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This paper aims to focus on identifying various human enablers that could influence the performance of a firm using advanced manufacturing systems (AMSs), and to develop a framework to manage them through an analytical case study.

In this work, digraph and matrix technique through case study was utilized for finding the single numerical value, i.e. human enabler index (HEI), for quantifying the impact of human enablers of a firm under consideration using AMSs.

The major finding of this analytical case study is that a firm can identify the weak human‐related issues existing in their organizations. The HEI value for the considered organization is found to be low because it does not understand its corporate social responsibility, therefore the necessary decisions/actions may be taken for the performance enhancement and optimal utilization of the human element. In this way, the effectiveness of an AMS can be enhanced without big investments.

A larger number of human enablers could be considered for the analysis. However, the ranking has been done on the basis of the survey and expert opinion which may have an element of bias.

Analytical case study and the evaluated HEI could be used for quantifying the impact of human enablers in an AMS environment. This index can also be used to rank different organizations according to their capability to extract benefits in manufacturing from human characteristics.

The major and unique contribution of this research is to perform an analytical case study of a company using AMSs. Besides this, evaluating the HEI value for the organization, framework for comparison of two organizations and ways to improve its performance have been the unique contribution in its domain.

The purpose of this paper is to validate the enablers of bio-inspired model of organizational excellence proposed by Agarwal and Vrat (2015a). It is also aimed to test its usefulness as an instrument in evaluating the degree of organizational excellence achieved.

Survey methodology has been used to investigate the hypotheses. Data have been collected through a self-structured questionnaire with closed-ended questions. Structural equation modeling and analytic hierarchic process have been used to test and analyze the data.

The results of hypothesis testing indicated that out of 19 paths only ten were significant. However, in the revised model only six paths out of those ten significant paths were retained and three new suggested paths were incorporated to make the final model. Besides, calculation of relative weights of each enabler shows that being self-driven occupies the top-most rankings, supply chain optimization occupies the second rank and promptness and reliability and maintainability occupy the bottom ranking in driving performance excellence, being resulting outcomes.

The major limitations of this research are: verification of the proposed model is done on only two test companies, and results of which are compared with one excellence award model only. Besides, the score resulted for test companies are based on self-assessment by them and thus require further validation.

The validated model can be applied for assessing degree of organizational excellence achieved by any company. The results of the assessment would serve as key focus areas for improvement.

The research is first to benchmark the human body for assessing the degree of organizational excellence achieved. The uniqueness of this model is that the various parameters of excellence assessment which are derived from human body, perceived as a benchmark in being the most well managed system.

Humanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.

Total interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.

GHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.

One noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.

To the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

The generation of new ventures within established companies, also known as corporate entrepreneurship (CE), is a process influenced by a set of individual and organizational factors. This paper aims to focus on creativity and human resource management enablers of CE, with the purpose to define an integrative framework and draw a set of related research propositions.

The paper relies on a multidisciplinary literature review in the fields of CE, creativity and organizational innovation.

The effectiveness of CE depends on a set of individual factors, distinguished into professional and psychological characteristics, and organizational factors, which include the system of values of the organization and the management practices applied in the same.

From a theoretical point of view, the paper develops an integrative framework of conditions that impact on CE and outlines a set of propositions and alternative research methods to test.

From a practitioner perspective, the study provides managers with a comprehensive set of factors enabling CE by leveraging the creativity of individuals and make it flourish through consistent human resource management practices.

The value of the paper stays in the integration of individual-related and organizational-related determinants of entrepreneurial performance.

This paper aims to examine the views of the global knowledge management (KM) community on the research area of KM and business performance and identify key future research themes.

An interview study spanning 222 informants in 38 countries was launched to collect data on KM expert views concerning the future research needs of the KM field.

The value contribution of KM requires more research despite experts agreeing on the complexities involved in solving this challenge. Further research areas identified were related to the influence of KM to support business strategy, intellectual capital, decision-making, knowledge sharing, organizational learning, innovation performance, productivity and competitive advantage.

