Examining the Perception of Jamaican Human Resource Professionals on the Barriers to Artificial Intelligence Adoption Within Human Resources Processes: A Qualitative Study
1. Introduction
Background of the Problem: This study focuses on the perceived barriers of Jamaican human resource (HR) professionals in adopting artificial intelligence (AI) in HR processes. Statement of the Problem Situation: AI has the potential to change HR processes and leverage valuable resources, but Jamaica faces the challenge of managing national development with relatively low technological advancement compared to developed countries. Purpose of the Study: The study aims to explore the perceived barriers of Jamaican HR professionals in adopting AI in order to identify strategies for managing development needs in Jamaica’s context. Theoretical Framework: The study is grounded in the need to understand the barriers to AI adoption in HR processes and explore potential solutions for overcoming these challenges. Importance of the Study: Understanding the perceived barriers to AI adoption is crucial for identifying solutions that can help Jamaica leverage technology for HR processes and national development. Scope and Limitations of the Study: The study will focus specifically on the perceived barriers of Jamaican HR professionals in adopting AI, with limitations regarding the broader technological landscape in Jamaica.
Hence, this study presents HR professionals’ subjective lived experiences of the factors that are impediments to the adoption of AI in some HR processes within the Jamaican context. This information is useful for organizations seeking to establish and evaluate proposals for the training and development of HR professionals as business partners. It is also useful to policy and decision-makers in the government of Jamaica seeking to develop strategic policies and measures for e-governance, business process reengineering, and training and development. Articulating these barriers in management literature and practice is a likely, longer-term indirect impact of this research. Businesses can, therefore, more successfully embark on their venture of implementing AI technologies in the HR department, whether strapped for cash or not. They have hopefully found the answer to the burning question: What could be stopping us from implementing this?
1.1. Background and Rationale
The pervasiveness of artificial intelligence (AI) in various business sectors cannot be overstated in contemporary discussions of strategic decision-making. As new technologies continue to converge in novel ways, this allows business professionals to devise innovative strategies to efficiently carry out business activities. Although technology generally impacts activities such as communication, production, supply chain activities, market segmentation, and many others, the technology in question within this study is AI, as it relates to the Human Resource Management sector. In a democratic sign of the social possibilities automation and AI bring, the emphasis is typically on the ways that technology can support humans in carrying out their activities more efficiently.
Globally, society is in the IndTech 4.0 evolutionary stage, which, among other things, discusses AI and how it is expected to enable processes within entities specifically HRM practices. Within the context of Jamaica, technological adoption lags behind developed countries because of the socio-economic landscape that exists. With the growth of technological practices being implemented in HR departments, which are directly influenced by HR professionals, it would be interesting to know what the challenges associated with the implementation of AI into the Jamaican HR setting are. Many central implications are absent from the current literature, and this paucity of generalizations needs to be addressed in order to influence HR practices locally. It is in this context that this paper explores the perspectives of Jamaican Human Resource Professionals in AI adoption within the sector.
1.2. Research Aim and Objectives
The primary aim of this qualitative study is to explore Jamaican human resource (HR) professionals’ perception of the barriers to artificial intelligence (AI) adoption in HR processes. This study has the following specific objectives: 1) To understand Jamaican human resource professionals’ awareness of AI technologies. 2) To explore Jamaican human resource professionals’ attitudes towards the current and future uses of AI technologies within their field. 3) To explore the factors driving the attitudes and experiences of Jamaican human resource professionals towards AI technologies.
In order to effectively address our stated aim and objectives, our study is primarily guided by the following research questions: 1) To what extent are Jamaican human resource professionals aware of the current state of AI technologies? 2) What are their attitudes towards the current and potential future use of AI technologies within their field? 3) What factors shape Jamaican human resource professionals’ attitudes towards AI technologies in human resource processes?
