As technology continues to advance and drive changes to business models, the role of all types of employees will need to be further developed and adapted at an accelerating pace (Margherita and Braccini, 2021; Lu et al., 2019; Sigala, 2018). The World Economic Forum's (2018) Future of Jobs Report predicted that, by 2022, 75 million employees could be laid off because of a shift in the division of labour between the human workforce and new technologies (WEF, 2018). In addition, according to the World Economic Forum, by 2025, the wave of robotics will damage 85 million global jobs (WEF, 2018). Undoubtedly, the most important challenges related to Career Sustainability (CS) facing employees in our current era involve the quest to preserve their jobs (Chin et al., 2019; Xu et al.,2018; Larivière et al., 2017), especially as the threat of technology to the labour market is becoming evident (Doeringer and Piore, 2020; Decker et al., 2017; Hanushek et al., 2017).

As we are in a highly competitive employment market, where each employee seeks to show all their talents and abilities to keep their job, the issue of CS has become of paramount importance. Organizations endeavour to retain employees who possess unique capabilities and exceptional skills that distinguish them from others (Richardson and McKenna, 2020; Tordera et al., 2020). Meanwhile, organizations are seeking to replace their human employees with robots for several reasons (Li et a., 2019), including: (a) Safety: as some jobs pose serious risks to human health, especially working with heavy machinery, high temperatures and sharp tools, delegating robots instead is appropriate (Galin and Meshcheryakov, 2021; Sparrow and Howard, 2017); (b) Perfectionism: robots are programmed to perform tasks with extreme accuracy, are less likely to make mistakes, and may monitor the quality of their work, leading to the highest quality standards (Hinz et al., 2019); (c) Speed: because robots are machines, they do not need to stop, are not distracted, and do not need times for rest and holidays (Pfeiffer, 2016; Gombolay et al., 2015); (d) Continuity: robots concentrate on a single task, and their work is not dependent on the work of others, so using them eliminates the idea of emergencies and unexpected events more reliably than using human labour (Tavakoli et al., 2020); (e) Pay: robots receive no monthly salary, although there are capital and maintenance costs beyond the initial guarantees (Aksoy et al., 2021; Rosemurgy et al., 2015); and (f) Robots require no insurance or post-retirement benefits (Straubhaar, 2017).

For all these reasons, the issue of employees’ concern about the sustainability of their jobs has become of paramount importance to them (Coupe, 2019; Oosthuizen, 2019). Governments, too, are increasingly concerned that robots will replace human employees, which will increase unemployment and put great pressure on governments (Nam, 2019).

We live in the era of knowledge, and employees depend on knowledge to ensure the sustainability of their jobs (Ferreira et al., 2018; Barthauer et al., 2020; Chin et al., 2019). Over time, knowledge has become an important resource for all institutions and companies, especially in light of technological progress, where it plays a vital role in the success of both employees and organizations.

Empirical studies dedicated to KM have examined its direct effect on sustainability. However, the basis of such research can be traced back to business literature concerning the relationship between the two variables (Chopra et al., 2021; Martins et al., 2019). In addition, KM has been extended to other fields, covering additional KM dimensions (TKM, CKM, SKM and HKM), with special focus on organizational sustainability (Demir et al., 2021). This extends to KM factors (Bibi et al., 2021) with potential to be determinants of the conditions under which single KM dimensions may be suitably adopted over others.

In contrast, empirical studies concerning the direct relationship between KM and sustainability have attracted less attention, and this paper makes several contributions to knowledge and sustainability research. The first is to explain the conditions under which KM contributes to the science of CS. While research on dynamic knowledge capabilities is enhanced by advancing the capabilities and skills of employees, it is paradoxical that employees suffer from the anxiety of losing their jobs because of massive technological advances (Arntz et al., 2020; López-Cabarcos et al., 2020), and governments’ and employees’ fear of job loss and increased unemployment (Bhargava et al., 2021; Ionescu, 2019). To the best of the author's knowledge, this is a global pioneering study that tests the effects of KM dimensions on CS. It is the first to investigate the moderating role of HC on the KM–CS relationship. There is therefore little or no information on the influence of firms’ performance, in terms of their actual activities and their resource investments, to address the knowledge generated. Thus, this study aims to fill this gap in the literature.

