Business Review Research Articles (Page 1)

Abstract The case study method of learning has long been a teaching approach that uses a form of experiential learning to allow students to make the connection between classroom learning and real‐world applications. Cases such as those published by the Harvard Business Review utilize detailed historical information, financial data, and other relevant data, while others are created to highlight an existing set of problems or issues that also support or enhance the traditional lecture. In this paper, we present a course concept that provides a framework for multiple learning experiences where case studies build on core knowledge to prepare the student for real‐time experiential learning opportunities based on live interactions with experts during company visits. The case studies serve to bridge the gap between traditional lectures and company visits and are complemented by the FM risk management game, online quizzes, and pre‐visit company research projects. While our paper is based on an Enterprise Risk Management course in a specific geographic market, we believe that the framework of online learning, case studies, and on‐site experiential learning is applicable in different locations and settings.

This systematic literature review examines the convergence of technological innovation with climate change mitigation, emphasising the resultant business and economic ramifications. This analysis examines 61 peer-reviewed, open-access papers from 2015 to 2025, following the PRISMA technique to synthesise information regarding the application of digital and green technologies across industries in tackling climate challenges. The findings indicate that advancements like artificial intelligence, blockchain, Internet of Things IoT, big data analytics, and renewable energy technologies fulfill two functions: promoting environmental sustainability and bolstering corporate competitiveness. Technological integration has enhanced ESG performance, enabled circular economy practices, and promoted low-carbon transitions across sectors including as manufacturing, energy, logistics, and finance. The research indicates that digital transformation, in conjunction with contextual governance, organisational preparation, and strategic leadership, serves as a catalyst for sustainable innovation. Green financial instruments, such as carbon trading schemes, green bonds, and fintech platforms, serve as facilitators for connecting investments with sustainability objectives. The study provides a thorough understanding of the strategic, economic, and operational mechanisms via which technology enhances climate mitigation goals and fosters sustainable development in various socio-economic contexts.

Abstract This diachronic study analyzes engagement features — epistemic positioning and interactivity in particular — in the business case studies published by the Harvard Business Review (HBR) over the last century (1922–2023). Our study is based on a small, specialized corpus of HBR case studies, in which three periods (1922–1929, 1961–1979 and 2008–2023) in which we compare the frequency of epistemic and interactivity features. After reviewing the literature on the expression of positioning and stance in specialized contexts, we conceptualize business case studies as a register, and we identify a set of features by adopting a corpus-driven approach thanks to a semantic tagger. Our corpus-driven, quantitative analysis highlights sharp differences in the frequency patterns as well in the functions they fulfill in the discourse.

Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis.

Book Review: Net Positive: How Courageous Companies Thrive by Giving More Than They Take <i>Polman, P., &amp; Andrew, S. (2021).</i> Winston: Net positive: How courageous companies thrive by giving more than they take. <i>Harvard Business Review Press. ISBN-13: 978-1647821302, ISBN-10: 1647821304.</i>

As AI rapidly advances, it is sparking global debates across industries. In business, two key challenges have emerged: leaders face pressure to adopt AI for innovation and competitiveness, while employees fear job loss due to automation. These strategic and existential concerns define a central dilemma for modern organizations. Therefore, one of the top 50 highly recommended books by Harvard Business Review, The AI-Savvy Leader: Nine Ways to Take Back Control and Make AI Work by David De Cremer is a timely and necessary guide to address these two tensions. As AI technologies increasingly shape strategic decisions, organizational operations, and workforce dynamics, the need for informed, responsible, and visionary leadership becomes critical (Davenport &amp; Ronanki, 2018).