Research on human resource (HR) practice regarding AI transition is scant. This research aims to inform academic theory and practice by highlighting HR perceptions and attitudes toward AI. This qualitative study seeks to examine the perception of Jamaican human resource professionals on the barriers to artificial intelligence (AI) adoption within HR processes. In Jamaica, the HR landscape continues to experience significant changes, with the integration of new and advanced technology and the prolonged impact of globalization. The Jamaican public is hoping for the increased use of technology in the workplace to be positive and transformative for the Jamaican business environment, specifically in relation to increasing efficiency and effectiveness while tackling necessary tasks. Continuing this necessary digital transformation within the Jamaican HR environment, we interrogate both those factors that are barriers to AI acceptance and potential mediators of such. Through a thematic analysis, this study reveals Jamaican HR’s views towards AI’s infancy evolution within programming and the workplace that comes hand-in-hand with blurred abilities to predict the future with the use of AI, and lacks of perceived trust in data necessary for AI use. These results may have contemporary policy and practice value within HR in the case-study context, given the modern and global use of AI technologies.
The findings of the study could have contemporary policy and practice implications for the HRD in relation to possible resistance to or barriers for AI transactions in manual processes, mirroring the feelings of HR employees across multinational organizations or globally. The study has both an academic and practical contribution. The academia and practice associated with HRD may benefit from insight into the potential barriers in new HR technologies being used within the context. This study is of interest to the readership of the journal as a contemporary exploration into the field of study given the growing movement within the AI field. This exploration within the HRD context is, however, little explored, more so within the Caribbean context, and specifically within a Jamaican or developing country lens.
1.3. Significance of the Study
To the best of our knowledge, no empirical research has been conducted on the perception of human resource (HR) professionals regarding the factors that might militate against AI adoption within HR processes in Jamaica. Equally important, the existing literature has focused largely on Western countries. However, we contend that there is an urgent need to explore this topic from the perspective of a small developing country. Our research will make a significant contribution to the literature on the integration of technology into the field of HRM. Moreover, there is a dearth of literature examining the perception of the barriers to adopting AI in the HR process, which must be understood in order to ensure the success of AI adoption in the human resource field.
Ultimately, the results of our research will help to foster a better understanding of the barriers identified by Jamaican HR professionals and strategies to mitigate them. It will be useful to policymakers because they will have a better understanding of the willingness of HR professionals to adopt AI in the field and will be encouraged to continue developing and implementing laws to ensure the ethical use of AI. Additionally, it is important for HR practitioners to understand how AI can influence and change their roles, and therefore how they can be prepared for the advent of AI in the field of HR. This research will allow HR professionals to identify and find ways to mitigate the barriers that will be associated with introducing AI into HR systems within their organizations. Furthermore, it is important to note that a small group of stakeholders, such as software developers, entrepreneurs, and HR professionals, may use the results and recommendations that will form the basis of a software package providing support in the form of recommendations on the use of AI in the HR field.
2. Literature Review
Artificial intelligence (AI) has been incorporated into human resource (HR) management in organizations. Various conceptual models have illuminated how AI can be used in HR functions for selection, performance management, training, etc. Despite theoretical recommendations on the use of AI in HR, the practical application is affected by several barriers. These barriers are grouped into four dominant categories: techno-economical, organizational, human risk factors, and regulatory barriers. The techno-economical aspect focuses on system operability and reliability, ethical issues, compatibility, flexibility, integration of AI technologies, cultural resistance, lack of eligibility to use AI, knowledge, skills, and training. Organizational resistance is defined by a lack of technical knowledge, fear of unemployment, AI inability, security and guarantees, economic constraints, internal control functions regime, negative attitudes of the company, and technology governance framework. AI adoption in HR hinges on advanced workshops in low-income countries but with solvable skill and organizational barriers.
