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This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.
Abstract
Purpose
This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.
Design/methodology/approach
The critical and logical analysis is based on current knowledge of generative AI.
Findings
Generative AI seems very useful for qualitative research in phygital settings to understand the customer experience and should be used in qualitative research projects. Generative AI can provide much-needed validation of the subjective nature of qualitative research and can also generate insights beyond human intuition.
Research limitations/implications
The study is based on current technology, which changes fast. In the future, the skills of qualitative researchers may become outdated, relegating them to the role of prompt engineers.
Practical implications
Technology, and especially generative AI, will be a key tool for practitioners as they conduct practical research.
Social implications
Qualitative researchers should overcome potential anti-technology speciesism and embrace the potential of generative AI.
Originality/value
This commentary provides insights into the role of generative AI for qualitative research in phygital settings.
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Jacob Dencik, Brian Goehring and Anthony Marshall
Since the release of ChatGPT by OpenAI in November 2022 – with its ability to create compelling, relevant content, new large language model (LLM) technology – business leaders…
Abstract
Purpose
Since the release of ChatGPT by OpenAI in November 2022 – with its ability to create compelling, relevant content, new large language model (LLM) technology – business leaders, especially CEOs, are being pressured to accelerate new generative AI investments. IBM IBV surveyed executives to assess their progress and concerns and their adoption strategies.
Design/methodology/approach
Adoption of generative AI is still in its very early stages. Most organizations are only beginning to figure out how and where to make use of it. In fact, as few as 6 percent of executives in new surveying conducted by the IBM Institute for Business Value say they are operating generative AI in their enterprise today.
Findings
In contrast to many peoples’ expectations about AI, automating tasks is not the top priority for executives looking to tap generative AI to grow business value. Looking at benefits by function, research and innovation is the primary area where organizations see opportunities for generative AI.
Practical implications
IBM IBV's recent survey of executives found that the key barriers to the effective deployment and use of generative AI are linked to security, privacy, ethics, regulations and economics – not access to the underlying technology itself.
Originality/value
Organizations will have to evaluate where in their enterprise the potential gains and cost efficiencies outweigh the risks of possible errors or unintended consequences from the use of generative AI along with broader ethical considerations. Ecosystems expand generative AI opportunities to harness data, insights and technology capabilities from across partners and stakeholders while enabling control over the capabilities that are most central to an organization’s value proposition.
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Addison Sellon and Lindsay Hastings
Applying traditional grounded theory techniques, the present research reanalyzed secondary data from four previously conducted studies to explore how generativity is manifested in…
Abstract
Purpose
Applying traditional grounded theory techniques, the present research reanalyzed secondary data from four previously conducted studies to explore how generativity is manifested in young adults.
Design/methodology/approach
A new conceptual model of generativity was developed to depict how generativity manifests among this age group.
Findings
This study's findings provide leadership educators with a refined approach to interacting with this construct while simultaneously increasing young adults’ potential ability to experience the benefits available to them through generativity at an earlier stage in their lives.
Originality/value
This study advances the field of leadership education by establishing foundational insight into the uniqueness of generativity’s development in young adulthood.
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Anthony Marshall, Christian Bieck, Jacob Dencik, Brian C. Goehring and Richard Warrick
Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead…
Abstract
Purpose
Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.
Design/methodology/approach
Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.
Findings
1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation
Research limitations/implications
We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.
Practical implications
Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.
Social implications
To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.
Originality/value
This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.
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Truong Quang Do, Nguyen Dinh Tho and Nguyen-Hau Le
This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness…
Abstract
Purpose
This study aims to investigate a mediation model in which generative learning positively affects marketing innovation and both organizational control and relationship openness mediate the relationship between learning intent and generative learning of international joint ventures (IJVs) in emerging markets. We also decipher the degree of necessity of these factors for generative learning and of generative learning for marketing innovation.
Design/methodology/approach
A sample of 181 marketing managers of IJVs in Vietnam, an emerging market, was surveyed to collect data. Partial least squares structural equation modeling (PLS-SEM) was employed to test the net effect, and necessary condition analysis (NCA) was used to decipher the degree of necessity.
