Purpose The high failure rate – above 80% – in digital transformation projects raises concerns about relying solely on internal digital resources to generate competitive advantage. In this context, digital transformational leadership becomes critical for enabling organizations to respond to rapid technological change. While prior research has largely emphasized intraorganizational outcomes, this study aims to examine the mediating role of inbound open innovation between organizational agility and digital open innovation performance, particularly under conditions of digital transformational leadership. Design/methodology/approach The moderated mediation was tested with survey data from a sample of companies operating in the medium and high digital intensity service and manufacturing sectors. Findings Organizational agility has a strong positive effect on digital open innovation performance. Inbound open innovation plays a statistically significant but limited mediating role, indicating that most of the effect of agility remains direct. However, digital transformational leadership strengthens the relationship between inbound open innovation and digital open innovation performance, reinforcing the indirect pathway and enabling firms to translate collaboration with external partners into improved digital innovation outcomes Originality/value This study contributes to the literature on digital open innovation in three ways. First, it reconceptualizes digital transformational leadership as a boundary-spanning capability orchestrating agility and external knowledge flows. Second, it establishes the theoretical primacy of organizational agility in digital open innovation performance, while theorizing inbound open innovation as a complementary, leadership-contingent mechanism. Third, it conceptualizes digital transformational leadership as a boundary condition for the effectiveness of inbound open innovation, advancing the view that external knowledge flows yield conditional rather than intrinsic value.
Purpose The purpose of this study is to examine ethno-racial differences in job expectations within Peru’s diverse workforce. Specifically, the study evaluates whether members of Peru’s four major ethno-racial groups assign different levels of importance to various job characteristics, how their rank-order prioritizations compare and how these patterns inform organizational practices related to recruitment, retention and employee engagement. Design/methodology/approach Using Manhardt’s 25-item job expectations questionnaire, 562 Peruvian respondents (White/European, Mestizo, Indigenous/Native and Afro-Peruvian/African) rated the importance of job characteristics on a five-point Likert scale. Mean importance ratings, rank-order patterns and group differences were analyzed using multivariate analysis of covariance with age, gender and employment status as covariates, followed by analysis of covariances and post hoc comparisons. Spearman rank correlations assessed similarity in value hierarchies. Findings Ethno-racial identity significantly predicted job expectations across 22 of 25 items. Mestizo and Indigenous/Native respondents consistently assigned higher importance to both intrinsic and extrinsic job characteristics than White/European respondents, with Afro-Peruvian/African respondents generally falling between these groups. Despite absolute differences, rank-order correlations indicated moderate-to-strong similarity in how groups prioritized job attributes. Practical implications Organizations operating in Peru should tailor recruitment messages, job design and development opportunities to reflect the motivational priorities of different ethno-racial groups, particularly emphasizing security, growth and meaningful work for Mestizo and Indigenous/Native workers. Social implications Findings highlight how ethno-racial stratification shapes work values in Peru, offering insights for more equitable and culturally informed talent management strategies. Originality/value This study contributes to the limited body of research on within-country ethno-racial variation in job expectations in Latin America by extending the social characteristics model and self-determination theory to Peru’s distinct sociocultural context.
Purpose This paper aims to investigate how price, housing attributes and developer brand influence consumer happiness and purchase intention in Spain housing market. The research also examines the significance of technological innovation in influencing brand development and buyer experience. Design/methodology/approach This paper conducted a survey of 583 residents in Spain between June and September 2025, consisting of validated measurement scales. Partial least squares-structural equation modeling was used to test the proposed relationships. Findings The results show that housing attributes, developer brand and price all positively affect consumer happiness, which in turn, has a great influence on purchase intention. Housing attributes have the strongest influence, followed by developer brand and price. The findings also suggest that technology-supported brand signals, such as digital communication tools and PropTech-based interactions, also contribute positively to emotional responses and indirectly enhance purchase intention. Practical implications Developers may enhance purchase intention by improving housing quality, strengthening brand communication and adopting digital tools that increase transparency and consumer engagement. Originality/value This study offers a comprehensive view of housing decisions by integrating functional, emotional and technological factors into a single framework. It also extends the existing research by demonstrating the role of consumer happiness as a mediator in housing choices.
