1. Lockheed Martin Performance-Based Master of Engineering in Engineering Management (ME-EM) Degree Program, University of Colorado Boulder, USA; rusho@colorado.edu
2. VNHK Academy of Leadership and Management, Hong Kong; chan@yandex.com

*             Correspondence: maher.rusho@colorado.edu

Keywords: risk management, small scale, medium size, industry

1. INTRODUCTION

As data innovation advances, groups are able to showcase computerized functions as the future standard. Representatives are therefore faced with new kinds of work that may reduce individual cooperation while increasing collaboration with IT [1]. These improved work approaches imply that people are unable to fulfill their obligations with the same attributes and beliefs that they are accustomed to [2]. The perception of one’s job at the workplace, or one’s self-convictions that comprise a proficient personality at work, is subject to a constant shift [3]. Being exposed to situations that are inconsistent with one’s values may cause a lack of self-assurance, which in turn may pose a threat to one’s personality [4]. As emerging innovations have altered the landscape and encounter of a variety of callings, this could adopt initiatives meant to preserve confidence associated with way of life. The desire for improved function as the new standard in associations is highlighted by the digitization of the work environment [5]. The true test for 21st-century associations these days is the organization’s capacity to grow in the face of an extraordinarily competitive market wherein shrewd positions are always emerging. Organizations are now integrating information technology into their administrative procedures due to the internationalization and globalisation of company sectors, innovation, item or service quality, and customer needs [6]. Without innovation, organisations cannot sustain their advantage in highly competitive situations [7]. Businesses have long looked to assign more labour to machines in order to reduce expenses and boost production. Human labour supplanted “manual labour” in the context of assembly lines and other mechanical and repetitive tasks [8]. Because of the digital revolution, the world has already transformed into a modern one where data dominates all economic activities. Data centers are no longer necessary for the storage of data (Lam, 2018). Thanks to sensors of all kinds, data may now be measured and produced by any object or environment. Modern, computerized (data) revolutions have a financial impact on all facets of our lives—business, work, and society at large. This implies the use of digital resources, which vary depending on the situation: Big data, augmented reality, 3D printing, quantum registering, artificial intelligence, and so forth [9]. As “the capacity of a machine to carry out mental roles that we partner with human minds, for example, seeing, interacting, thinking, collaborating with the climate, critical thinking, direction, and in any event, showing imagination [10],” artificial insight (computer-based intelligence) is one of the main themes driving this conversation. Artificial intelligence, which has undergone unmatched advancement over the past many years, continues to be the most amazing IT application available today [11]. It is defined as a collection of theories and techniques for creating machines capable of acting out knowledge. Artificial intelligence is the modeling of intelligent behaviour by a computer with little human intervention. Conversely, AI has provoked discussion among academics and industry professionals. Experts predict that artificial intelligence would result in the loss of millions of jobs and a rise in the unemployment rate [12]. This will result in additional challenges, including the requirement to reconstruct infrastructure, guarantee vehicle safety, and amend laws and regulations. Although artificial intelligence has several risks, including the ability to turn humans into machines, misjudge individuals, and have an unnecessarily high framework, it can support human resource work [13]. Simulated intelligence, in any event, may lead to worth co-obliteration when client differences occur. Furthermore, the application of simulated intelligence may also contribute to increased susceptibility to security breaches [14]. This unfavourable trait is frequently mentioned as the “clouded side of artificial intelligence,” a reference to the risks that simulated intelligence poses to individuals, groups, and society as a whole. According to a report by International Data Corporation, artificial intelligence is anticipated to be used in 40% of advanced transformation projects in 2019 and to power 75% of commercial applications by 2021. Organisations should rely far more on artificial intelligence to operate on their own in order to increase efficiency and support new administrations [15]. In light of this, the advancement of computer-based intelligence does not replace human labour; rather, it enhances it. However, the application of AI in organisations could create new jobs in fields such as interior design, programming, and even friendly places, without eliminating or altering existing positions [15]. Artificial intelligence and its financial implications are constantly promoted. Even while the public discourse on AI has recently shifted towards optimism, the fear that AI simulations would replace existing jobs outweighs the opportunities for collaborative AI efforts involving humans [16]. Artificial intelligence might not directly affect worker engagement or performance at work when other factors are taken into account. The moderating mechanism selected to explain the effects of Artificial Intelligence on work commitment and employee performance is change leadership [17]. The effectiveness of an organisation is largely determined by its leadership. The organisation can optimise the potential of implementing Artificial Intelligence by assuming the leadership position. Authority is the capacity to positively influence others in order to accomplish hierarchical goals [18].  The advent of Artificial Intelligence (AI) in the workplace has brought up several practical and ethical issues. Artificial intelligence (AI) has the ability to boost output and efficiency as well as enhance worker performance, but it also has the potential to disrupt job markets and replace current workers. It’s critical to evaluate the potential effects of AI technology on worker performance, job security, and general work experience as it develops [19]. This issue statement aims to investigate the possible effects of AI’s development on worker productivity and performance. It specifically aims to determine how AI might influence how workers operate, what kinds of jobs they are required to perform, and how it might effect job security [20]. It will also examine the possible advantages and disadvantages of implementing AI in the workplace, including the possibility of higher productivity, better accuracy and speed, and a reduction in human error. Lastly, it will evaluate the moral ramifications of employing AI to track and enhance worker performance, taking into account the possibility of job displacement and unjust labour practises [21]. This analysis will contribute significantly to the body of knowledge about how artificial intelligence impacts workers’ productivity and commitment to their work. The current study intends to provide a succinct assessment of current research in this field, make recommendations for future research, and thoroughly examine artificial intelligence and its impact on job performance.

