The Impact of Digitization on Work Performance
digital technologies are being used to help businesses facilitate employee work, improve productivity, make customer service lighter and faster, increase sales, and improve the Coordination of organizational work, and companies must use a balanced symbiosis of human and computer intelligence to create new work experiences. Currently, there is a significant lack of empirical research on how the use of new digital technologies (artificial intelligence) affects the work experience of employees. As such, this study aims to analyze how the work experience of employees who use new digital technologies can be improved. A survey was taken of 92 employees who work in organizations that use digital technologies with artificial intelligence (sales, service stations, restaurants, health care facilities and others that sell cosmetics). The study aimed to indicate that job enrichment, choice and stress management, higher sensitivity, better working conditions, less travel, more payments, job security and protection, more careers, dealing with change, Privacy, working with strangers, identification are key factors in the activities of organizations that use artificial intelligence, which play an important role in the work experience of employees and can directly affect the work experience of employees. So, using the results of this study, it is expected that service organizations will be more effective and profitable if they use artificial intelligence optimally to improve the work experience of their employees.
Introduction
The new digital technology (AI) can improve and develop services and work by collecting and processing information about the services, purchases and options that customers and customers have previously received, facilitate human labor, and create methodological recommendations for replacing them with computers and artificial intelligence. This has an impact on the performance of a wide range of tasks, including product and service procurement activities, service patterns based on customer demand and preferences, and effective product and service recommendations (Maras, 2020). The expected benefits include increasing the level of automation, reducing costs, increasing flexibility, and optimizing customer relationships. To fully realize these benefits, it is necessary to study and understand this complex phenomenon more deeply. The dependence on artificial intelligence technologies and the need to increase the amount of information related to the functions of users, customers and the employee who provides services to them, as well as the process of work performance, are important to increase the work experience of employees (Dwivedi et al.2019). It is necessary to understand that the work experience of employees who use digital technologies with artificial intelligence can
(A) improve the quality of Service,
(B) adjust their creativity consistently in their work,
(C) the entire experience of using artificial intelligence can effectively make a difference for customers and customers to perform services at a light and professional level. While these issues can play an important role, previous studies have focused primarily on the use of artificial intelligence from a technical and organizational point of view (Jarrahi, 2018). As a result, there is a lack of research on how employees perceive artificial intelligence technology as part of their work performance and work experience, and how it creates a more pleasant experience and stronger relationships with consumers and customers (Shank et al., 2019; Wang, Molina & Sunder. 2020). Therefore, this study aims to examine and analyze how the work experience of employees who use artificial intelligence can be improved by integrating it with artificial intelligence in service organizations. For this, we provide quality service design (Parasuraman et al., 1994) and using a model based on employees ' work experience. Our model summarizes the work experience of employees who use AI through service quality factors in organizations that use artificial intelligence
Theoretical basis
Manulang (2013) Work experience. work experience is the process of creating knowledge and skills about the method of work due to the involvement of the employee in the implementation of work tasks. According to the (2015), work experience is the knowledge, skills, and skills that employees acquire to fulfill their previous tasks. Since the advent of industrial robotics in the 20th century, there has been a common saying that robots will replace "dirty, dangerous and boring" jobs and create a more flexible workforce (Engelberger, J. F. 1980). However, since this time, history has changed: robots are intended to work with them, rather than replace human work. Now the work of Man and robot is no longer separated by a mesh, but both work together, close and harmoniously. This combination of human-robot work is not so simple.
HRI-'s research has examined many dimensions of this problem, from technical to socio-technical, in the context of production. The socio-technical perspective is especially important, because all technologies interfere with work, and robots that move or replace work much more. Robots are designed and integrated into the workplace, so it is important for organizations to consider how their disruptions affect human resources. Even minor changes in work tasks and environments have a significant impact on human experience, such as motivation, satisfaction, fatigue, and cognitive demands. It is important to understand how the design and integration of technology changes the human experience, thus allowing the organization to take advantage of opportunities and avoid accidental damage to human resources. Recently, digital technologies have radically changed the working situation. These changes are used and observed in the workplace to gain experience. Changes caused by technology affect work experience in several ways. Combined, such changes continue to be a major problem in human resource management strategies (Robert E. Sibson 1992). There are dozens of ways in which growing technology affects work experience. Before determining how technology affects work, you will need a few general explanations of its impact.
Knowing that technology has a significant impact on work experience, it will be easy to determine how work technology can optimally integrate work experience.