The advance of artificial intelligence (AI), especially in HR, is necessitated by the demand for predictive analytics and improved employee experiences. However, AI faces significant barriers in HR adoption in contemporary organizations. In this vein, emerging research investigates the challenges of AI adoption existing in current organizations and offers guidelines for managers. Both the Innovation Diffusion Theory and the resource-based model are used to anchor their study, contributing insights regarding the benefits that organizations gain from AI adoption. Another similar study assessed the readiness and attitudes towards AI adoption simultaneously in various countries. Approximately 91% of the respondents claimed that their organizations would grow from AI, reduce costs, and transform the role of HR. Socio-cultural and economic contexts vary from one socio-political environment to another, thus necessitating further inquiries on AI adoption in HR in various world contexts. Such studies are vital in investigating the reception of AI sophisticated systems in organizations directly or indirectly.
2.1. Conceptual Framework of Artificial Intelligence in HR
Conceptual Framework: Artificial Intelligence in HR
Three frameworks that provide an understanding of the application of AI in HR processes are explored. The Factual Model of HR situates HRM, HRD, and HRP to take a systems perspective to understand human resource management on the individual, transactional, and policy levels, with policy connected to what managers can expect and the intentions of the workers in the organization. The introduction of new digital infrastructure initiates the process of regulation, and both the machine learning process and outcomes hold implications at the human and operational resource planning level. These impacts include the movements to retraining, human resource planning, recruitment, and selection, with the direction of movement of the HR strategies dependent on the initial position of the organization in the marketplace. AI can also be situated within the practice approach mental frameworks; in the field of AI, it is used in interpreting values and skills as a way of differentiating technologies. AI can be used as technology to transfer and extend the skills and knowledge of human analysis and intelligence. Finally, AI can be used strategically to be distributed from certain sectors and shared by peers to keep organizations relevant. This lays a practical framework of how HR can use machine learning for training and retaining employees by allowing them more flexibility and autonomy to reskill. The positives are the improved scaffold knowledge, strategic upskilling, development of new facilities, and the alignment of talents with strategic assets. The threats that need to be managed are privacy concerns and the proliferation of the ‘intelligent’ expert, as their leadership poses a threat to them.
In moving to the Jamaican context, this work illustrates that HRPs should conceptualize AI from these frameworks’ perspectives, situating it within to understand the causes and implications in order to create solutions that they can sell to be adopted. These conceptual frameworks outline how AI can be leveraged to deliver efficiency and competitive advantage from efficient and effective process outcomes, as well as focusing on serving the Jamaican context.
2.2. Barriers to AI Adoption in HR Processes
One of the key considerations for this study was built around a critical examination of existing literature to determine how global trends or constraints might apply to the local Jamaican context. The barriers to AI adoption in human resource processes are multilayered and wide-ranging. Common barriers appear to fall into several overarching factors including organizational culture, lack of technological infrastructure, resistance to change, fear of displacement, and lack of coordination when integrating AI into human resource processes. Although these barriers are connected to specific regions or countries, they have the potential to create barriers to adoption in a variety of environments. This section discusses the barriers to AI adoption uncovered during the literature review and their prevalence in both global and Jamaican contexts.
Many studies have underscored the slow adoption of AI in human resource processes by organizations. Existing studies have attempted to suggest multifaceted reasons for AI hesitations, suggesting that they are part of a greater resistance to digital-based solutions within an organization. In general, AIs have greater accuracy, believability, and persuasiveness in high anxiety situations. However, the Jamaican context is unique in terms of religion, language, and cultural norms, making their workers particularly unique. Various Jamaican-specific idiosyncrasies were discussed because they were the topic of the survey, regarding the particular attitude of Jamaicans towards technological change, both from a professional and personal perspective. These same considerations were also identified during the presentation in interviews with three human resources directors of three major international companies on Jamaican soil, as well as government HR directors.
2.3. Previous Studies on AI Adoption in HR
Given the relevance of artificial intelligence (AI) adoption to the field of human resource management, several studies have been undertaken to determine how successful and impactful such an adoption would be. Findings have suggested that AI adoption has been slow but can be of profound significance. Previous studies have also attempted to engage with AI adoption in the public sector. Research on AI adoption within the public sector posits various barriers to its adoption, including a wary reluctance to accept any technological solutions for problem-solving, despite there being cases for AI efficacy in the private sector.