ABSTRACT In this paper I analyse the voices of higher and vocational education practitioners and stakeholders in the micro-credentials arena to answer the research question: What are the possible affordances, complexities and limitations of micro-credentials? Micro-credentials are small pieces of recognised learning and assessment (European Commission, 2020) that can function as an agent of change for better or worse (Desmarchelier &amp; Cary, 2022, Gibson et al., 2016, Hanshaw, 2024, McGreal &amp; Olcott, 2022, Pollard &amp; Vincent, 2022, Ralston, 2021, Wilson et al., 2016). There is a gap in the literature on the possible affordances, complexities and limitations of micro-credentials experienced in practice and following the voices of practitioners’ lived experience points bring us to understanding new ways of doing things (Clandinin &amp; Connelly, 2000). My data collection involved semi-structured interviews with ten participants from Aotearoa New Zealand and Canada who were experts or stakeholders in micro-credentialing development. By using Reflexive Thematic Analyses and Qualitative Descriptive Research, I uncover and present themes, which indicate multiple powerful and positive affordances which act as catalysts to micro-credential development, and numerous associated complexities/limitations which act as inhibitors, and investigate the relationship between them. Looking through the lenses of power/knowledge, which is practised in society as a strategy to exert control over others (Foucault, 1980) and disruptive innovations, which create footholds in markets where no market existed, (Christensen et al., 2015), I explore a possible motivational context behind these inhibitors. Finally, I propose how we might better leverage the successful build out of powerful micro-credentials, to the betterment of the human experience. REFERENCE LIST Clandinin, D.J. &amp; Connelly, F. M. (2000). Narrative inquiry: experience and story in qualitative research. Jossey-Bass. Christensen, C. M., Raynor, M., &amp; McDonald, R. (2015). What is disruptive innovation? Harvard Business Review. Reprint R1512B Desmarchelier, R., &amp; Cary, L. J. (2022). Toward just and equitable micro-credentials: an Australian perspective. International Journal of Educational Technology in Higher Education, 19(1), 25. https://doi.org/10.1186/s41239-022-00332-y European Commission. (2020). European Commission expert report on micro-credentials. European Commission. Foucault, M. (1980). Power knowledge: Selected interviews and other writings. Harvester Press. Gibson, D., Coleman, K., &amp; Irving, L. (2016). Learning journeys in higher education: designing digital pathways badges for learning, motivation and assessment. In D. Ifenthaler, N. Bellin-Mularski, &amp; D. K. Mah (Eds.), Foundation of digital badges and micro-credentials (pp. 115–138). Springer International Publishing. Hanshaw, J. (2024). Micro-credentials in higher and vocational education: an innovation or a disruption? A review of the literature. Journal of Applied Learning and Teaching, 7(1), 256-265. https://doi.org/10.37074/jalt.2023.7.1.39 McGreal, R., &amp; Olcott, D. (2021). Micro-credentials landscape report: transforming workforce futures: strategic perspectives and practices for university micro-credentials. Unpublished Manuscript. https://auspace.athabascau.ca/handle/2149/3655 Pollard, V., &amp; Vincent, A. (2022). Micro-credentials: A postdigital counternarrative. Postdigital Science and Education, 4(3), 843–859. https://doi.org/10.1007/s42438-022-00311-6 Ralston, S. J. (2021). Higher education’s microcredentialing craze: a postdigital-Deweyan critique. Postdigital Science and Education, 2021 (3), 83-101. https://doi.org/10.1007/s42438-020-00121-8 Wilson, B. G., Gasell, C., Ozyer, A., &amp; Scrogan, L. (2016). Adopting digital badges in higher education: Scoping the territory. In Ifenthaler, D., Bellin-Mularski, N., Mah, D. K. (Eds.) Foundation of Digital Badges and Micro-credentials (pp. 163-177). Springer International Publishing.

As we move into the artificial intelligence decade, with more emphasis on creativity and innovation, it will be important to ask questions in a different way. It will be critical for leaders and faculty to think about questions they haven't asked in the past. The 2024 research of Chevallier et al., which has been reported in the Harvard Business Review, is applied to nursing. [J Contin Educ Nurs. 2025;56(7):267-269.].