The main objective is therefore to investigate the role of HC as a moderating variable on the relationship between the KM dimensions (TKM, CKM, SKM and HKM) and CS. The study specifically focuses on HC activities connected to the knowledge mix and their interaction with the four KM dimensions. HC is referred to as optimal performance, where skills and experience views are linked to approaches to sustainability achievement. The moderating variable choice is built on the resource-based theory that considers HC and KM strategies as those that contribute to generating and enhancing knowledge to satisfy the requirements of the present era in light of sustainable careers.

Directed towards the above objective, this study is organized in the following way: in the first section, the introduction of the study is presented. The second section subsequently sets out the literature review, formulation of hypotheses and the development of the study framework (see Fig. 1). The third section provides a description of the methodology adopted, as well as a detailed description of the research design and data collection and analysis methods. In the fourth section, the empirical results from the data analysis are presented, while the fifth section discusses the implications of the findings and provides recommendations based on them. The paper ends by identifying the limitations of the study and makes recommendations for future studies. The paper's primary hypotheses are formulated from the study sample of Malaysian public universities. The results support some hypotheses and reject others. The analytical framework is validated in light of the moderating role of HC on the relationship between KM dimensions and CS.

Fig. 1.

Study model.

(0.11MB).

This paper focuses on Malaysian public universities as the sample study and population frame. Public universities were chosen for several reasons. They have higher levels of government funding and support because they have access to modern technology, including robots. Second, the top universities in Malaysia are primarily public universities. Most importantly, CS in public universities is further developed than in private universities from data obtained from the 2021 Malaysian Educational Statistics, Ministry of Education Malaysia. A total of 31,740 lecturers are employed in Malaysian public universities. Based on Krejcie and Morgan's (1970) sample size determination table, sample size increases at a diminishing rate with increased population, remaining constant at 380 with a population numbering between 30,000 and 40,000. Thus, this study required at least 380 responses, although, because of poor survey response rates, 700 copies of the questionnaire were distributed. The study assumed that the larger the sample of the study, the more the results could be generalized to the population under study. The researcher adopted a sampling method that ensures the collection of authentic and accurate information from the population regarding the study variables (HC, KM dimensions and CS). A random sampling method was applied to the population and questionnaires were distributed using e-mail. Of the 400 sent out, only 279 were returned, of which 251 were found to be appropriate for further analysis.

The stage after data collection was data analysis, but, first, the raw data collected from the field was screened and cleaned to ensure that it was suitable for the main analysis (Alqershi et al., 2022b). The data screening procedure was done to identify missing values and to detect mistakes committed by the respondents during entry of data. This stage was performed with the help of SPSS software. Descriptive analysis was then carried out, also utilizing SPSS in order to determine the characteristics of the sample in terms of age, working experience, education level, gender and number of employees. This is to ensure that the researcher understands the demographic composition of the sample (Huff and Tingley, 2015) (see Table 1). The hypothesis testing and data analysis were then carried out using Smart-PLS version 3, PLS-SEM.

Finally, with regard to measurement of the study variables, the moderating variable HC was adopted from Sharabati et al. (2010), while the KM measurement items were adopted from Chuang (2004), and the CS measurement items from Chin et al. (2021). Responses to all items were measured using a five-point Likert scale that ranged from 1, denoting strongly agree, to 5, denoting strongly disagree.

KM encourages changing culture and stimulating innovation by supporting freedom of thought, and contributes to helping employees adopt the approach of change and support ideas that result in so-called innovation. Innovation is linked with knowledge in building educational organizations, and, in the current economy, it is not possible for an organization to succeed without embracing and believing in a culture of knowledge, promoting and supporting new ideas and rewarding employees accordingly.

We found that TKM, CKM and HKM are significantly related to CS, while SKM is not. We can summarize the relationship between the KM and CS of universities as follows.

First, HKM is the most critical and important part of KM. Examples include employees’ unique knowledge, expertise that can be used for creating wealth, management strategy and capacity for dynamic learning in a highly competitive environment. As HKM can significantly affect CS, we emphasize the need for long-term investment in it.