It was further posited that these barriers are determined by the social and cultural dimensions of the context, rather than the technological phenomenon within which the solutions are nested. These situations are indeed strategic challenges that should be channeled for future research or evidence-based policy experimentation. Moreover, discussions about AI adoption in HRM are mostly drawn from Western, Asian, and African perspectives, with current research being conducted in developed countries. Although the private sector in Jamaica utilizes AI within its HR departments, the study finds a gap within the literature as it relates to any past or previous studies being conducted on this topic. The Jamaican context represents an untapped milieu that reflects the broader changing workforce in the context of limited technological environments. Consequently, future studies and evidence-based policy actions must take into account how future technologies will change or adapt to the Caribbean environment in order for valid generalizations. In this qualitative study, a new lens is provided to explore the mindset, views, and experiences of HR professionals regarding AI use within HRM in their respective operations in Jamaica.
Previous studies have been undertaken within the Asian and Western European contexts. Although prior research presents several barriers to the implementation and usage of AI, attributable to the peculiarities of any local context, new studies argue that the view of AI adoption in Sri Lanka represents an entirely new contribution. There remains a research gap that explores the view of Jamaican HR professionals on AI. This chapter will therefore explore: the significance of AI in HRM operations; a theoretical framework on AI in HRM use; social theoretical foundations in HRM use in research on AI in HRM systems in the developing world; global views on AI in HRM use; previous studies on AI in HRM; and a discussion of barriers to AI use in HRM systems from the cultural dimension.
3. Research Methodology
The research utilized a qualitative design, as it sought to explore the perceptions of Jamaican HR professionals on the barriers to AI adoption within HR processes. As qualitative research captures experiences and preferences, the framework is an outcome of personal narratives, the meaning that individuals give to their experiences, and the way people construct their world. The design includes the use of interviews and/or focus groups, which produce rich descriptive data. It allows for the in-depth review of specific areas or issues and uses various methods of data production, from interviews to observation. The population from which the sample will be drawn is human resource professionals in Jamaica who reside and work on the island.
The HR professionals will represent a cross-section of small, medium, and large organizations from both public and private sectors to whom the use of AI in their work would be applicable. This sample was obtained by combining purposive and snowball sampling. A purposive sample was selected by personal judgment as to the selection of individuals and groups to study. The sample frame will include government, manufacturing, banking and finance, medical, and the BPO sectors. Participants may be chosen because of their status or specific role within the organization. Snowball sampling is a non-probability sampling technique; this is the process of requesting an individual who meets the study’s characteristics criteria to recommend individuals they may know who also fit this description.
The qualitative data will be collected and analyzed using a systematic approach and will be used to identify key themes emerging from the narratives of participants. Each interview and/or focus group will be recorded and transcribed by the researcher, who will ask for clarification when necessary. Qualitative data analysis can follow a range of methodological techniques with the aim to uncover and build meanings and themes. Ethical acceptance for this study was approved, and informed consent was sought from each participant prior to the collection of data. Participant confidentiality throughout this study will be respected.
3.1. Research Design
The researchers chose to conduct a qualitative study utilizing a structured framework to guide the research process. The qualitative design was chosen as it was critical to investigate the psychological, economic, and social areas of concern. Additionally, the method utilized was effective for uncovering detailed experiences, perspectives, and opinions of Jamaican human resource professionals regarding AI adoption within HR processes. A qualitative approach is crucial to exploring an issue in order to gain an understanding and description of the nitty-gritty of specific areas or detailed descriptions. Since the researchers were assessing the human dynamics, emotions, and beliefs, a case study and thematic analysis would provide accurate results as thematic analysis presents the potential to offer theoretical insights on AI perceived barriers from the perspectives and experiences of the Jamaican human resource professionals.