Correction to “<i>Business and Society Review</i>, volume 130, issue S1”

Background and Aims: Enhanced investor scrutiny, regulatory obligations, consumer lobbying, and the obvious effects of social injustice and climate change have all significantly enhanced the relevance of environmental and social responsibility in business. Ethical consumption, environmental principles, and ESG investing have all worked together to transform how businesses operate and engage with the public. In addition to evaluating theoretical frameworks like stakeholder theory, CSR, and ESG. Thus, this paper aims to consolidate existing knowledge, highlight trends, and identify research gaps in Ethical Edge. Methodology: A qualitative documentary research technique was used to investigate company reports, case studies, regulatory frameworks, and peer-reviewed academic literature for themes. The UN SDGs, the Harvard Business Review, the IPCC, and publications from top ESG assessment organizations are examples of core sources. Results: The findings demonstrate the growing significance of supply chain ethics and DEI, the positive correlation between ethical leadership and business resilience, and the strategic importance of environmental responsibility in sectors such as technology, fashion, and the automotive sector. ESG-aligned companies generally do better than their counterparts in terms of market value, client loyalty, and talent retention. However, there is still criticism of worldwide enforcement disparities, conflicting ESG standards, and greenwashing. Conclusion: Ethical and sustainable business practices are no longer optional; they are now crucial to long-term viability and stakeholder trust. The future of business depends on integrated solutions that take social justice, environmental responsibility, and ethical governance into account. Continuous innovation, technology integration, and critical research are essential to advancing this shifting paradigm.

PurposeThis study explores the influence of fake reviews, with a specific focus on non-reviews, on consumer decision-making and the credibility of e-commerce platforms. Utilizing advanced natural language processing (NLP) and machine learning techniques, the research develops a detection model designed to identify and filter irrelevant reviews, thereby strengthening the reliability of online review systems and fostering consumer trust.Design/methodology/approachThis study employs heterogeneous review corpora to implement NLP and machine learning techniques, including text feature analysis, corpus construction and classification models. Data were collected from Google business reviews via web scraping. The methodology encompasses data preprocessing, feature extraction and model training using support vector machine (SVM), Logistic Regression and Naive Bayes classifier, with performance evaluated through confusion matrices and F1 scores.FindingsThe study confirms that the proposed model effectively identifies non-reviews among fake reviews with a high degree of accuracy. Experimental results reveal that the Naive Bayes classifier achieves an F1 score as high as 0.997, with exceptional performance in hotel reviews. Moreover, the findings highlight the superiority of the bag-of-words model in capturing intricate review details and effectively detecting fake reviews. By defining opinion sentences and emphasizing detailed feature extraction, the study significantly improves detection accuracy and model robustness across diverse review types.Originality/valueThis research enhances fake review detection by focusing on non-reviews and using detailed feature extraction methods. Innovative NLP and machine learning techniques, such as opinion sentence and term frequency analysis, improve the identification of fake reviews. This study advances the technology for distinguishing genuine reviews and contributes to the healthy development of e-commerce. The findings are expected to improve the online shopping experience and business decision-making, benefiting both consumers and merchants.

Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model’s internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp’s ranking algorithm to rank businesses in a neighborhood. Due to Yelp’s substantial popularity and its high impact on local businesses’ success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp’s business ranking and review recommendation system. We examine three hypotheses to assess if Yelp’s recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp’s business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants’ location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.

The Economic Footprint of AI: A Systematic Review of Business and Development Literature

With the rapid growth in social network comments, the need for more effective methods to classify their polarity—negative, neutral, or positive—has become essential. Sentiment analysis, powered by natural language processing, has evolved significantly with the adoption of advanced deep learning techniques. Long Short-Term Memory networks capture long-range dependencies in text, while transformers, with their attention mechanisms, excel at preserving contextual meaning and handling high-dimensional, semantically complex data. This study compares the performance of sentiment analysis models based on LSTM and BERT architectures using key evaluation metrics. The dataset consists of business reviews from the Yelp Open Dataset. We tested LSTM-based methods against BERT and its variants—RoBERTa, BERTweet, and DistilBERT—leveraging popular pipelines from the Hugging Face Hub. A class-by-class performance analysis is presented, revealing that more complex BERT-based models do not always guarantee superior results in the classification of Yelp reviews. Additionally, the use of bidirectionality in LSTMs does not necessarily lead to better performance. However, across a diversity of test sets, transformer models outperform traditional RNN-based models, as their generalization capability is greater than that of a simple LSTM model.