The impact of technology on work experience will be even more significant when people's ability to change fully matches the speed of change (Philip Kotler et al., 2021). In most cases, technology changes work experience in many ways over a period of time. Most of the changes will not go unnoticed for a short time and, of course, will not become a major event in a year.
Also, admit that the total period of introduction of some large technologies and their widespread use is generally very long; sometimes there is a thirty-year strategic planning cycle. This means that changes in work experience due to technology can span many generations. Workers of the first generation can perceive changes that are greatly broken and crumbling, and workers of the third generation can perceive normal working conditions. Generation X and Generation X are still dealing with computers in a bad way, but Generation I and Generation Alpha use the same way they use pencils. (Robert E. Sibson 1992).
High-tech with high sensors. The technology requires high touch. High touch refers to the ability to communicate and communicate privately, compared to lonely work or just communicating with a machine. However, without personal communication, we do not know much about high-tech work. Knowledge work implies interpersonal communication. The technology has brought smaller sizes and compact operation, which means more touch. The technology includes communication technology, which is relatively high-touch (Robert E. Sibson 1992).
There are high-tech jobs that have little to do with others. But at the same time, there are many jobs in automated factories, offices and warehouses, where there is little personal communication. It is not exactly clear how much the personal senses will want to be at work. There is no research or experience to determine the optimal amount of personal communication at work to ensure personal satisfaction at work or to ensure high quality of work. We know that technology automatically gives a higher feeling and that technology is one of the ways that it affects work experience.
Enrich the work.
High technology at work means the use of more knowledge. The use of more knowledge generally implies a greater diversity of work. More diversity means enriching the workplace. More diversity means enriching the workplace. High technology leads to the enrichment of the workplace in several ways (Robert E. Sibson 1992). Years ago, job enrichment was considered an important topic in Human Resource Management. Some large companies had work enrichment specialists and separate departments to do this work. In the machine-driven industrial age, more machines meant performing simpler, repetitive tasks. Work has become a method beyond the endurance of the human nervous system, and it has become necessary to enrich the workplace. HR professionals worked to transform the workplace, change the situation, and provide additional elements of the job to enrich the workplace (Robert E. Sibson 1992). As technology became a workspace, job enrichment was never great, in part because conventional jobs were replaced by computers and robots, and technology automatically enriched many other jobs.
Better working conditions
Another way that technology can significantly affect work experience is related to working conditions. In general, high technology automatically improves working conditions. In a general sense, high technology turns part-time skilled jobs into jobs for paid technicians, and unskilled jobs into more skilled jobs. High technology is turning the often repetitive clerical work into a machine-operated workplace managed by workers. Some high technologies are turning unskilled jobs, such as clerical positions, into administrative jobs.(Robert E. Sibson 1992).
In most cases where there is more high-tech, working conditions are becoming more conventional, clean, less noisy, less physically demanding, less dangerous, or a combination of these. Almost always clear and high-tech work leads to a significant improvement in working conditions. In most cases where there is more high-tech, working conditions are becoming more conventional, clean, less noisy, less physically demanding, less dangerous, or a combination of these. Almost always clear and high-tech work leads to a significant improvement in working conditions.
Better working conditions are high-tech, partly because of the nature of the work involved in high-tech work, and partly because of the often high cost, and partly because of the higher cost in better conditions. In high-tech work, the cost of things like equipment is relatively cheap compared to the salary of professionals.
Some argue that, in fact, the cost of better conditions is simple, and the effect of better conditions on the productivity of professional workers engaged in technological activities is very great. It would seem reasonable, but reasonable and doubtful has not been proven. It is important to consider the impact of technology on work experience. It should be remembered that high technologies automatically lead to improved working conditions. This happens due to the nature of the work, and it is not necessary to create it artificially.
More fees.
Technology affects wages. More technology always requires more knowledge,in an unregulated labor market this often means more wages. Higher wages are another result of larger technologies (Robert E. Sibson 1992). Every day, more and more high-tech is happening around the world, and the media is unable to report events of this kind. However, looking at the overall business experience, high technology almost always means high wages, and the average net salary has been constantly increasing for many years. Payments for knowledge work are increasing due to the labor shortage that is common in technological work and the shortage of qualified people, which is indirectly related to technology. Labor shortages can lead to the innovation and training of Technology job candidates, stretching down the career ladder, and making the job efficient in the use of the technology being used by the company. All these factors lead to the fact that more people pay.
Job security.
