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The purpose of this paper is to investigate whether social relations are associated with the health of workers. It uses two types of health status measures – self-reported and more objective health – and it considers two types of social relationships: individual social relations, measured through the frequency of meetings with friends; and contextual social relations, the average frequency with which people meet friends at the community level.

A probit model is estimated from the worker sample accounting for the possibility of selecting individuals in the labour market (selection equation). Then expanded probit models (including inverse Mills ratio) are used on both self-reported and more objective health measures using new data from an income and living conditions survey carried out in 2006 by the Italian Statistics Office. Robustness checks are employed to deal with possible problems when interpreting the results.

The study finds that social relations are correlated with health status of workers with differences among health outcomes. Social relations at the individual level are positively correlated with self-perceived health (SPH), negatively associated with chronic condition (CC) but not related to limitations in daily activities. Contextual social relations are negatively linked with CC and limitations in daily activities but not correlated with SPH.

Although the results are consistent with the argument that individual and contextual social relations influence workers’ health, the author cannot prove causality.

Improving the health of workers could reduce health inequalities and could increase work performance. The implication at a macro-economic level of an improvement in the health conditions of workers is relevant in Italy, where the level of labour productivity is low compared to the other developed countries (OECD, 2013). Policy makers should consider the benefits, both at social and economic level, of public policies designed to improve the social and physical infrastructure of social relations.

This paper is the first to relate individual and contextual social relations simultaneously to workers’ health. Moreover, it makes several other contributions to this area: it control for unobserved worker heterogeneity; it uses both subjective self-reported health as well as a more objective measure of health based on CC and limitations in activities of daily living; it adopts a multilevel approach to examine in the same framework the individual and contextual relationship of social relations with individual health status of workers, in so doing, filling a gap in the literature on social capital and public health.

To propose a comprehensive and semi‐automatic method for constructing or updating knowledge organization tools such as thesauri.

The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow NLP with a clustering algorithm and an information visualization interface. The resulting system TermWatch, extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a 2D using an integrated visualization tool.

The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations.

This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. TermWatch can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations.

This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge‐oriented tasks like science and technology watch, textmining, query refinement.

Vast volumes of rich online consumer-generated content (CGC) can be used effectively to gain important insights for decision-making, product improvement and brand management. Recently, many studies have proposed semi-supervised aspect-based sentiment classification of unstructured CGC. However, most of the existing CGC mining methods rely on explicitly detecting aspect-based sentiments and overlooking the context of sentiment-bearing words. Therefore, this study aims to extract implicit context-sensitive sentiment, and handle slangs, ambiguous, informal and special words used in CGC.

A novel text mining framework is proposed to detect and evaluate implicit semantic word relations and context. First, POS (part of speech) tagging is used for detecting aspect descriptions and sentiment-bearing words. Then, LDA (latent Dirichlet allocation) is used to group similar aspects together and to form an attribute. Semantically and contextually similar words are found using the skip-gram model for distributed word vectorisation. Finally, to find context-sensitive sentiment of each attribute, cosine similarity is used along with a set of positive and negative seed words.

Experimental results using more than 400,000 Amazon mobile phone reviews showed that the proposed method efficiently found product attributes and corresponding context-aware sentiments. This method also outperforms the classification accuracy of the baseline model and state-of-the-art techniques using context-sensitive information on data sets from two different domains.

Extracted attributes can be easily classified into consumer issues and brand merits. A brand-based comparative study is presented to demonstrate the practical significance of the proposed approach.

This paper presents a novel method for context-sensitive attribute-based sentiment analysis of CGC, which is useful for both brand and product improvement.

Lean and green strategies are good options to increase the environmental and operational performance of manufacturing industries. The purpose of this paper is to identify the critical success factors (CSFs) to implement green lean practices (GLPs) in manufacturing industries through the review of the literature and to develop a conceptual model after analysing the fundamental facilitating factors by using ISM-MICMAC approach.

The methodology consists of identifying 12 critical success factors (CSFs) for the green lean implementation by reviewing the relevant available literature. The views of eight experts are valued for inter-relationships of these factors. ISM-MICMAC approach is used for analysing the relations between factors and to develop a conceptual model for green lean implementation.

Twelve CSFs are identified through a review of the literature to adopt GLPs in manufacturing industries. This paper has established the relationships among 12 identified CSFs using the ISM methodology. This paper analyses the dependence power and the driving power of identified CSFs with the help of MICMAC analysis. “Top management commitment” and “Government support” are the most significant CSFs implement GLPs successfully.

The ISM model presented in this study is based on expert opinions. But expert opinions may be biased as these are based on their own judgements. However, the proposed ISM based model needs statistically validations. The ISM model in the present study may be tested in real-world situations of manufacturing industries where results obtained may be different.

This study may provide a useful input for academicians and managers of industries to differentiate between independent and dependent CSFs and their mutual relationships which would help them to focus on those key CSFs that are most significant to implement GLPs.

A conceptual ISM model of identified CSFs shows the different levels of these CSFs. This model may help the manufacturer to implement the green-lean strategies. It may also support policymakers towards adopting GLPs. Arranging CSFs in a hierarchy and to categorise the CSFs into different levels with the help of ISM-MICMAC is an exclusive effort in the area of green lean engagement.

Green human resource management (GHRM) is an arising issue for the tannery industry in the context of developing economies. As the tannery industry can be seen as one of the highest polluting industries on earth, it becomes imperative for the industry to implement GHRM practices for greening the workforce. In this context, the purpose of this paper is to focus on antecedents that will support the implementation of GHRM practices in the tannery industry supply chain.

