Purpose: The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approach: This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.Findings: The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/value: The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

Industrial big data is the key to realize the vision of smart factories. This research aims to identify and explore potential barriers that prevent organizations from deploying industrial big data solutions in the development of smart factories through a socio-technical perspective. The research follows an inductive qualitative approach. Twenty-seven semi-structured interviews were conducted with the CEO, smart factory manager, IT managers, departmental heads, and IS consultants in the selected case company. The interview data were analyzed using a thematic analysis method. Derived from a thematic analysis, six sets of barriers including technical, data, technical support, organization, individual, and social issues were identified, as well as the relationships between them. An empirical framework was developed to highlight the relationship between these barriers. This study contributes to the knowledge of industrial big data in general and provides constructive insight into industrial big data implementation in smart factory development particularly.

This study aimed to explore factors affecting continual usage of one-stop smart urban apps, which is the key to realise the vision of smart cities. A questionnaire (involved a total of 578 users/citizens) was carried out to test the research model, which was established based on existing user behavioural theories and recent literature on smart cities. The results, derived from confirmatory factor analysis and structural equation modelling, confirmed that lower costs (measured by cognitive effort and perceived risks), as well as higher “smartness” (measured by output usefulness, output velocity, and the quality of offline services), would lead to better user satisfaction. Moreover, system quality has indirect influence on user satisfaction (i.e. citizens only feel dissatisfied when system pitfalls affect output usefulness and output velocity of smart urban apps), which together with social influence will determine citizens’ continuance usage intention of smart urban apps.

Live streaming e-commerce is a new mode of shopping that has been introduced over recent years. Understanding the factors affecting consumers' purchase intention is becoming increasingly critical. The purpose of this paper is to present an empirical analysis of consumers' purchase intention in live streaming e-commerce. An inductive qualitative approach was followed. Using the research method of interview, twenty-eight consumers were selected as research samples to explore what factors affect consumers' purchase intention. The interview data was analyzed using a thematic analysis method. The results of this research contain a categorization of seven different key factors, namely streamer's credibility, product evaluation, platform design, price promotion, perceived value, live interactivity and service integrity, together with the interrelationships between them. This study contributes to the literature of live streaming e-commerce, consumer purchase intention and marketing by bridging three disciplines and provides a constructive insight into live streaming e-commerce merchants.