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Benefits of positive mental health have been demonstrated across work and non-work domains. Individuals reporting positive mental health experience better work performance, better social relationships and better physical health. Additionally, positive work environments can contribute to employee mental health. The purpose of this paper is to develop “expert” consensus regarding practical, actionable strategies that organisations can implement to promote positive mental health in the workplace.

A Delphi consensus method was used to establish expert consensus on strategies to promote positive workplace mental health. A 278-item questionnaire was developed and strategies were rated over three survey rounds by two panels comprising 36 workplace mental health practitioners and 36 employer representatives and employees (27 and 9, respectively), employees with experience of promoting positive mental health and well-being in the workplace (total – 72 panellists).

In total, 220/278 strategies were rated as essential or important by at least 80 per cent of both panels. Endorsed strategies covered the topics of: mental health and well-being strategy, work environment that promotes positive mental health, positive leadership styles, effective communication, designing jobs for positive mental health, recruitment and selection, supporting and developing employees, work-life balance, and positive mental health and well-being initiatives.

The guidelines arising from this study represent expert consensus on what is currently appropriate for promoting positive mental health at work from the perspectives of workplace mental health practitioners, employers and employees, and constitute a resource for translating the growing body of knowledge in this area into policy and practice.

By the end of this chapter, you should be able to demonstrate an understanding of:

The importance of big data in the information revolution.

The resource-based view of the firm and dynamic capabilities as they relate to big data.

The use of big data in marketing decisions.

Consumer security concerns over the storage and processing of big data.

This paper aims to propose a model for measuring the performance of knowledge management (KM) projects in enterprises. No such model has been proposed in the literature thus far. The activities, factors and outcomes of KM are the main constructs of the model. Their operationalization and interactions are investigated.

A survey was conducted of 120 respondents from SME firms in Morocco. A structural equation modeling (SEM) technique called partial least squares (PLS) was used to assess the validity of the constructs and verify the hypotheses. A performance index for KM projects was derived from the model constructs.

The results support the model designed for KM activities and related interactions. The effects of KM activities on its outcomes are significant as well. The results also confirm that KM factors are predictors of KM activities and that the effects of these are significant. Furthermore, a performance importance analysis (importance performance map analysis [IPMA]) was performed on the data to expand the results of the PLS-SEM by identifying under-performing KM drivers that require managerial action.

This paper is one of the first to propose a generic performance measurement model for KM projects. Additionally, it is a pioneering study in the use of IPMA for KM performance measurement.

This paper responds to a recent trend towards reifying “knowledge hoarding” for purposes of quantitative/deductive research, via a study of information technology (IT) service professionals. A “rhetorical theory” lens is applied to reconsider “knowledge hoarding” as a value-laden rhetoric that directs managers towards addressing assumed worker dysfunctionality.

A qualitative study of practicing IT service professionals (assumed within IT service management “best practice” to be inclined to hoard knowledge) was conducted over a 34-day period. Twenty workers were closely observed processing IT service incidents, and 26 workers were interviewed about knowledge-sharing practices.

The study found that IT service practice is characterized more by pro-social collegiality in sharing knowledge/know-how than by self-interested strategic knowledge concealment.

The study concerns a single occupational context. The study indicates that deductive research that reifies “knowledge hoarding” as a naturally occurring phenomenon is flawed, with clear implications for future research.

The study suggests that management concern for productivity might be redirected away from addressing assumed knowledge-hoarding behaviour and towards encouraging knowledge sharing via social interaction in the workplace.

Previous studies have not directly examined the concept of knowledge hoarding using qualitative methods, nor have they considered it as a rhetorical device.