As editors of the American Business Review (ABR), we write this editorial at a moment of global inflection. ABR has prioritized research that matters—not only to academic scholars, but to business practitioners, students, and policy observers. The new policy directions under the Trump 2.0 administration offer a reconfigured environment, where global trade, education, immigration, and enterprise are undergoing rapid recalibration. These changes call for rigorous academic engagement, not ideological entrenchment. Our role at ABR is not to advocate political positions. Rather, we aim to encourage and curate scholarship that interprets, questions, and informs real-world business dynamics. This new era, characterized by reciprocal tariffs, changing immigration enforcement, and a revived debate around Diversity, Equity, and Inclusion (DEI), presents precisely such a moment.

In today’s hyper-competitive and transparent business landscape, company reputation has emerged as a critical intangible asset that influences consumer trust, employee engagement, and organizational success. This paper explores the strategic role of company reputation management through the enhancement of workspace environment and organizational culture. Reputation is shaped not only by external perceptions but also by internal practices that reflect employee satisfaction, cultural coherence, and ethical governance. The study examines how factors such as collaborative workspaces, cultural alignment, crisis management, and internal communication significantly impact stakeholder trust and corporate image. Through qualitative analysis of employee feedback, social media sentiment, and unstructured business reviews, the research highlights practical approaches to data-driven reputation monitoring. Moreover, it discusses the challenges and risks of data misinterpretation, resource limitations, and crisis unpredictability. The findings underscore that a structured and proactive reputation management framework—rooted in employee well-being and cultural resilience—not only mitigates risks but also fosters innovation, loyalty, and sustainable growth.

With the trend of economic globalization becoming more and more obvious, many enterprises nowadays adopt different ways to expand the scope of their business operations. Among them, investment, mergers and acquisitions (M&amp;A) of enterprises in other countries are the most significant. Foreign mergers and acquisitions, investment in the host country to bring a positive impact on the stimulation of domestic industry, but if not controlled may endanger national security, in this case, different countries have adopted different ways of foreign business review. And China as a rapidly developing economy in recent years, his foreign investment system is worth studying, but there are still some defects. After analyzing the system of each country, we can make an outlook on the global economic development.

Abstract Incumbent large businesses play a crucial role in fostering resilient entrepreneurial ecosystems. However, many regions lack the presence of such firms. Early research suggests that an entrepreneurial ecosystem can compensate for this absence through positive spillovers from foreign direct investment. While the role of multinational enterprises in entrepreneurial ecosystems remains underexplored, our comprehensive review of the broader business and economics literature delineates and describes six key multinational enterprise spillover mechanisms that impact entrepreneurial ecosystem genesis and evolution. We contribute a theoretical model of six multinational enterprise spillover mechanisms’ impact on entrepreneurial ecosystem evolution from genesis, through growth, and on to maturation as a more resilient entrepreneurial ecosystem. We ultimately propose a future research agenda to further and more deeply explore the impact of multinational enterprises’ spillovers on entrepreneurial ecosystems.

Systematic reviews (SRs) in the Business and Management (B&M) literature differ from SRs in other disciplines, particularly compared to reviews in the medical/health field where SRs originated. In this analysis, we document some of the commonly cited methodological literature used by B&M researchers. We note some of the differences in the approaches and guidance between the B&M methodological literature and the medical/health methodological literature. Second, we examine how SR methods were reported by B&M researchers. This analysis identifies areas that can benefit from improved reporting and makes recommendations for librarian supporting B&M researchers conducting SRs.

Abstract In 1974, Business Society and Review/Innovation (BSR) published “The Future of Capitalism: A Symposium,” in which six experts predicted the future of the US economic system. These experts agreed that in the future, capitalism would still be the primary economic system of the United States, but the government would play a more significant role. In 1975, Martin and Lodge surveyed Harvard Business Review (HBR) subscribers about the ideological underpinnings of capitalism and their predictions. Similarly, these subscribers believed that capitalism was dominant then but would evolve into a socially capitalistic format in the future. We updated their work fifty years later by surveying 1,635 managers and professionals in the US. Our findings suggest a growing preference for an ideology that retains some of the fundamentals of capitalism but modifies others. We propose polarity thinking as a framework that may best explain how the future US economy might be both capitalistic and communitarian without being socialist. This quasi‐capitalist form is developing, but the specifics of its implementation in the future are unclear.