In the factory environment, where the mentality prevailed in the past, occupational safety means a preference for dismissal. Safety at work means that there must be enough years, and the reduction in production does not affect a person. Safety at work means freedom from worries about the closure of the plant. If everything else fails, then occupational safety means salary from work, payment for unemploymen (Robert E. Sibson 1992).
In the present and future planning of technology jobs, job security means that the majority of knowledgeable workers will be able to take another job. In most areas of knowledge work (not just in areas with chronic labor shortages), the average employee engaged in technology work can always get another job in the same geographic area. Better than the average worker will find many opportunities. Technology has been blamed for job loss. In fact, technology means many new jobs, benefits and more security. Technology means a new direction of work in the field of new technologies. For knowledgeable workers, technology implies the possibility of finding many jobs. Technology is increasingly improving the ability to get another job. The balance of technology improves work safety.
Privacy.
Higher technology means that we are less aware of our work environment. In this technological age, workers use a lot of machines and equipment, but there is practically no understanding of how many of them work. Therefore, technology has brought a lot of secrets to work, which changes the work experience. Many years ago, Education was enough knowledge in all areas so that a knowledgeable employee understood the technologies of work. Educated people understood how things work. They had concepts of machines, axes, mechanical things, physics and electricity. Today, a lot of work has become mysterious.For example, most people who work at a computer don't understand how computers work. Understanding technology at work has been a positive factor in working conditions. This was also helpful, since it was assumed that such knowledge would be enough for the worker to ask the right question and be able to understand the answer (Robert E. Sibson 1992).
Now education is a sample of existing disciplines and areas of knowledge. Beyond the experience of university and college education, you can't learn enough to have a basic understanding of all disciplines. For some, this lack of familiarity can separate them from their work experience and sometimes affect productivity management.
Work with strangers. Relationships with other employees vary in the technological world. Many years ago, most people knew everyone in the office and in the store and had known them for many years. Now we spend a lot of time working with strangers. This changes the work experience. Working with strangers implies privacy. For those who value privacy, technology indicates greater satisfaction at work. However, for many people, random behavior by people you know interferes with work. Randomness in work relationships is part of relationships at work that are closely intertwined with family and community. We are becoming strangers wherever we go. Technology tends to produce more modest operations. This means that fewer people are employed in the work. This means that a small number of working relationships have a tendency to get closer in the short term. There are people who think that casual communication at home and in the community makes employees develop close relationships at work. In a social environment, they may seek intimacy, even if some workers form contact with fewer people or fewer workers. This could be part of a future challenge for Human Resource Management professionals to build close relationships among employees who do not work with each other for a long time or continuously.
Conceptual framework of the study
The principal conceptual basis of this study was drawn from the methods used by Robert E. Sibson (1992), Manulang (2013), Marwansyah (2015), Philip Kotler (2021), and other scholars to measure and evaluate technology and work experience. Based on the theories and methodologies of these scholars, the framework and model for this study are presented as follows (Figure 1).
Figure 1. Research model/framework for the impact of technology on work experience:
According to the proposed model, the hypotheses regarding the relationship between technology and work experience are tested using correlation analysis and factor reliability values as evaluation criteria.
Research hypotheses
H1 – Technology is directly expressed through high touch, job enrichment, better working conditions, higher pay, job security, privacy, and working with strangers.
H2 – The technological dimensions of high touch, job enrichment, better working conditions, higher pay, job security, privacy, and working with strangers have a direct positive effect on work experience.
The study variables, technology and work experience, were evaluated by senior executives, CEOs, middle managers, and human resource managers working in business organizations.
Sample design
To determine the population size, a non-probability judgmental (purposive) sampling approach was used. For managers and executives in business organizations, the optimal sample size calculated using a SAMPLE SIZE CALCULATOR was 91. To increase sampling adequacy, it was deemed appropriate to include 92 respondents in the study. The optimal sample size was calculated by assuming a possible standard deviation of 4/3 = 1.333, a confidence level of 95% = 1.96, and an allowable error of 0.2.
