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Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

Service robots with advanced artificial intelligence (AI) can collect data on customer preferences, understand complex requests, improve services, and tailor marketing strategies. This study examined how perceived relatedness, perceived warmth, and customer–AI-assisted exchanges (CAIX) of service robots affect customer service competencies and brand love through service-robot intimacy.

A brand love model was developed based on the AI device using acceptance and an emotional perspective. Data were collected from customers who had dined in robot restaurants; 415 questionnaires were completed, and partial least squares analysis was adapted to the proposed model.

The results demonstrate that the perceived relatedness, perceived warmth and CAIX of service robots affect the intimacy of robot restaurants. Customers who feel friendly and satisfied with a restaurant’s service robot will recommend it to their friends.

This study draws on theory and existing literature to identify principal factors in robot restaurant service capabilities. Future research can include service robot data analysis capabilities and adoption process factors as the direction of customer relationship management research while also exploring the influence of AI computing on restaurant supply chains. Likewise, the agility of service robots in the stages of innovation can be discussed in future research based on different theories, which will bridge unique insights.

The findings of this study emphasize the relationship between service robots and restaurant brand love and propose specific practice areas for restaurants.

This study expands the main issue of current brand love research from traditional restaurant operations to the novel field of humanoid service robot restaurants. It enriches our understanding of how consumers’ emotional fondness for a brand affects their behavioural intentions.