Search results

The study investigates the effect of political instability and employee tenure security on the performance of firms in middle-income economies (MIEs) after controlling for the influence of corruption, international quality certification, external auditor services and firm age. It examines whether ownership and sector effects matter in the explored relationships.

The study adopts the generalized method of moments estimator and collects firm-level cross-sectional data from 77 MIEs.

The evidence shows that political uncertainty, employee tenure security and firm age negatively impact firm performance. Also, external quality assurance mainly improves firm performance. Additionally, foreign-owned firms benefit from corruption more than their domestic counterparts. Moreover, there are ownership and sector effects in the firm performance drivers.

The findings suggest the need for MIE firm managers to implement policies and programs to improve permanent employees' efficiency, commitment and honesty. Policy makers and political actors must work toward a stable political environment in MIEs. The policy must also focus on at least minimizing corruption.

The study shows the contributions of employee tenure security, political instability and corruption to the performance of MIE firms. It documents sector and ownership effects in the factors influencing firm performance.

This is a general review study aiming to specify the key customer-based factors and technologies that influence the value co-creation (VCC) process through artificial intelligence (AI) and automation in the hospitality and tourism industry.

The study uses a theory-based general literature review approach to explore key customer-based factors and technologies influencing VCC in the tourism industry. By reviewing the relevant literature, the authors conclude a theoretical framework postulating the determinants of VCC in the AI-driven tourism industry.

This paper identifies customers' perceptions, attitudes, trust, social influence, hedonic motivations, anthropomorphism and prior experience as customer-based factors to VCC through the use of AI. Service robots, AI-enabled self-service kiosks, chatbots, metaversal tourism and new reality, machine learning (ML) and natural language processing (NLP) are technologies that influence VCC.

The results of this research inform a theoretical framework articulating the human and AI elements for future research set to expand the models predicting VCC in the tourism industry.

Few studies have examined consumer-related factors that influence their participation in the VCC process through automation and AI.