In this study, an expanded literature review was organized to establish antecedents for implementing GHRM practices. The total interpretive structural modeling (TISM) technique is employed to explore interactions among the identified antecedents. Furthermore, Matriced Impact Croises Multiplication Applique analysis was conducted for determining the driving-dependence power of each antecedent.

The results revealed that “green selection facility,” “green recruiting facility,” “green organizational culture,” “green purchasing,” “green strategy towards ES,” “regulatory forces towards ES” and “top management commitment towards greening the workforce” are the key antecedents for the exercise of GHRM practices in the tannery industry.

The proposed model might support decision makers to understand the interactions among the antecedents of GHRM practices. This model will help managers to understand the impact of one antecedent on another prior to the implementation of GHRM practices in the tannery industry.

In this study, the author(s) propose a new version of the interpretive structural modeling approach (ISM), named the TISM technique, for determining the contextual interactions between GHRM initiative antecedents that are very new in the existing literature.

The study aims to identify, analyse and develop a model for measuring the inter-relationship and interaction among the inter-partner factors. International joint ventures (IJVs) literature has scantly studied the interactions and inter-relationships among the inter-partner factors of IJVs performance. To address this gap, this research creates a hierarchical relationship framework among the inter-partner factors of IJVs performance.

The comprehensive literature review is used to identify the factors and possible relationships between the factors and IJVs performance. M-TISM (modified total interpretive structural modelling) methodology is used to examine the relationship among the factors of IJVs performance.

This study highlights 12 inter-partner factors that affect IJVs performance. The results suggest that size asymmetry, commitment, goal incongruency, competitive overlap, trust, control, the interdependency of resources, cooperation, economic distance, cultural distance, geographical distance and administrative distance significantly impact IJVs performance. Commitment is found to be a linkage variable as it has high dependence and driving power. Goal incongruency and competitive overlap have high driving power.

This study gives a proposed list of critical inter-partner factors for policymakers and practitioners to consider when developing rules or recommendations to improve IJVs performance. The focus of policymakers should be on economic and geographical distance, followed by cultural distance, competitive overlap, goal incongruency, size asymmetry and administrative distance to improve inter-relationship among the partners in IJVs. The work contributes to the academician by presenting a modified TISM model that highlights the significance of ambidexterity in driving the performance of IJVs.

This study fills a gap in the literature by analysing the interactions among the inter-partner parameters that influence the performance of IJVs. It offers insights into the variables, such as driver dependence and the relationships between the variables.

The term “social responsibility” bears different meanings among nations. Most studies on corporate social responsibility are conducted in Western countries, and literature shows a dearth of scientific research on the subject in developing countries. Meanwhile, the public sector, with its essential role in national development and social welfare, has been the focus of limited studies on social responsibility. In Iran, as a developing country, the concept of social responsibility has not yet found its rightful place inside organizations and companies. Therefore, the purpose of this paper is to design a multi-level social responsibility model in the Iranian Social Security Organization (SSO), the largest public organization in the country.

In this paper, the thematic analysis was used along with interpretive structural modeling (ISM) to design a multi-level social responsibility model in a specific context. It was a field survey with 20 open interviews. The direct relationships between variables of different levels, identified via thematic analysis and ISM, were investigated in a study population of 510 SSO stakeholders using structural equation modeling (SEM).

In the designed model, service improvement and optional and voluntary activities variables were identified in the first level; economic and financial activities in the second level; political activities and education and research variables in the third level; and finally, laws and regulations, organizational components and organizational culture variables were identified in the fourth level. The results of SEM revealed the direct influence of all lower-level variables on their higher level counterparts, except the influence of laws and regulations on political activities. Based on the results, organizational components had direct effects on education and research (ß = 0.630), and political activities (ß = 0.341), the variable of organizational culture affected education and research (ß = 0.176) and political activities (ß = 0.613), the variable of political activities affected economic and financial activities (ß = 0.633), the variable of education and research affected economic and financial activities (ß = 0.381), the variable of economic and financial activities affected service improvement (ß = 0.925) and optional and voluntarily activities (ß = 0.877) and the variable of laws and regulations affected education and research (ß = 0.151).

The research results showed that social responsibility was a dynamic and context-based phenomenon, which should be used in accordance with the Iranian organizations and national conditions. According to the results, a qualitative research plan can give new insights into social responsibility through the phenomenological constructivism approach. Policymakers could perceive the subject better and take more effective actions by identifying the activities of social responsibility at the Iranian SSO from the perspective of key stakeholders and analyzing the interplay between them.

This model is the first designed and developed at the Iranian SSO by considering the expectations of major stakeholders affecting the SSO. Another advantage of this model is the use of qualitative approaches in model development, which increased integrity of the proposed model. Dimensions of the model showed the main bottlenecks hindering the realization of SSO responsibilities. Finally, this study recommends to the empirical researchers of social responsibility to consider the institutionalized conditions of every society in their work.

The purpose of this paper is to explore the utility of a pragmatist epistemology as a viable methodological avenue for addressing the challenges associated with the normative models of science that dominate organisational management. At the same time, theorisation of a paradigm grounded in a pragmatic epistemology is presented.

The paper seeks to explore the different assumptions guiding a pragmatist epistemology, based on the development of an alternative philosophic framework, such as an indigenous paradigm that draws its logic from a Māori worldview. In doing so, it counters the ideological tension created by the disjunction of applying an epistemological perspective that aligns with what we conceptualise as a mainstream Western view of knowledge creation and maintaining the integrity of taking an indigenous worldview.

In the paper, we argue that kaupapa Māori research, as an indigenous paradigm draws from a pragmatist epistemology, providing a platform for a culturally attuned response to mainstream organisational research.

The paper is of use to qualitative researchers, in and beyond indigenous contexts, as it grounded in a methodological approach that draws from a pragmatic epistemology offering insightful, more richly contextualised research avenues in organisation and management.