Findings
The PLS-SEM results demonstrate that the effect of learning intent on generative learning is fully mediated by organizational control and relationship openness, which in turn leads to marketing innovation. The NCA findings reveal that all three factors, namely learning intent, organizational control and relationship openness, serve as necessary conditions for generative learning. However, generative learning does not play the role of a necessary condition for marketing innovation.
Practical implications
The study findings suggest that IJVs in emerging markets should pay attention not only to the net effects of those factors but also to their degrees of necessity for generative learning in order to achieve marketing innovation.
Originality/value
The study contributes to the literature by confirming the mediating roles of organizational control and relationship openness in the relationship between learning intent and generative learning. Furthermore, it is among the first to decipher the degrees of necessity of these factors for generative learning and of generative learning for the marketing innovation of IJVs in emerging markets.
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Ying Zhou, Yu Wang, Chenshuang Li, Lieyun Ding and Cong Wang
This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health…
Abstract
Purpose
This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.
Design/methodology/approach
The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.
Findings
Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.
Research limitations/implications
The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.
Originality/value
The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.
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Amy Stornaiuolo, Jennifer Higgs, Opal Jawale and Rhianne Mae Martin
With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives…
Abstract
Purpose
With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives. Drawing on critical postdigital approaches to learning and literacy, this study aims to center the experiences and perspectives of young people who encounter and experiment with generative AI in their daily writing practices.
Design/methodology/approach
This critical case study of one digital platform – Character.ai – brings together an adolescent and adult authorship team to inquire about the intertwining of young people’s playful and critical perspectives when writing on/with digital platforms. Drawing on critical walkthrough methodology (Light et al., 2018), the authors engage digital methods to study how the creative and “fun” uses of AI in youths’ writing lives are situated in broader platform ecologies.
Findings
The findings suggest experimentation and pleasure are key aspects of young people’s engagement with generative AI. The authors demonstrate how one platform works to capitalize on these dimensions, even as youth users engage critically and artfully with the platform and develop their digital writing practices.
Practical implications
This study highlights how playful experimentation with generative AI can engage young people both in pleasurable digital writing and in exploration and contemplation of platforms dynamics and structures that shape their and others’ literate activities. Educators can consider young people’s creative uses of these evolving technologies as potential opportunities to develop a critical awareness of how commercial platforms seek to benefit from their users.
Originality/value
This study contributes to the development of a critical and humanist research agenda around generative AI by centering the experiences, perspectives and practices of young people who are underrepresented in the burgeoning research devoted to AI and literacies.
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Lucinda McKnight and Cara Shipp
The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool”…
Abstract
Purpose
The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.
Design/methodology/approach
The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.
Findings
Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.
Originality/value
Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.
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Sumaira Nazeer, Muhammad Saleem Sumbal, Gang Liu, Hina Munir and Eric Tsui
The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual…
Abstract
Purpose
The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.
Design/methodology/approach
The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.
Findings
This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.
Research limitations/implications
This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.
Originality/value
ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.
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Aayush Bhat, Vyom Gupta, Savitoj Singh Aulakh and Renold S. Elsen
The purpose of this paper is to implement the generative design as an optimization technique to achieve a reasonable trade-off between weight and reliability for the control arm…
Abstract
Purpose
The purpose of this paper is to implement the generative design as an optimization technique to achieve a reasonable trade-off between weight and reliability for the control arm plate of a double-wishbone suspension assembly of a Formula Student race car.
Design/methodology/approach
The generative design methodology is applied to develop a low-weight design alternative to a standard control arm plate design. A static stress simulation and a fatigue life study are developed to assess the response of the plate against the loading criteria and to ensure that the plate sustains the theoretically determined number of loading cycles.
Findings
The approach implemented provides a justifiable outcome for a weight-factor of safety trade-off. In addition to optimal material distribution, the generative design methodology provides several design outcomes, for different materials and fabrication techniques. This enables the selection of the best possible outcome for several structural requirements.
Research limitations/implications
This technique can be used for applications with pre-defined constraints, such as packaging and loading, usually observed in load-bearing components developed in the automotive and aerospace sectors of the manufacturing industry.
Practical implications
Using this technique can provide an alternative design solution to long periods spent in the design phase, because of its ability to generate several possible outcomes in just a fraction of time.
Originality/value
The proposed research provides a means of developing optimized designs and provides techniques in which the design developed and chosen can be structurally analyzed.
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