The study sought to answer the following research questions: What are HR managers’ perceptions of artificial intelligence? What barriers to artificial intelligence adoption do HR managers in Jamaica perceive existing within the various human resource processes? This excerpt on research design demonstrates that the chosen approach was intentional based on the research questions. The explanation of the research design outlines its aims and relevance.
3.2. Data Collection Methods
This was a qualitative study utilizing semi-structured interviews and focus group discussions. The interview and focus group guides were shared publicly for review before the day of the discussions as a form of analyst triangulation or member checking, which is important for reliable and valid findings – a main hallmark of interpretivist research. Analyst triangulation also involves reviewing each other’s coding frames and rationalizing any differences. The ultimate aim of these methods is to arrive at truthful conclusions, abolish researcher bias, and ensure that findings closely resemble the participants’ true narratives.
The qualitative methods employed are best suited for research that aims to explore and comprehend the perceptions and experiences of individuals. In addition, due to the exploratory nature of this research, the methods were ideal in uncovering novel data and identifying elements that were not known to the researchers prior to collection. Semi-structured interviews and focus groups permit researchers to probe into an individual’s disclosed thoughts and feelings as well as receive additional, possibly subconscious, information that the moderator has not thought to elicit. Through these methods, we aimed to collect data that was more detailed, descriptive, and rich than what would be expected through quantitative surveys. The data is also person-specific and heavily influenced by the individual’s context, ensuring that all contributing factors that shape participants’ perceptions, thoughts, and feelings were successfully captured. The recruitment of diverse participants allowed us to gather narratives from a wide array of HR professionals with differing experiences, skills, expertise, and, importantly, perceptions.
The rapport that was established with participants from the outset aided in the openness of the discussions, as participants offered candid responses to the topics presented. Incentives such as meals, refreshments, and free time away from official responsibilities were also given to care for the participants’ well-being and keep their attention active, which led to the energetic flow of discussions. The ultimate aim was to collect in-depth data that comprised support details and was reliable and wide-ranging enough to allow for a deep, experiential analysis of all participants’ accounts. True to the exploratory nature of a grounded theory approach, the interview and focus group guides were piloted on two HR supervisors before data collection commenced. This served as a check to ensure the questions not only provided responses that accurately addressed the research questions, but also provided further, unforeseen insights into the participants’ attitudes towards AI, which indicated that the questions were adequately poised to provide the sort of response this research aimed to address.
3.3. Data Analysis Techniques
The research team subscribed to an inductive and reflexive approach by using thematic analysis. The process of data analysis was utilized within this study. In using thematic analysis, the analysis of the data is a systematic process composed of a series of activities undertaken alongside a constant unfolding of the research process. This involves the analysis of language in order to expose nuances and contradictions that may not become evident through more structured analysis.
The stages of thematic analysis include familiarizing ourselves with the data through repeated readings and note-taking; generating initial codes; searching for themes; reviewing and labeling themes; defining themes and writing about them. The process of data analysis started with the coding of the data. This inductive process of open coding was conducted individually by identifying segments within the transcripts and labeling them with interpretive codes. These codes were then used to categorize the responses and the researchers met to discuss the categories and develop analytical themes. Through a reflective process and to enhance trustworthiness, the themes were reviewed by members of the research team for their robustness and an analytical report of our findings was compiled.
During the analysis, it was important for the researchers to maintain a level of reflexivity and be aware of their positionality within the study. Reflexivity incorporates a critical self-awareness and an interrogation of the researchers’ backgrounds, experiences, perspectives, and values. It helps to reduce systematic biases and allows the study to become a collaborative, ethnographic pursuit. Furthermore, qualitative software was used to import, transcribe, and analyze the audio-recorded interviews. The use of qualitative software in the analytical process is oriented to reducing oversight and human error as it relates to coding, to maximize effectiveness and transparency in code application and the retrieval of category instances.
