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This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.

This paper aims to use the origin story of Dalhousie’s Faculty of Management as a foil for unpacking the tensions between deep disciplinary specialization and liberal education in business schools in Canada and the USA. Ultimately, the paper reveals that those tensions are not irreconcilable, and that through the fortunes of historical contingencies and deliberate decision-taking, a faculty can embrace the benefits of both breadth and depth.

The paper proposes a critical organizational history of management education through a case study. By drawing on secondary literature and archival sources, the authors focus on moments in business education, such as the founding of the Wharton School of Business, the release of the Carnegie and Ford Reports and the trend towards increased specialization to situate a case study of Dalhousie University’s Faculty of Management.

The authors find that the evolution of business education in North America from its broad, liberal origins towards narrow, specialization has come at a cost to some of the benefits of business and management education. An alternative approach, one reflected in the design of Dalhousie’s Faculty of Management, its programme offerings and its interconnection with other disciplines, enables the advantages of deep disciplinarity to co-exist (and cross-inform) with the advantages of liberal approach to knowledges.

The Dalhousie model offers business schools an example of a faculty that balances the rich insights of liberal interdisciplinarity with the need for sophisticated approaches to more granular, often disciplinary, topics. In addition, the paper offers the story of a multidisciplinary management faculty, some explanation for how that faculty was maintained despite pressures towards specialization; and in doing so, contributes to the limited historical research of management education, particularly in Canada, post-2000.

In Chapter 1, we critically reviewed the foundations of the free enterprise capital system (FECS), which has been successful primarily because of its wealth and asset accumulation potentiality and actuality. In this chapter, we critically argue that this capacity has been grounded upon the profit maximization (PM) theories, models, and paradigms of FECS. The intent of this chapter is not anti-PM. The PM models of FECS have worked and performed well for more than 200 years of the economic history of the United States and other developed countries, and this phenomenon is celebrated and featured as “market performativity.” However, market performativity has not truly benefitted the poor and the marginalized; on the contrary, market performativity has wittingly or unwittingly created gaping inequalities of wealth, income, opportunity, and prosperity. Critical thinking does not combat PM but challenges it with alternative models of profit sharing that promote social wealth, social welfare, social progress, and opportunity for all, which we explore here. Economic development without social progress breeds economic inequality and social injustice. Economic development alone is not enough; we should create a new paradigm in which economic development is the servant of social progress, not vice versa. Such a paradigm shift involves integrating the creativity and innovativity of market performativity and the goals and drives of social performativity together with PM, that is, from market performativity to social performativity.

This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the consumption behavior of urban white-collar women and riding on its community e-commerce advantage, “Red”, a cross-border e-commerce startup, pulled in three rounds of financing within just 16 months regardless of increasingly competitive market. On the other hand, well-established platforms such as T-mall International and Joybuy also stepped in, and their involvement will also speed up the industry integration and usher in a reshuffling period. Confronted with the “price war” started by those e-commerce giants, in what ways can “Red” adjust its shopping experience and after-sales services to enhance the brand value and sharpen its edge?

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

The disposition effect remains one of the most significant investor behavior puzzles. This study aims to consolidate the knowledge, explore current dynamics, elicit trends and offer future research directions to demystify the disposition effect.

This study applies the hybrid review method. It first used bibliometric analysis (212 documents), followed by content analysis (54 articles) to analyze the breadth and depth of literature on the disposition effect.

This study presents performance analysis and science mapping. It identifies five main research streams: evidence, implications and mitigation techniques; theoretical explanations; investor biases and hedonic framing; attributes, beliefs and preferences; and implications for asset pricing and market efficiency. This study further offers future research directions for disposition effect research.

This study deploys sequential bibliometric and content analysis. A meta-analysis of quantitative articles could provide specific insights regarding the disposition effect. Besides, this study is based on Scopus-indexed journals only.

This study benefits investors and portfolio managers as they learn effective ways to guard against the disposition effect. Policymakers may tweak tax laws to incentivize long-term holding, and regulators can run investor education campaigns to minimize the disposition effect’s consequences effectively.

To the best of the authors’ knowledge, this is probably the first hybrid review of high-quality, contemporary articles on the disposition effect that offers science mapping, research streams, future research directions and a succinct summary of theories, contexts, characteristics and methods deployed in the field of research.

The study analyzed how the service-learning (SL) methodology can become a university social responsibility (USR) strategy that generates shared value for the university and its stakeholders.

This qualitative and exploratory–descriptive research had a nonexperimental field and cross-sectional design that used field techniques such as focus groups and in-depth interviews with the participants of the USR program “MIPyME vs COVID-19” in two Latin American countries.

This study revealed the perceptions of students who participated in the USR program on how a virtual service-learning (vSL) strategy related to the use of technology generates a positive impact on the development of shared learning between students and micro-entrepreneurs from a global and Latin American perspective, as well as for the internationalization of their study plans.

The field study was conducted in only two Latin American countries; however, the USR program has been implemented in eight universities from eight Latin American countries with very similar social, political and health contexts.

It is one of the first studies on SL used in a strategic and articulated way in universities with a USR approach. It analyzed traditional evaluations of this methodology, incorporating others such as virtuality (produced by the effects of the COVID-19), which led the authors to generate methodological innovations based on new ways of connecting, linking and generating shared learning and value for all.

Investors who can transfer their savings to investments in a well-regulated market benefit not only themselves but also economic development. Hence, it is crucial for fund owners to evaluate their stock market investment decisions. The goal of the study is to understand which model determines the asset returns most efficiently. In this regard, the validity of single and multi-index asset pricing models (capital asset pricing model-CAPM and Fama–French models) has been examined in the Turkish Stock Exchange for 2009–2020, with the quantile regression (QR) approach.

On 18 portfolios comprised of quoted stocks in the Istanbul Stock Exchange 100 (ISE-100/BIST-100), we test the CAPM, the Fama and French three factor model (FF3) and the Fama and French five factor model (FF5). Empirical analyses have been carried out via QR approach regressing the portfolios' average weekly excess returns on risk premium/market factor (Rm-Rf), firm size, book value/market value (B/M), profitability and investments factors. QR estimation has been employed since QR is more effective and provides a better definition of the distribution’s tails.

Our empirical findings have revealed that the average excess weekly returns can be explained more strongly via CAPM. Moreover, Fama and French models are expected to give more reliable result with more data, whereas the market premium would give robust results for the Turkish Capital Market.

Individuals investing in financial assets must find the price model that best fits the market. The return can be approximated in the most appropriate manner using the right variables.

The study differs from other research by comparing the asset pricing models via examining the assets' weekly returns with QR in the Istanbul Stock Exchange 100 (ISE-100).

The aim of this study is to systematise the entrepreneurial coaching (EC) literature by understanding how the phenomenon has been investigated along four dimensions inspired by Gartner’s (1985) conceptualisation of entrepreneurship: entrepreneurial process, context, target and outcomes. In so doing, this study will provide a frame of its multifunctional role and identify relevant gaps and suggestions for future research.

We conducted a systematic literature review to collect existing works on EC. We analysed 85 selected papers with a qualitative content analysis that allowed us to highlight relevant research themes for the entrepreneurial process, context, outcomes and target.

Our results demonstrate that EC has a multifunctional role in entrepreneurship that can be summarised in five different typologies, depending on the stage of the entrepreneurial process in which it is applied, the context, the target and the outcomes.

As one of the first attempts to systematise studies on EC, this work extends previous conceptualisation of EC by detailing different typologies of this intervention, thereby contributing to reduced fragmentation and conceptual ambiguity.

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.