British Journal of Management最新文献

Shuang Ren, Riikka M. Sarala, Paul Hibbert

The advent of generative artificial intelligence (GAI) has sparked both enthusiasm and anxiety as different stakeholders grapple with the potential to reshape the business and management landscape. This dynamic discourse extends beyond GAI itself to encompass closely related innovations that have existed for some time, for example, machine learning, thereby creating a collective anticipation of opportunities and dilemmas surrounding the transformative or disruptive capacities of these emerging technologies. Recently, ChatGPT's ability to access information from the web in real time marks a significant advancement with profound implications for businesses. This feature is argued to enhance the model's capacity to provide up-to-date, contextually relevant information, enabling more dynamic customer interactions. For businesses, this could mean improvements in areas like market analysis, trend tracking, customer service and real-time data-driven problem-solving. However, this also raises concerns about the accuracy and reliability of the information sourced, given the dynamic and sometimes unverified nature of web content. Additionally, real-time web access might complicate data privacy and security, as the boundaries of GAI interactions extend into the vast and diverse Internet landscape. These factors necessitate a careful and responsible approach to evaluating and using advanced GAI capabilities in business and management contexts.

GAI is attracting much interest both in the academic and business practitioner literature. A quick search in Google Scholar, using the search terms ‘generative artificial intelligence’ and ‘business’ or ‘management’, yields approximately 1740 results. Within this extensive repository, scholars delve into diverse facets, exploring GAI's potential applications across various business and management functions, contemplating its implications for management educators and scrutinizing specific technological applications. Learned societies such as the British Academy of Management have also joined forces in leading the discussion on AI and digitalization in business and management academe. Meanwhile, practitioners and consultants alike (e.g. McKinsey & Company, PWC, World Economic Forum) have produced dedicated discussions, reports and forums to offer insights into the multifaceted impacts and considerations surrounding the integration of GAI in contemporary business and management practices. Table 1 illustrates some current applications of GAI as documented in the practitioner literature.

In an attempt to capture the new opportunities and challenges brought about by this technology and to hopefully find a way forward to guide research and practice, management journals have been swift to embrace the trend, introducing special issues on GAI. These issues aim to promote intellectual debate, for instance in relation to specific business disciplines (e.g. Benbya,

The purpose of this introduction and Special Issue (SI) is to offer a unique and timely opportunity to explore, revisit and critically examine key methodological debates and tensions with the purpose of advancing diversity and novel theorizing in the field. We join voices with the authors of the five papers of this SI to problematize taken-for-granted assumptions and research traditions and pave the way for inclusive and novel theorizing in management scholarship. We revisit four long-lasting debates that hinder methodological pluralism and diversity in management scholarship: (a) the quantitative-qualitative research divide, (b) the legitimacy of mixed-methods research, (c) the rigour versus relevance tension and (d) the lack of methodological innovation. We suggest that these debates are at least partly counterproductive because they create silos and opposing camps, thereby inhibiting an appreciation of different worldviews and collective learning. The dominance of functionalism and positivism in quantitative research and the inappropriate transfer of quantitative logics in qualitative research have led to a lack of diversity in empirical methodologies. The field's limited methodological diversity is further proliferated by a strict adherence to quality standards that have inadvertently promoted homogeneity. This introduction highlights the challenges and potential of mixed methods, which are gaining momentum owing to calls for methodological pluralism. We also call for a re-evaluation of quality standards to encourage more innovative and diverse research methodologies.