foresight

Walia, Sandeep; Kautish, Pradeep

2023 foresight

doi: 10.1108/fs-03-2023-259

Ivanov, Stanislav Hristov

2023 foresight

doi: 10.1108/fs-09-2021-0183

This paper aims to analyse three decision-making approaches that involve humans and artificial autonomous agents, namely, human “in the loop”, “on the loop” and “out of the loop” and identifies the decision characteristics that determine the choice of a decision-making approach.Design/methodology/approachThis is a conceptual paper that analyses the relationships between the human and the artificial autonomous agents in the decision-making process from the perspectives of the agency theory, sustainability, legislation, economics and operations management.FindingsThe paper concludes that the human “out of the loop” approach is most suitable for quick, standardised, frequent decisions with low negative consequences of a wrong decision by the artificial intelligence taken within a well-defined context. Complex decisions with high outcome uncertainty that involve significant ethical issues require human participation in the form of a human “in the loop” or “on the loop” approach. Decisions that require high transparency need to be left to humans.Originality/valueThe paper evaluates the decision-making approaches from the perspectives of the agency theory, sustainability, legislation, economics and operations management and identifies the decision characteristics that determine the choice of a decision-making approach.

Shant Priya, Samant; Jain, Vineet; Priya, Meenu Shant; Dixit, Sushil Kumar; Joshi, Gaurav

2023 foresight

doi: 10.1108/fs-09-2021-0181

This study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.Design/methodology/approachTo determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.FindingsThis study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d’ Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.Practical implicationsThis study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.Originality/valueAccording to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.

Munir, Sabra; Abdul Rasid, Siti Zaleha; Aamir, Muhammad; Jamil, Farrukh; Ahmed, Ishfaq

2023 foresight

doi: 10.1108/fs-08-2021-0161

This paper aims to assess the impact of big data analytics capabilities (BDAC) on organizational innovation performance through process-oriented dynamic capabilities (PODC), as a mediator, as well as the moderating roles of organizational culture (OC) and management accountants, in this artificial intelligence (AI) era. This paper also aims to provide information on the emerging trends and implications of the abovementioned relationships by focusing on these relationships and interactions.Design/methodology/approachThis exploratory study used the close-ended questionnaire approach based on the resource-based view and socio-materiality theories. This included sending questionnaires to top-level management, including Chief Financial Officer/Chief Executive Officers/Chief Information Officers (CFO/CEOs/CIOs), having an in-depth understanding of the concepts, practical applications and usage of big data as well as BDAC.181 valid questionnaire-based responses were analyzed using the partial least square structural equation modelling technique and bootstrapping moderated mediation method.FindingsThis study provides empirical insights into how BDAC impact innovative performance through PODC as well as the moderating effects of OC and management accountants. This involves a shift in focus from almost standardized approaches to developing BDAC without contextual focus on approaches that are much more heterogeneously related to each organization and hence are more focused on the context of the pharmaceutical industry.Research limitations/implicationsThe main aim of key research questions in this study is to increase the contributions of BDAC toward improving innovation performance in the presence of the abovementioned variables and relationships that exist between them. The chosen research approach can be improved by carrying out interviews with the top management to obtain more relevant and detailed information for developing a better understanding of the abovementioned relationships.Practical implicationsThis study outlines how organizations that are developing BDAC approaches can focus on relevant factors and variables to help their initiatives and its role in organizational innovative performance. This will also help them develop sustainable competitive advantage in manufacturing concerns, specifically in the health industry, namely, the pharmaceutical industry.Originality/valueThis study investigated the effects and implications of big data on organizations in the AI era that aim to achieve innovation performance. At the same time, it provides an original understanding of the contextual importance of investing in BDAC development. It also considers the role of management accountants as a bridge between data scientists and business managers in a big data environment, especially in the pharmaceutical industry. The current study used first-time data from surveys involving CFOs, CEOs or CIOs of pharmaceutical companies in Pakistan and analyzed the proposed model using bootstrapping moderated mediation analysis.

Agarwal, Alpana

2023 foresight

doi: 10.1108/fs-10-2021-0199

The purpose of this study is to explore and examine the determinants of artificial intelligence (AI) adoption by human resource management (HRM). Further, the impact of AI adoption by HR department on their effectiveness has also been tested.Design/methodology/approachA model explaining the antecedents of AI adoption by HRM is proposed in this study. The proposed model is based on task–organization–environment and task–technology fit models. A two-step partial least square-based structural equational modelling (PLS-SEM) has been used for testing the model. Data was collected from 210 HRM employees (only senior level or specialized HR positions), working in IT firms located in Delhi-NCR region.FindingsLiterature review shows that among others, organizational preparedness, perceived benefits and technology readiness determine AI adoption which in turn can make HR system more effective. Results of PLS-SEM support all hypothesized relationships and validate the proposed model.Originality/valueConsidering paucity of research on antecedents of AI adoption by human resource department, this study adds significantly to the body of knowledge. Additionally, based on the findings of statistical analysis, certain AI-related recommendations are given to HRM.