Descriptive statistics for the preliminary analysis results
Table 3. Demographic information
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Gender | Male | 41 | 44.6 | 44.6 | 44.6 |
| Female | 51 | 55.4 | 55.4 | 100.0 | |
| Total | 92 | 100.0 | 100.0 | ||
| Age | 22-30 | 15 | 16.3 | 16.3 | 16.3 |
| 31-40 | 29 | 31.5 | 31.5 | 47,8 | |
| 41-50 | 43 | 46.7 | 46.7 | 94.5 | |
| 51 and above | 5 | 5.5 | 5.5 | 100 | |
| Total | 92 | 100 | 100 | ||
| Education | Higher education | 84 | 91.3 | 91.3 | 91.3 |
| Vocational education | 2 | 2.2 | 2.2 | 93.5 | |
| Complete secondary | 6 | 6.5 | 6.5 | 100 | |
| Total | 92 | 100 | 100 | ||
| Academic degree | Doctorate | 1 | 1.1 | 1.1 | 1.1 |
| Master’s | 34 | 36.9 | 36.9 | 38 | |
| Bachelor’s | 55 | 59.8 | 59.8 | 97.8 | |
| Other | 2 | 2.2 | 2.2 | 100 | |
| Total | 92 | 100 | 100 | ||
| Position | Senior management | 29 | 31.5 | 31.5 | 31.5 |
| Branch/unit director | 6 | 6.5 | 6.5 | 38 | |
| Middle management | 47 | 51.1 | 51.1 | 89.1 | |
| Lower management | 10 | 10.9 | 10.9 | 100 | |
| Total | 92 | 100 | 100 |
A total of 92 managers from 71 business organizations participated in the study. Of them, 44.6% were male and 55.4% were female, and all participants were aged 22 and above. The majority, 91.3%, had higher education, while 96.7% held bachelor’s or master’s degrees. The departments and units in which the respondents worked were relatively evenly distributed.
Reliability of the questionnaire: The average score for each item was calculated using Cronbach’s alpha. As the Cronbach’s alpha value approaches 1, the combined items indicate the construct more clearly. Based on the preliminary criterion, the Cronbach’s alpha coefficient is significant at a = 5%. This analysis was used to determine whether the characteristics of each variable used in the study reliably represented the research results. In this study, the Cronbach’s alpha coefficients ranged from 0.677 to 0.838, all above 0.5, indicating that the variables used in the study were reliable.
Data Collection Process
The study was conducted using a questionnaire survey beginning in November 2023 through face-to-face meetings in which respondents completed the questionnaire individually. To enhance the realism of the findings, participants were selected using a non-probability judgmental, or purposive, sampling method.
Data analysis method. The data for the study were processed and analyzed using SPSS 23.
The results of factor analysis were used to determine whether the dependent variables and the independent variables representing them could express the study results validly and reliably. Participants evaluated the characteristics of technology and work experience using a five-point Likert scale. Multiple regression analysis, distribution analysis, and correlation analysis were used to measure the relationships among variables, along with tests to determine whether the model was statistically significant.
Before conducting factor analysis, the Kaiser-Meyer-Olkin (KMO) test for sampling adequacy and Bartlett’s sphericity test for variable interdependence were examined. A KMO statistic of 0.5–0.6 was considered moderate, 0.7–0.8 good, 0.8–0.9 very good, and above 0.9 excellent.
Analysis and results
Factor analysis results
In this study, the principal component analysis (PCA) method was used to examine technology in business organizations.
Table 4. Factor analysis results for the relationships among the technological characteristics of business organizations
| № | Technological characteristics | Expected values | |||
| KMO | Bartlett’s | Sig | Variance | ||
| 1 | High Touch | 0.731 | 212.666 | .000 | 36.232 |
| 2 | Job Enrichment | 0.532 | 14.671 | .023 | 34.902 |
| 3 | Better Working Conditions | 0.736 | 142.826 | .000 | 31.528 |
| 4 | Higher Pay | 0.553 | 119.017 | .000 | 44.132 |
| 5 | Job Security | 0.769 | 266.363 | .000 | 34.033 |
| 6 | Privacy | 0.715 | 107.169 | .000 | 75.651 |
| 7 | Servant Leadership | 0.779 | 99.279 | .000 | 61.461 |
| Average score | 0.688 | 137.427 | .003 | 45.420 |
Source: Factor analysis results
Factor analysis conducted on the seven variables used in the study showed a KMO (Kaiser-Meyer-Olkin) statistic of 0.689, indicating a good result. (A KMO value of 0.5 or above is considered valid.)
For Bartlett’s Test, the significance value should be below 0.05 at a 95% confidence level. In this study, Sig = 0.003, confirming that the factor analysis was significant. The variance coefficient was 45.4, which is a very good result (it is important for it to exceed 10).