4. Findings and Discussion
6.1 Introduction This chapter presents the results of the data analysis from a series of interviews conducted with Human Resource professionals in Jamaica. We examined their perceptions of the barriers to the adoption of Artificial Intelligence within Human Resources processes to obtain narrative data that best captures their perspectives on the research topic. The major identified themes are discussed, exploring the socio-economic and cultural challenges that may influence how Jamaican HR professionals perceive barriers to decision-making.
6.2 Framework The Jamaican context is characterized by a unique cultural landscape, providing some opportunities and challenges that may impact participants’ perspectives. In Jamaica, it is common to see a harmonious blend of different cultures and racial backgrounds, indicating that Jamaicans are open to accepting foreigners and their cultures. The country’s population is primarily of African descent, with the descendants of African slaves constituting a significant portion of the populace. The descendants of East Indian and African intermarriages, Afro-Europeans and Europeans, and those of Chinese descent also contribute to the demographic diversity. As of 2018, Jamaica has had less than 40% of its GDP as the Gross Fixed Capital formation from 2001, with the highest amount being a notable percentage in 2010. This has meant that Jamaica is unable to efficiently invest in capital equipment, artificial intelligence, and/or systems that will allow for future economic and social gains.
Employing Artificial Intelligence-driven systems within organizations often produces long-term cost optimization that might prevent the further recruitment of new employees. In Jamaica, with its already high unemployment and underemployment rate, it is plausible for decision-makers to perceive that soliciting the implementation of AI systems might create ethical dilemmas. This might be why being uncomfortable about suggesting the implementation of AI systems that are decision-based was a prevalent response in our findings. Furthermore, the implementation of AI systems can further widen the digital divide between developed countries and third-world nations such as the Caribbean nations. If the world continues in the way it is going and developed organizations keep reinforcing AI automation and AI technology, third-world countries and their citizens might be left jobless and unable to provide for themselves. This type of education aims to omit these potential implications of technological advancements in these communities.
4.1. Themes Emerging from the Data
4.1. Themes Emerging from the Data. During the creation of the Adult Training Survey, discussions were facilitated with local stakeholders in relation to various labor market issues, including the adoption of technology and its likely impact on jobs. Jamaican stakeholders acknowledged that there are several challenges and complexities that could impact the short-term development of the country. These included demographic profiles, such as the prevalence of youth unemployment and high levels of informality. Other challenges included ‘soft skills deficits’ in certain professional and occupational areas, educational curricula not being aligned with labor market requirements, and gaps in more advanced technical and professional skills at the middle management levels. Despite these contextual challenges, the findings of this study highlight a real-time, on-the-ground push in Jamaica about pursuing a conversation about the use of AI in HR.
A total of 20 participants (19 females, 1 male) were interviewed, of which twelve represented for-profit private sector organizations, three worked in the public sector, and five worked within non-profit sectoral agencies. Some key themes emerged from the qualitative data and included comments in relation to (1) technological challenges; (2) resistance to change; (3) fear of job displacement; (4) limited awareness about AI; (5) human-oriented jobs cannot be done by a machine or an algorithm; and (6) AI will do the repetitive parts of HR, which will free up time for HR professionals to do more strategic aspects of the job. With respect to technological challenges, a couple of participants spoke about the digitization requirement of the HR function. They suggested that greater emphasis on digitizing HR is a likely precursor to more innovative technology-oriented approaches in the form of AI. This is because there are some traditional and basic HR processes that have not yet been digitized, and as long as this remains in its current status, the transition into an AI-driven HR environment is a long way off. For instance, one participant noted, “To be honest, in the HR world, in the Jamaican culture, we are not people that are big on HR automation, right? A number of organizations still do manual leave forms, still do handwritten appraisals, and so that’s where we are.” Other participants noted that the process of HR digitalization is a top priority for HR clients.
4.2. Comparison with Existing Literature
This study adds to the existing literature by extending the discussion of the barriers to AI adoption in human resources processes. The study’s participants share similar views to the discussion in the existing literature; however, various unique perspectives emerged from the participants, which indicate that the existing literature may not provide full coverage of the barriers to AI adoption. As such, local studies and the perspectives of human resource professionals on AI adoption are valuable for informing global discussions.
