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In the current study based on the resource-based view (RBV), a three-way interaction model tests the relationships among human and social capital resources, innovation orientation (IO) and innovation capability in the context of new ventures.

Hierarchical linear regression modeling presents the linear relations at two decision layers of start-ups, their founders and managers. Data is collected and analyzed from 233 new ventures in Turkey.

Findings of the two and three-way interaction analyses indicate a positive relationship between human capital and innovation capability when social capital and IO are high; however, the relation turns off when low.

The study extends the previous works on the proposed link between intellectual capital (IC) resources and innovation, by confirming the moderating role of social capital and IO on the positive association between human capital resources and innovation capability.

The results show that for start-up companies, the co-existence of strong social capital and the strategic orientation towards innovation is required for the effective utilization of human capital for generating innovation capability within the organization. Thus, this study highlights the importance of networks, alliances and social relationships, together with the unification of strategic thinking, organizational learning and a culture of innovation for attaining innovation goals, which are crucial for the survival and success of these units.

This study presents the first model in the literature which examines the moderating effects of IO and social capital on the human capital-innovation capability relationship.

Digital transformation (DT) has created a number of significant opportunities and related alterations in consumers' behaviours. However, consumers differ in adopting DT. There is still a need to investigate consumer's capabilities and his/her perception of other customers that supports or hinders the DT process. This study aims to investigate human thinking capabilities (critical thinking, holistic thinking and creative thinking) as drivers of two specific types of outcomes: DT barriers and/or DT nurture. In addition, this research also examines the linkage between human capabilities (three types of thinking) and other customers' perception. Finally, it postulates that other customers' perception drives DT barriers and/or DT nurture.

Applying to the public, private and international banking sectors, 273 questionnaires were collected. The collected data was analysed using AMOS as well as the PROCESS macro.

The results showed that human thinking capabilities have different effects on DT barriers and nurture. While critical thinking has a significant effect on DT barriers, both holistic thinking and creative thinking have a significant effect on DT nurture. Other customers' perception construct has a significant effect on both DT barriers and nurture. In addition, only holistic thinking has an effect on other customers' perception.

This study advances knowledge in three ways. Firstly, it examined DT barriers and nurture as variables from the customer's perspective. Secondly, it examined each human thinking capability on DT barriers and nurture, giving more insights. Thirdly, it studied human thinking capabilities as one of the main factors that might affect other customers' perception and DT barriers and nurture, while most previous studies focused only on other customers' perception's impact on DT barriers and nurture.

Technology is revolutionizing the management logic of service systems. The increasing use of artificial intelligence (AI), in particular, is challenging interaction between humans and machines changing the service systems’ value co-creation configurations and logic. To envision possible future scenarios, this paper aims to reflect upon how the humans’ use of AI technology can impact value co-creation.

The study is developed, at a conceptual level, using selected elements from managerial and marketing theoretical frameworks interested in value co-creation – Service-Dominant Logic, Service Science and Viable Systems Approach (VSA) – used as interpretative tools to reframe value co-creation in the digital age.

The interpretative approach adopted and, in particular, the new VSA notion of Intelligence Augmentation (IA), in the perspective of the information variety model, shed new light on value co-creation in the digital age framing a possible “IA effect” that can empower value co-creation in complex decision-making contexts.

The study provides insights useful in the design and management of service systems suggesting a rethinking of the view of AI as a means for mainly increasing the smartness of service systems and a new focus on the enhancement of the human resources contribution to make the service systems wiser.

The paper provides a refocused interpretative view of the interaction between humans and AI that looks at a possible positive impact of the use of AI on humans in terms of augmented decision-making capabilities in conditions of complexity.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

This study investigates how enhancing slum dwellers' capabilities influences their entrepreneurship development and contributes to urban poverty reduction, providing insights for social policy design.

A quantitative research design is adopted applying structural equation modeling to survey data from 585 beneficiaries of social welfare schemes across Indian slums.

Educational, economic and sociocultural capabilities positively impact quantitative and qualitative dimensions of slum entrepreneurship development, which reduces urban poverty, supporting the hypothesized relationships grounded in the Capability Approach.

The cross-sectional data limits causal inference. Wider sampling can improve generalizability. Capability antecedents of entrepreneurship merit further investigation across contexts.

Integrated policy initiatives focused on education, skill building, access to finance and markets can leverage entrepreneurship for sustainable urban poverty alleviation.

Enhancing slum dweller capabilities fosters entrepreneurship and empowerment, enabling people to shape their own destinies and reduce deprivations.

The research provides timely empirical validation of the Capability Approach and evidence-based insights to inform social policy aiming to alleviate urban poverty via entrepreneurship in developing countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0514.

The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.

This study aims to provide a detailed characterization of Argentinean exporting wineries using a new rich firm-level data set to understand how capabilities and business strategies differ among firms with different levels of involvement in the export market.

A survey was distributed among all wineries along all wine regions of Argentina; the 45-min questionnaire was answered by 230 wineries, a representative sample with a response rate of 26.3% of the total population of Argentinean wineries. The survey assessed the interaction between wineries’ dynamics and characteristics and their participation in export markets. In the comparative analysis, the results are presented by dividing the sample into four categories according to the export intensity of the wineries.

High-intensity exporting wineries in Argentina differ from other Argentinean wineries in several dimensions. In particular, the most internationalized Argentinean wineries are the most endowed with higher capabilities; they follow a specific business model emphasizing product differentiation, quality upgrading, brand building and the development of distribution channels. Exporting wineries from Argentina adopt business practices that differ from those that prevail among wineries that only target the domestic market. They have developed firm capabilities such as human capital and technology to play a critical role in quality upgrading for their participation in global wine markets.

To the best of the authors’ knowledge, this paper is the first to study the Argentinean exporting wineries using a firm-level sizeable representative sample.

The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.

The paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.

Some authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.

The main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338

This study aims to develop and test an original conceptual framework that examines the role of various factors borrowed from three theories (i.e. Institutional Theory, Resource-Based View and Diffusion of Innovation) in adopting Human Resource Analytics (HRA).

A new conceptual framework (research model) is developed based on previous research and coherent theoretical arguments. Its factors are classified using the Technology–Organization–Environment (TOE) framework. Research hypotheses are tested using primary data collected from 152 managers of Greek organizations. Empirical data are analyzed using the “Structural Equation Modelling” (SEM) technique.

The technological and organizational context proved extremely important in enhancing Organizational Analytics Maturity (OAM) and HRA adoption, while the environmental context did not. Relative advantage and top management support were found to significantly impact the adoption of HRA, while Information Technology (IT) infrastructure, human resource capabilities and top management support are crucial for increasing OAM. Overall, the latter is the most important factor in enhancing HRA adoption.

This study contributes to the limited published research on HRA adoption while at the same time it can be used as a guideline for future research. The novel findings offer insights into the factors impacting OAM and HRA adoption.

With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines.

We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected.

This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies.

This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.