The sample is dominated by European-based KM experts and the self-selecting sampling approach that was used by relying on the networks of each partner could have biased the structure of this sample.

The recognition of the complexity to demonstrate the value contribution of KM could prevent practitioners from using over-simplified approaches and encourage them to use more advanced measurement approaches.

The paper is unique, in that it reports on the views of 222 KM experts from 38 countries representing both academia and practice, on the issue of future research needs in terms of KM and business outcomes. As such it provides valuable guidance for future studies in the KM field and related subjects.

This study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling.

A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region.

This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization.

Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence.

This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

The paper aims to analyse the contextual relationship and dependency amongst enablers for lean manufacturing implementation in Bulgarian small and medium-sized enterprises (SMEs).

In this study, the interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for enablers. Also, the interpretive ranking process (IRP) was used to analyse and rank enablers with reference to performance variables. For the ISM approach, a structural self- integration matrix was developed with the help of experts’ suggestions and opinions. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship amongst enablers. A total of nine experts were chosen for collecting the primary data in which seven experts belong to the industry and two experts were academicians. The dominant relationship amongst the enablers was analysed through IRP modelling.

A total of 11 enablers were identified for the purpose of this study. The model shows that “leadership and commitment by management”, “human resource management”, “customer relation management”, “supplier relation management” and “information technology system” are the most significant enablers for lean implementation in Bulgarian SMEs as these are positioned at the bottom levels in ISM model. MICMAC analysis shows that five enablers fall in the independent factor, two enablers in linkage factor and four enablers in the dependant factor while there is no enabler in the autonomous factor. ISM and IRP models show that “continuous improvement” is an essential enabler for the successful implementation of lean in Bulgarian SMEs. This study also helps to explain the comparative analysis of ISM and IRP, which indicates that IRP is a more robust modelling approach than ISM, as it incorporates the relationship of enablers with performance variables.

ISM and IRP modelling approaches are based solely on expert opinions and responses. This limitation can be overcome with the help of empirical study.

This study supports the professionals/experts to prioritise and manage enablers at strategic and tactical levels while implementing lean manufacturing practices in Bulgarian SMEs. The models developed in the study will be helpful for practitioners to understand and analyse the interdependence of enablers for lean manufacturing implementation.

This study helps to identify and prioritise enablers that affect lean manufacturing adoption using ISM and IRP approaches. Literature shows that numerous authors have used the ISM approach but the use of IRP approach is limited. The models were developed in the study, totally dependent on data collected from the experts to ensure their real-life validity.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

This study aims to investigate the intellectual capital (IC) dynamics in entrepreneurial ecosystems (EEs) by conceptualizing EEs as systems whose purposes include the (re)generation of the intangible resources needed for effective entrepreneurship. The study proposes a taxonomy of the key enablers of IC and develops a model that captures the unfolding interdependencies across the enablers of explorative and exploitative human, relational and organizational capital in EEs.

The authors purposefully selected a successful EE around an entrepreneurial university in Latin America as empirical case. Specifically, they investigated the IC dynamics of the EE in the context of a series of internationally awarded hackathons and related activities organized at the ecosystem level over three years (2019–2021). The research leveraged participant observations, in-depth interviews with multiple EE actors and archival documents. For the data analysis, this study combined the Gioia method with an abductive approach.

First, the study identified 27 operationalizable enablers of IC at the EE level clustered into human, relational and organizational capital and further differentiated these enablers considering the explorative and exploitative facets of IC. Second, it determined that the dynamic interplay across IC enablers is critical for an EE to flourish and evolve adaptively. This study concludes that assessing IC enablers and their dynamics can aid the understanding, evaluation and management of EEs.

To the best of the authors’ knowledge, this study is the first to conceptualize EEs as systems whose purposes include creating the conditions for an effective interplay of explorative and exploitative IC at the system level. The resulting taxonomy of IC enablers provides formative constructs that will benefit both scholars and practitioners investigating the crucial role of intangible resources in EEs and guide managers, entrepreneurs and policymakers in their decision-making processes.