Purpose: The digital transformation under Industry 4.0 is complex and resource-intensive, making a strategic digitalization guideline vital to small and medium-sized enterprises' success in the Industry 4.0 transition. The present study aims to provide manufacturing small and medium-sized enterprises (SMEs) with a guideline for digital transformation success under Industry 4.0.Design/methodology/approach: The study first performed a content-centric literature review to identify digital transformation success determinants. The study further implemented interpretive structural modeling to extract the order at which the success determinants should be present to facilitate the SMEs’ digital transformation success optimally. The interpretive model and interpretive logic knowledge base matrix were also used for developing the digital transformation guideline.Findings: Eleven success determinants are vital to SMEs’ digital transformation efforts. For example, results revealed that external support for digitalization is the first step in ensuring digital transformation success among SMEs, while operations technology readiness is the most inaccessible success determinant.Research limitations/implications: The study highlights the degree of importance of the 11 success determinants identified, which magnifies each determinant's strategic priority based on its driving power and dependence power. Theorizing the dependent variable of “digital transformation success” and quantitatively measuring the extent to which each success determinant contributes to explaining “digital transformation success” offers an exciting opportunity for future research.Practical implications: Digital transformation success phenomenon within the Industry 4.0 context is significantly different from the digitalization success concept within the traditional literature. The digital transformation under Industry 4.0 is immensely resource-intensive and complex. Smaller manufacturers must have specific capabilities such as change management and digitalization strategic planning capability to reach a certain degree of information, digital, operations and cyber maturity.Originality/value: The digital transformation success guide developed in the study describes each success determinants' functionality in relation to other determinants and explains how they might contribute to the digital transformation success within the manufacturing sector. This guide enables smaller manufacturers to better understand the concept of manufacturing digital transformation under Industry 4.0 and devise robust strategies to steer their digital transformation process effectively.

Purpose: The present study offers a holistic but detailed understanding of the factors that might affect small and medium-sized enterprises (SMEs) adoption of Industry 4.0 technologies to empower smaller businesses to embrace Industry 4.0.Design/methodology/approach: The study conducted a systematic review of the literature and drew on the technology-organization-environment framework to identify various technological, organizational and environmental determinants of Industry 4.0 technology adoption and their underlying components. The study applied the textual narrative synthesis to extract findings from the eligible articles and interpret them into the Industry 4.0 technology adoption roadmap.Findings: Industry 4.0 is a vital strategic option to SMEs, enabling them to keep up with the digitalization race. SMEs significantly lag behind large organizations in benefiting from disruptive Industry 4.0 technologies. SMEs are still struggling with the initial adoption decisions regarding the digital transformation under Industry 4.0. Results identified various determinants that might explain this condition. The study developed a digitalization roadmap that describes the necessary conditions for facilitating SMEs’ digitalization under Industry 4.0.Practical implications: Various technological, organizational and environmental factors might determine the current positioning of SMEs against Industry 4.0. These determinants can act as barriers or drivers depending on their properties. The roadmap describes determinants indispensable to promoting Industry 4.0 technology adoption among SMEs, such as knowledge competencies or value chain digitalization readiness.Originality/value: Exclusively focusing on empirical research that reported applied insights into Industry 4.0 technology adoption, the study offers unique implications for promoting Industry 4.0 digital transformation among SMEs.

Purpose: The quality of people life and efficiency of banks can be improved by mobile banking (m-banking). The long-term success of m-banking depends on its constant use. The purpose of this paper is to investigate the determinants of m-banking continuance intention to use, using the technology continuance theory (TCT) by including the self-efficacy and channel preference. Design/methodology/approach: Empirical data from 369 Malaysian users who had prior experience with mobile banking were analysed, using partial least squares technique. Findings: The results confirmed that the TCT model had a high exploratory power in explaining users’ perceived usefulness (PU), satisfaction, attitude and intentions to continue to use m-banking. Furthermore, self-efficacy and channel importance were important drivers of continuance intention in the context of m-banking. According to the results, perceived ease of use has no effect on PU and attitude in the post-adoption stage. Practical implications: The findings help bank managers to understand the importance of meeting customers’ needs and expectations as a prerequisite in enhancing their satisfaction and favourable attitude towards m-banking and consequently their continuance intention. Originality/value: Based on the TCT model, this study contributes to the limited body of research on continuance intention to use m-banking. Furthermore, self-efficacy and channel preferences were added to the TCT model and the results confirmed the importance of enriching the TCT model to explain continuance intention to use information systems by adding contextual factors.