Huda, Miftachul

2023 foresight

doi: 10.1108/fs-09-2021-0184

The presence of digital learning space is widely seen as there is an active engagement between educators and learners. However, the challenge raised mainly amidst the pandemic age, which is potentially leading to the interference on the active engagement in education process. The necessary act to have a critical response from the student’s feedback towards the online learning services should be taken into consideration in ensuring the continuance of teacher education in enabling to grab the potential chance to advance the assessment of strategic approach in online learning. This paper aims to examine the digital access during the pandemic age through elaborating the extensive value of better learning service or adaptation for the online learning achievement amidst the pandemic age.Design/methodology/approachThis study is conducted with a qualitative approach through the particular method of data collection, namely, structured interview. This qualitative approach was selected to enable obtaining the richness of information and related data. The insightful feedback will be coming from 27 higher education learners.FindingsThe finding revealed that better design of achievement pathway on the digital access could be enhanced in supporting the online learning performance through the online services. The main point refers to look into detail about digital online infrastructure insufficiency for online access support and improvements on digital online infrastructure for accessibility of learning service. The main occupations are clearly pointed in the following phase. Those are empowering digital access for learning service support and enhancing digital-adaptation for online learning achievement.Originality/valueThis study is supposed to contribute in assisting the value contribution with an extensive point to continue the digital access during pandemic age through the adaptation empowerment of higher learner’s online learning services.

Warintarawej, Pattaraporn; Nillaor, Pichetwut

2023 foresight

doi: 10.1108/fs-09-2021-0179

The research objectives are as follows: to understand the situation of solid waste management in the Makham Tia Subdistrict Administrative Organization, Surat Thani Province, Thailand; identify the patterns in household waste generation and 3Rs behavior (recycle, reuse and reduce waste); and formulate sustainable municipal solid waste management guidelines.Design/methodology/approachThis study aimed to propose the solution by using data analysis and a participatory research approach to set the guidelines for sustainable community waste management in a low-budget area. A survey of household behavior was done with questionnaires. Mixed clustering using the Gower coefficient was performed to assess the categorical socio-demographic variables along with the numeric variables related to the 3Rs behavior. The guidelines for waste management were generated based on the characteristics of the household groups.FindingsThe guidelines for waste management were generated based on the characteristics of the household groups. An appropriate practical plan for municipal solid waste management in Makham Tia Subdistrict was proposed in this work. The study showed that the guidelines were implemented and revised by members of the community, and this led to the development of sustainable community solid waste management for the future.Originality/valueThe goal of this study was to provide a solution for sustainable community waste management in a low-budget location by using data mining techniques and a participatory research approach. The study showed that the guidelines were implemented and revised by members of the community, and this led to the development of sustainable community solid waste management for the future.

Arora, Manpreet; Sharma, Roshan Lal

2023 foresight

doi: 10.1108/fs-10-2021-0216

The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

Bhatt, Prachi

2023 foresight

doi: 10.1108/fs-07-2021-0144

In the context of new workplace environment, this study aims to study and generate insights about artificial intelligence (AI) adoption in hiring process of firms. It is very relevant when AI is dramatically reshaping hiring function in the changing scenario.Design/methodology/approachThe objectives are achieved with the help of three studies involving Delphi method to explore the criteria for AI adoption decision. Followed by two multi criteria decision-making techniques, i.e. analytic hierarchy process to identify weights of the criteria and fuzzy technique for order preference by similarity to ideal solution to assess the extent of AI adoption in hiring.FindingsThe findings reveal that information security and return on investment are considered two very important criteria by human resources managers while contemplating the adoption of AI in hiring process. It was found that AI adoption will be suitable at the sourcing and initial screening stages of hiring. And the suitability of the hiring stage where AI can be applied has been found to have changed from before and after the onset of COVID-19 pandemic situation. The findings and its discussion assist and enhance better decisions about AI adoption in hiring processes of firms amid changing scenario – external and internal to a firm.Research limitations/implicationsFindings also highlight research implications for future research studies in this emerging area.Practical implicationsResults act as a starting point for other human resources managers, who are still pondering over the idea of adopting AI in hiring in future.Originality/valueThis paper through a systematic approach contributes by identifying important evaluation criteria influencing AI adoption in firms and extent of its application in the stages of hiring. It makes a substantial contribution to the under-developed yet emerging paradigm of AI based hiring in practice and research.