The data largely support the findings that suggest a lack of AI knowledge, data privacy concerns, and financial constraints as significant barriers to adopting AI in HR processes. The link between these findings and the literature stems from the data being consistent with the theoretical perspectives that underscore research findings in the AI field. Some participants also highlighted additional barriers to AI adoption that are not widely discussed. For example, the data suggest that participants perceive significant importance of HR managers in AI adoption—a perspective not emphasized—and that there are a number of factors that could potentially drive HR managers to adopt AI in HR. Furthermore, participants discussed the belief in reduced discrimination as a driver, and some studies on AI adoption in HR do not mention the reduction of discrimination as an incentive for organizations to adopt AI in HR. None of these perspectives were found in the literature, indicating that this study further widens the existing AI field.
5. Conclusion and Recommendations
Findings from the research deepen our understanding of the perceptions and barriers of AI adoption as articulated by Jamaican HR professionals and provide a unique insight into the research of AI in smaller economies. Jamaican HR professionals identified four technological barriers: leveraging AI in Jamaica, sources of technology, change management in an AI context, and costs of AI. They further detailed three policy and six workforce-related barriers. From our analysis, we deduced that emerging policies and strategies need to be associated with human resource management AI and framed by digital ethics. Given that Jamaican companies manifest high localization orientation in their foreign direct investment strategies, we opine that regional strategies could be informed by these findings in respect of HR management AI from a regional Jamaican perspective. We also propose a number of future studies in respect of HRM and AI within small island developing states and companies that operate in them.
We conclude this paper with a few practical implications that emerge for Jamaican HR managers from our findings. To overcome the visibility of opportunities barrier for AI uptake by recruiting line managers in particular and HR professionals in general, our analyses suggest that HR professionals might consider sharing a case study of effective AI use in their hiring process with the company. Similarly, we contend that Jamaican HR professionals could replicate the case study insights with the Jamaican case studies to overcome the perceived legitimacy barrier across different levels of local organizations. After Jamaican managers see the positive effects of AI use on hiring and its applicants, a group of HR professionals, line managers, AI developers, and job seekers can be enlightened about the virtues of HR AI. Our analyses suggest that HR professionals may be able to open the door for AI to migrate from an impersonal data generator to a systematic recruiter, personal workplace assistant, and strategic career coach. Lastly, a recruiting line manager is needed to test the effectiveness risk barrier of an AI tool’s inability to not invite unintended discrimination before it is used to complement its array of other valid recruitment aids. The next study in Jamaica and the region is, therefore, indicative of an orientation.
5.1. Summary of Findings
In conclusion, the study indicates that Jamaican HR professionals believe that there are many barriers to AI adoption within HR processes. The barriers identified in this study are influenced by the technological limitations and cultural concerns that are driven by the unique environment of the Jamaican labor force in terms of size and national culture. HR professionals in this study believe that if AI is to undergo successful adoption in Jamaica, it has to be done with a local knowledge approach and in such a manner as to manage these barriers. It is important for us to contend with the fact that this research has been conducted within a particular labor market environment that is characterized by its unique national culture. It is possible that there are other HR-related barriers of such consequence that may or may not exist in other jurisdictions. However, in the spirit of explication, we provided a summary of the major findings of the study in this subsection for easy removal for knowledge practitioners and business leaders.
Jamaican HR professionals in this study have identified a list of particular barriers, which they perceive could potentially prevent the adoption of artificial intelligence in their work. These include both technological constraints, which make the implementation of a number of features particularly difficult for them, as well as several cultural barriers that their colleagues may have to AI. These include concerns about the displacement of low-skill jobs and job security. As technology is constantly being developed, the findings support the prediction that any HR-specific AI software will become more complex in due course. In addition to developing software that integrates more HR-related functions, AI companies must also consider interventions and assistance in learning and development training regardless of what AI is in the future. The issues that are raised in this study require context-specific understanding. There are potential managerial implications in this for organizations that may be looking to market HR-specific AI to organizations in Jamaica. For instance, if one is marketing HR-specific AI to HR professionals in the Caribbean, caution should be taken to ensure that the AI development process has taken the above issues into account or that the AI company has the capacity to help HR departments negotiate these barriers.
