Scale: self-image impairment, social isolation, discrimination, and internalized stigma, which were strongly related to our perceived breast cancer stigma model and accounted for 69.443% of the total variance. Cronbach’s α for the total scale was 0.86, and each subscale was 0.75–0.882. The test–retest reliability with intra-class correlation coefficients of the total scale was 0.947, and split-half reliability with intra-class correlation coefficients of the total scale was 0.911. The content validity index was 0.73–1.0.ConclusionThe newly developed Breast Cancer Stigma Scale offers a valid and reliable instrument for assessing the perceived stigma of patients with breast cancer in clinical and research settings. It may be helpful for stigma prevention in China.