Second, optimizing TKM maximizes the benefits of CS. This resource can contribute to CS by interacting with CKM because it contains several factors, such as an organization's culture, mechanisms and essential, advanced and innovative knowledge that enables the company to lead its industry and its competitors and distinguish itself from them.

The third line of investigation is consistent with hypotheses 2a, 2b, 2c and 2d, looking into the moderating influence of HC on the relationship between TKM, CKM, SKM and HKM, and CS in Malaysian public universities.

The previous section discussed the direct relationships between TKM, CKM, SKM and HKM and CS, but, on the basis of the study's findings, such direct relationships can be enhanced or mitigated by HC in Malaysian public universities. Stated clearly, HC has a moderating role on the respective relationships between TKM, SKM and HKM and CS. This implies that the higher the HC level, the higher the performance, and this holds true in the opposite direction. The study's support for a moderating effect is a considerable contribution to literature on the study variables and the phenomenon under study.

The literature review found mixed results for the relationship between KM and CS (Reich et al., 2014; Gorelick and Tantawy‐Monsou, 2005). Based on these inconsistent findings, this study found it appropriate to introduce a moderating variable to the relationships. It contributes by examining HC's moderating role in the dimensions of KM relating to CS. The rationale is that, in the current competitive and dynamic market, only the most useful elements of HC and knowledge enable establishments that strive to enhance their performance via the adoption of business processes and those that adopt sustainability have the most promising futures. In fact, knowledge and HC practices are the key solutions among universities desirous of pursuing unparalleled business performance.

In addition, to test the study hypotheses and answer the research questions, we formulated the previous moderating effect hypotheses. The result of the moderation test for HC on the relationship between KM and CS indicated that HC significantly moderates the relationship between TKM, SKM and HKM and CS, but not CKM and CS. This is in line with previous studies. Vaid and Honig (2020) argued that HC has a significant relationship with KM. However, ours is the only study which focuses on the direct relationship between this IV and DV, with HC playing an important role on KM and CS.

From the above results it is clear that universities build an educational environment in which knowledge is of paramount importance, as knowledge is a force in the era of the digital and knowledge economy. Universities must establish an environment in which a culture of learning and the production and sharing of knowledge spread in order to achieve benefit, creating an environment that encourages employees to learn from the flow of information and knowledge. Data is the raw material for information, converted by organizations and individuals into information, which in turn becomes knowledge by transferring, enhancing and enriching to provide an environment that supports this learning and exploits ideas, turning them into inventions and innovations and disseminating the results of these innovations in the organization for others to learn from and improve their skills. Building a culture of learning also allows employees to evaluate themselves and the units in which they work, organizing them with the aim of finding an approach to improvement and development, which in turn leads to the formation of distinctive knowledge that makes robots just an alternative for humans in providing knowledge to humans and in the education process. It remains the key to creating knowledge and it is the first provider in the educational and even administrative process. Finally, in preparing future-ready professionals, it will assist organizations and other actors in preparing personnel for future changes by promoting and enabling knowledge sharing, capacity building and thoughtful leadership.

Although all managers are encouraged to pay increasing attention to knowledge, our study indicates that it is not just knowledge that directly affects the sustainability of careers. The results of our study confirm the need to concentrate on keeping pace with technological progress through the human factor.

KM acts as a driving force that affects the development of employees’ capabilities, which in turn has implications for the sustainability of careers. Managers should therefore not only focus their efforts on embracing a culture of knowledge but should also devote attention to the quality of the knowledge that their employees have to acquire.

Currently, individuals may have difficulty not only in obtaining a job but also in sustaining their careers. Hence, managers need to understand the relationship between unique knowledge capabilities and the sustainability of careers so that they can monitor the internal process and focus their efforts on developing the knowledge capabilities that will help their employees to work with technology, rather than being replaced by it.

Our study focused on the impact of KM dimensions on the sustainability of careers in public universities in Malaysia, a concern for all workers, especially with the technological developments that threaten the human element in all types of business. CS is thus an important and fundamental concept for employees and organizations in general, and all branches and fields of administrative work.

Given the scarcity of research dealing with the concept of CS, the topic has not been seriously addressed at university level or investigated through its connection with other elements such as KM and HC. This is because the focus of most studies on business sustainability is on organizations and not individual careers in an age of technological progress. This is especially true in the education sector, which is closely related to the goals and trends that employees seek to achieve.