2. RESEARCH METHODOLOGY

Finding data utilizing the selection criteria for study is the aim of the multi-phase coordinated search technique. Phase 1 entails researching the seven key websites; compile your results. Phase 2: Look through the list of references for every chosen inquiry topic to find any particularly noteworthy articles. Academic e-databases were utilized in the computerized search strategy for this inquiry. These phases result in small- and medium-sized industry risk management. To gather the relevant literature, we used a number of significant electronic databases, such as Science Direct, Sage, and Springer. It is thoroughly examined during scanning measurements, and one of the author’s hypotheses may contain data that is important for doing a literature analysis. Three study assistants separately assessed each component’s quality at that point. Essentially, the aforementioned search methods were used to extract academic publications. It’s possible that significant posts remained after unnecessary documents were eliminated. Only potentially significant studies remained after irrelevant studies were removed by skimming the titles and abstracts. Reports that were left unfinished provide guidance on specific goals or directions for system architecture (and were not generalize). We used the following quality assessment questions to examine each of the investigation’s papers in more detail: Is the field or category correctly described? 2. Is the essay supported by research, or is it just a collection of educated observations compiled by experts? 3. Are the objectives of the research well-defined? 4. Is the environment in which the research was conducted adequately described? 5. Does the research plan effectively accomplish the objectives of the study? 6. Is the last statement clear and succinct? Does the research cover every facet of risk management in thoughtful small- and medium-sized businesses? 8. Does the research offer a synopsis of the subject? 9. Is an established design used in the study article to aid in the analysis? N (No) = 0 or indeterminate (i.e., no explicit data was provided), P (Partially) = 0.5, and Y (Yes) = 1. This is how the article’s ranking system was created. Only 16 out of the 44 items that underwent thorough analysis in order to facilitate data extraction and aggregation satisfied our inclusion criteria. Right now, the goal is to provide access to paperwork that contains trustworthy details regarding the preliminary investigation. Along with other basic data, the author, year, study techniques, country, and context were all extracted. The investigation’s objectives were taken into consideration when selecting these products. Data were collected, scrutinized, and subsequently extracted. There is more information regarding the results of the merging process in the sections that follow.

3. RESULTS AND ANALYSIS

Parameters from each of the selected publications were to be extracted during the data extraction phase. During the search process for this evaluation, 16 documents were located. The amount of research that we select from internet databases is shown in Figure 1. On the other hand, only three papers came from the MDPI and Springerlink databases, while the rest (n=5) were sourced via the Science Direct database. In addition, Figure 2 shows the chronological history of investigations over time.

Figure 01: Papers in seven selected electronic databases

Figure 02: Chronological order of studies over time

To assess the overall quality of the included research, we used nine quality assessment questions. The maximum score for each question is 1, and the aggregate score was 9. Figure 3 demonstrates the high value of the chosen research, which has a quality score above 5, and more details may be found in Appendix 2.

Figure 03: Quality assessment of all included studies

This study demonstrated that 75% of the selected researchers employed survey research as their primary quantitative strategy. However, just around 25% of all papers using qualitative approach were conceptual papers, empirical investigations, or case studies. The examination of a few selected studies showed that industries have been studied in a variety of settings and countries. Study investigated the small and medium sized industries relevant to RM in eight various scenarios, including two distinct contexts.

4. DISCUSSION AND CONCLUSION

The relevant studies that identified risk management in small and medium-sized industries were chosen for this evaluation. We looked at five datasets. In addition, abstract analysis, title and keyword analysis of the results, and inclusion and exclusion criteria were used to choose the papers. Our study predicts a large increase in the number of articles reporting studies starting in 2022. Therefore, research on the relationship between industries and risk management included examples of growing trends in the industry. Studies that use risk management in small-scale industries, however, have received less attention. Sixteen articles from 2015 to 2023 were chosen. The research methods used in the selected articles were then examined. While other approaches were employed less frequently, the survey strategy was employed in the majority of research. We wanted to increase the level of knowledge in this new field through these contributions, as well as our study of the topic and the development of a future research agenda based on a comprehensive evaluation of the literature for the preceding ten years. We inform the research audience about the expanding possibility for further study and contribute to the corpus of knowledge on risk management in small and medium-sized businesses. Even though research on this topic is still in its early stages, it is critically needed to be further explored through rigorous conceptual and empirical work in order to keep up with the rapid changes and unstable nature of the corporate environment. Together with the other papers in this special issue, this review will increase understanding, provide guidance for risk management in small and medium-sized businesses, and encourage creative ideas.

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