5.2. Implications for Practice
The results of this study can be utilized by HR professionals in Jamaica to address the barriers discovered in AI process adoption. Best practice recommendations that could be implemented to hasten the integration process have been tailored to the Jamaican context. There is a need for HR professionals to take strategic steps in understanding all components needed for the effective implementation of AI within the organization. The culture of the organization must be transformed and framed by decision-makers, as they tend to reject new innovations, seeing them as a threat to their daily routines and ultimately, their jobs. The AI process of decision-making looks beyond human intervention; it is daunting to the average day-to-day HR professional and could be a major factor in the resistance to AI.
Managers must condition their employees’ minds to accept future changes and help them understand that AI, just like their implemented HRM practices and systems, will not only make the life of an employee easier but also increase the productivity and effectiveness of HR processes. The soft systems can be approached by aligning information that is pro-change, which will undoubtedly result in the cooperation of the naysayers within the organization. Jamaican HR professionals must become AI-literate experts who can wield the information derived from the AI system for organizational development and future decision-making. It may behoove HR professionals to differentiate themselves from the process of change and align themselves more with the person who must facilitate the growth stemming from the change.
The results of this study showcase that the perception of several Human Resource professionals in Jamaica is that AI in HR is in its infancy and there are several deterrent points holding back its development. There are several practices, tailored to the Jamaican context, that can be implemented. It is the intention of this sub-section to discuss these practices which can influence HR practices in Jamaica today. The end goal is to persuade HRM practitioners to adopt these strategies because the implementation of AI in HR will significantly impact HR life in Jamaica. The significant impact of Jamaican HRM practitioners identifying the deterrent points will increase the adoption and usage of AI in HR by empowering, educating, and conditioning the minds of Human Resource professionals in Jamaica when addressing AI in HR. The strategic management plan seems pertinent to the application of AI in HR for these HR practitioners to consider.
5.3. Recommendations for Future Research
Among various future research recommendations offered, this study provides a number of areas that future studies can focus on. It is recommended that future studies in this area seek to conduct further research to investigate if HR professionals’ perceptions towards the barriers are changing. Qualitative longitudinal studies could be conducted among HR professionals. In addition, future studies can use a similar research design to investigate HR professionals’ perceptions towards the facilitators of AI adoption. It is recommended that future studies investigate other managers’ and employees’ perceptions regarding the barriers of AI adoption, as well as the intersection of culture and AI adoption, and how it influences or will influence HR practices, especially within the Caribbean context. Furthermore, future studies could also adopt more collaborative research methodologies. The recommendations are summarized.
Longitudinal studies: it is recommended that further research could be conducted to measure how the perception of the barriers of AI adoption in HR may have changed. Comparative study: it is recommended that future studies could adopt the research design of this study and compare the findings with those of another country to provide a more seamless study in the context of AI adoption. Focus on managers and employee perceptions: it is recommended that future research can assess managers and employees to determine whether common perceptions align with those of HR managers. Researchers could adopt mixed methods research involving surveys completed by managers and employees, as well as conducting one-on-one interviews with decision makers. Qualitative study design: research could investigate the views of experts on the same. There is also a potential area of research that investigates the intersection of technology and culture, especially within a region like the Caribbean, and how this intersection influences and is influenced by talent management. Collaboration: research could also adopt a more collaborative approach where researchers from different disciplines, including HR, accounting, finance, etc., in academia as well as the operational world, collaborate. The data from all the fields and analysis conducted with the assistance of all could give a more holistic picture of the situation.