As became clear through the spread of the coronavirus, millions of people lost their jobs because of the pandemic's impact on the world economy. However, the crisis does not lie only in the pandemic, as millions of employees lost their jobs during the last year as demand increased for robots. The robot is crowding more and more people out of their jobs, especially as it does not cost the project owner any salary, and can work accurately and precisely for a longer period.

The robot has been and will be resorted to, in light of the current crisis, because it is completely immune to the coronavirus, while the human employee remains vulnerable to infection. The epidemic period also witnessed an unparalleled increase in demand for technology products amid a focus on remote work to ensure social distancing.

As many services continued, in light of physical closure, through use of the internet and technology, this remarkable development will push university administrations and organizations as a whole to make fundamental changes in the future, as it is very possible to rely on robots in the education process. Our study offers an insight into the importance and role of knowledge, which all workers in the education sector and universities must consider in keeping pace with these changes, focusing on increasing their knowledge to the highest degree in order to retain priority in the educational professions.

Given that all studies indicate that robots will eliminate millions of jobs over the next few years, and as the pandemic has accelerated changes in the workplace, this is likely to exacerbate inequalities. It is certain that top management will accelerate plans for the digitization of work and implementation of new technologies. In this context, over the next few years, many workers will have to learn new skills. It is expected that, by 2025, employers will divide their work equally between humans and machines.

Finally, there are some tasks in which humans will retain their competitive advantage, including management, consulting, decision making, thinking and interaction, as well as functions related to the green economy, advanced data management, artificial intelligence, new roles in engineering, cloud computing and product development. Workers must prepare themselves with the cognitive capabilities to face the fierce war that will take place between them and robots and automated equipment.

Our work has very important recommendations. It has expanded the scope of existing knowledge of KM, HC and CS, and deepened our understandings of these areas. A major finding of this study relates to the moderating effect of HC variables on the relationship between KM and CS. No previous studies have investigated this relationship. The implication is therefore that the moderating effect of HC variables must be taken into consideration in order to understand the effect of KM dimensions on CS.

All workers must think about their strengths and be enthusiastic to add new knowledge and focus on KM instead of waiting for events to unfold, as this is related to a battle between machines and HC. It represents a union between the two parties – workers and organizations must therefore implement the method of continuous learning, increase human capabilities, improve the influence of people in organizations and keep pace with continuous changes in technical developments. In addition, unions and employers must search for practical solutions that encourage adaptation, flexibility and openness to new ways of working by:

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  Preparing and equipping employees to keep pace with future changes by enhancing their technological capabilities.

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  Enabling organizations to work to inform employees of the danger of technology to their jobs, as it is necessary to conduct intensive courses and programmes that explain in detail how digitization can take on a large part of future jobs, in order for employees to develop themselves and increase their knowledge to face these changes and maintain their jobs.

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  Maintaining frankness with employees about the total number of jobs that can be given to technology, replacing employees.

Our work is subject to several limitations that may be addressed in future studies. First, our study uses a measure of KM dimensions based on TKM, CKM, HKM and SKM. Extant studies on KM, however, indicate that other factors may impact their role. More work is therefore needed to test how other KM factors can play a significant role in CS in different contexts and populations.

Second, although universities and other educational institutions are two central factors that provide employees’ knowledge, governments and international agencies also have responsibilities. Future studies may thus consider these factors in research frameworks and there is contribution to the field of knowledge.

Third, while responses to the survey were obtained from a wide range of Malaysian universities, future studies could focus on other countries in southeast Asia, Europe and America.

Fourth, the population of the study was limited to public universities in Malaysia. Our work recommends that other sectors of the Malaysian economy be investigated, such as CS in hotels and manufacturing.

Finally, it would be desirable to investigate other marketing performance metrics such as growth in sales, the successful launch of new products and customer retention. The benefits of KM may extend beyond CS, and we speculate that other potential dependent variables or other moderators, such as organizational learning and artificial intelligence, may be explored. In addition, our work used HC as a moderator. Future studies could test others key variables as moderators, such as organizational or innovation capital.