First-generation college students in the United States are a unique demographic that is often characterized by the institutions that serve them with a risk-laden and deficit-based model. However, our analysis of the transcripts of open-ended, semi-structured interviews with 22 “first-gen” respondents suggests they are actively deft, agentic, self-determining parties to processes of identity construction that are both externally imposed and potentially stigmatizing, as well as exemplars of survivance and determination. We deploy a grounded theory approach to an open-coding process, modeled after the extended case method, while viewing our data through a novel synthesis of the dual theoretical lenses of structural and radical/structural symbolic interactionism and intersectional/standpoint feminist traditions, in order to reveal the complex, unfolding, active strategies students used to make sense of their obstacles, successes, co-created identities, and distinctive institutional encounters. We find that contrary to the dictates of prevailing paradigms, identity-building among first-gens is an incremental and bidirectional process through which students actively perceive and engage existing power structures to persist and even thrive amid incredibly trying, challenging, distressing, and even traumatic circumstances. Our findings suggest that successful institutional interventional strategies designed to serve this functionally unique student population (and particularly those tailored to the COVID-moment) would do well to listen deeply to their voices, consider the secondary consequences of “protectionary” policies as potentially more harmful than helpful, and fundamentally, to reexamine the presumption that such students present just institutional risk and vulnerability, but also present a valuable addition to university environments, due to the unique perspective and broader scale of vision their experiences afford them.

The increase in international trade, the advances in technology, the growing importance of the emerging markets are the main factors that have contributed to the explosion of counterfeiting experienced in recent years, estimated to be valued at about 5-7 per cent of the world trade. The luxury industry in Italy has been particularly hard hit and most brands nowadays are urgently looking for demand-side and supply-side strategies to track and control the phenomenon. The aim of this paper is to provide a supply chain view of counterfeiting and illegitimate trade phenomena, in a supply chain risk management perspective, to define and illuminate the interaction of the legitimate and the illegitimate supply chains.

The paper introduces the LISC model to represent and include all the illegitimate trade phenomena under analysis such as pure counterfeiting, factory overruns, grey and parallel market, supply chain infiltrations, product diversion and sale of stolen goods

The interrelations between legitimate and illegitimate supply chains are crucial to approach counterfeiting issue and define which illegitimate trade paths are more harmful to companies and customers.

The first limitation of the work is that the illegitimate trade categories defined in this paper mainly rely on data and phenomena collected from secondary sources that have not yet been directly observed by the authors. The second one is that a specific focus on high-end fashion industry was employed throughout this work: further analysis for evaluating the applicability and the significance of the illegitimate trade in other industries is still pending. The final limitation stems from the fact that it will be necessary to investigate the implications and the applicability of the model to the illegitimate on-line trade.

During the course of the MI-FIDO project, the model and the selection rules identified for illegitimate trade family classification were used as a basis for defining the rules for anomalies detection to be included in a “track and trace” system developed the project team currently under with a major Italian fashion brand.

To the authors ' knowledge, this is the first work that attempts to present a concise and systematic approach to luxury illegitimate trade from a supply chain perspective. Understanding which legitimate-illegitimate supply chain interactions are the most damaging will help fashion luxury and other industries to battle the counterfeiting phenomenon more effectively

The purpose of this paper is to examine how users on social media view brands with a heritage. Consumers commonly post opinions and accounts of their experiences with brands on social media. Such consumer-generated content may or may not overlap with content desired by brand managers. Drawing from “The medium is the message” paradigm, this study text-mines user narratives on Twitter1 to shed light on the role of social media in shaping public images of brands with heritage through the lens of the stereotype content model.

The study uses a data set of almost 80,000 unique tweets on 12 brands across six categories, compares brands high versus low in heritage and combines dictionary-based content analysis with sentiment analysis.

The results indicate that both user-generated content and sentiment are significantly more positive for brands low rather than high in heritage. Regarding warmth, consumers use significantly more positive words on sociability and fewer negative words on morality for brands low rather than high in heritage. Regarding competence, tweets include more positive words on assertiveness and ability for low-heritage brands. Finally, overall sentiment is more positive for brands low rather than high in heritage.

Important from co-creation and integrated marketing communication perspectives, the findings provide brand managers with actionable insights on how to more effectively use social media.

To the best of the authors’ knowledge, this research is among the first to examine user-generated content in a brand heritage context. It demonstrates that heritage brands, with their longevity and strong links to the past, need to be aware of how contemporary social media can detract from their image.

This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.

Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.

We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.

While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.

Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.

Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.