Purpose This study aims to analyse the heterogeneous effectiveness of government entrepreneurial support on start-ups by differentiating between two industry types, the supply chain (SC) economy, which supplies goods and services to other businesses, and the business-to-consumer economy (B2C), which serves final consumers. Design/methodology/approach The authors examine 1,956 start-ups that received participative loans from the Spanish governmental agency ENISA between 2014 and 2019. Using a difference-in-differences approach with the Callaway and Sant’Anna estimator, the authors estimate the heterogeneous effects on turnover and employment growth across SC and B2C categories and their subcategories (SC Local, SC Traded, B2C Local, B2C Traded). Findings SC start-ups experience a significant 29.2% increase in turnover alongside a significant 25.2% decline in employment; B2C start-ups show no significant effects on either outcome. Within subcategories, SC Traded industries drive the positive turnover result (31.9% increase). Event-study analysis confirms that effects emerge at loan receipt and remain positive one year after treatment, with no pre-treatment differences, and survival analysis shows no differential firm exit across sectors. Practical implications Public support programmes for start-ups should account for sectoral heterogeneity. Policymakers can use the SC/B2C framework as a tractable criterion to target resources towards the SC economy, where participative loans yield the largest marginal effect. Managers of B2B start-ups can interpret participative loans as an instrument that relaxes binding financial constraints and enables productivity-enhancing investments. Originality/value This study moves beyond traditional high-tech vs non-high-tech classifications, offering a novel framework for evaluating public entrepreneurial finance. The findings indicate that start-ups in the SC economy, particularly those serving traded markets, are more responsive to public financial support, offering new evidence for policymakers designing industry-sensitive support programmes.
Purpose This study examines how artificial intelligence (AI) influences employee emotional well-being by analysing the behavioural mechanisms linking autonomy, fairness, clarity, psychological safety and workplace happiness, with a specific focus on Iberoamerican organisational contexts.Design/methodology/approach A synthetic dataset (n = 650), calibrated to validated psychometric scales and qualitatively informed by exploratory interviews with organisational leaders and AI practitioners, is analysed using covariance-based structural equation modelling. The model evaluates how perceived AI support and AI pressure shape key organisational conditions and, through them, workplace happiness. All analyses are conducted in R using the lavaan package.Findings Perceived AI support positively influences autonomy, fairness and clarity, which jointly strengthen psychological safety. Psychological safety emerges as the strongest predictor of workplace happiness, while AI-related algorithmic pressure shows a significant negative association with employee well-being. The results indicate that managerial choices in the design and governance of AI systems directly shape emotional climates, engagement and the sustainability of digital transformation processes.Research limitations/implications The study relies on synthetic - but realistically calibrated - data, which may limit external generalisability. However, grounding the generative process in validated psychometrics and executive insights enhances contextual realism. Future research should incorporate longitudinal field data and cross-country comparisons. Conceptually, the paper advances happiness management by integrating behavioural constructs and AI governance, offering a scalable framework for studying well-being in digital workplaces.Practical implications The results provide actionable guidance for leaders implementing AI in human-centric organisations. Managers should prioritise autonomy, clarity of processes, psychological safety and fairness when deploying AI tools. The findings inform the design of governance structures, communication strategies and training programmes that mitigate emotional risks. The framework also helps firms identify conditions under which AI supports sustainable happiness, talent retention and positive emotional climates.Social implications The study contributes to the broader debate on ethical AI and decent work in Iberoamerica. It shows how AI adoption can either enhance or erode human well-being depending on governance quality. The results support policies aligned with the sustainable development goals (SDGs) - particularly SDG 3, SDG 8, and SDG 9 - by offering evidence-based insights into the emotional and social sustainability of AI-enabled workplaces.Originality/value The study introduces the concept of affective friction to explain employees' emotional responses to AI-enabled systems. By integrating human-centred AI design, psychological safety and happiness management within a unified behavioural-economic framework, the paper offers a culturally grounded contribution to organisational research in Iberoamerican contexts.