Because the factor analysis of relationships assessed seven distinct characteristics representing technology, factor analysis was conducted for each of them, with the requirement that the resulting factor structure be consistent across the measures. By including the attributes that define technology, the analysis examined which characteristics loaded as factors.
Table 5. Analysis results for the technological characteristics of business organizations
| Technology | Component matrix (a) | |
| 1 | ||
| 1 | High Touch | 0.712 |
| 2 | Job Enrichment | 0.586 |
| 3 | Better Working Conditions | 0.759 |
| 4 | Higher Pay | 0.819 |
| 5 | Job Security | 0.658 |
| 6 | Privacy | 0.582 |
| 7 | Working with Strangers | 0.736 |
The above results indicate that all seven technological characteristics demonstrated good explanatory power.
Reliability analysis results.
Examining the extent to which the characteristics represented each dependent variable, in other words their internal consistency, is important for determining research reliability. Reliability was therefore expressed using Cronbach’s (1951) alpha coefficient. Each variable was calculated separately in the analysis.
Table 6. Reliability analysis results for each variable
| № | Variable | Cronbach’s alpha value |
| 1 | High Touch | 0.743 |
| 2 | Job Enrichment | 0.772 |
| 3 | Better Working Conditions | 0.819 |
| 4 | Higher Pay | 0.838 |
| 5 | Job Security | 0.794 |
| 6 | Privacy | 0.838 |
| 7 | Working with Strangers | 0.789 |
Source: Results of the data reliability analysis
The Cronbach’s alpha coefficients ranged from 0.743 to 0.838, indicating that the characteristics represented the corresponding dependent variables very well. The results of the factor analysis and reliability analysis confirm that the study measures were well-founded and reliable.
Correlation analysis of the study variables
Table 7. Correlation analysis of technological characteristics
| Correlations | ||||||||
| HT | JE | BWC | HP | JS | P | WS | ||
| High Touch | Pearson Correlation | 1 | ||||||
| Sig. (2-tailed) | ||||||||
| N | 92 | |||||||
| Job Enrichment | Pearson Correlation | 0.408 | 1 | |||||
| Sig. (2-tailed) | 0 | |||||||
| N | 92 | 92 | ||||||
| Better Working Conditions | Pearson Correlation | 0.581 | .523** | 1 | ||||
| Sig. (2-tailed) | 0 | 0.001 | ||||||
| N | 92 | 92 | 92 | |||||
| Higher Pay | Pearson Correlation | 0.516 | .541** | .597** | 1 | |||
| Sig. (2-tailed) | 0 | 0 | 0 | |||||
| N | 92 | 92 | 92 | 92 | ||||
| Job Security | Pearson Correlation | 0.639 | .506** | .622** | .643** | 1 | ||
| Sig. (2-tailed) | 0 | 0 | 0 | 0 | ||||
| N | 92 | 92 | 92 | 91 | 92 | |||
| Privacy | Pearson Correlation | 0.675 | .518** | .462** | .532** | .656** | 1 | |
| Sig. (2-tailed) | 0 | 0.002 | 0 | 0 | 0 | |||
| N | 92 | 92 | 92 | 91 | 92 | 92 | ||
| Working with Strangers | Pearson Correlation | 0.588 | .480** | .407** | .478** | .583** | .597** | 1 |
| Sig. (2-tailed) | 0 | 0 | 0 | 0 | 0 | 0 | ||
| N | 92 | 92 | 92 | 91 | 92 | 92 | 92 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
The results show that high touch is strongly and positively correlated with job enrichment (0.408), better working conditions (0.581), higher pay (0.516), job security (0.639), privacy (0.675), and working with strangers (0.588). Job enrichment is also strongly and positively correlated with better working conditions (0.523), higher pay (0.541), job security (0.506), privacy (0.518), and working with strangers (0.480).
Better working conditions are strongly and positively correlated with higher pay (0.597) and job security (0.622), and moderately positively correlated with privacy (0.462) and working with strangers (0.407). Higher pay is moderately positively correlated with job security (0.643), privacy (0.532), and working with strangers (0.478), while job security is strongly and positively correlated with privacy (0.656) and working with strangers (0.583). Privacy is also strongly and positively correlated with working with strangers (0.597).
Table 8. Analysis of the relationship between work experience and technological characteristics
| Correlations | |||||||||
| Work Experience | HT | JE | BWC | HP | JS | Priv. | WS | ||
| Work Experience | Pearson Correlation | 1 | |||||||
| Sig. (2-tailed) | |||||||||
| N | 92 | ||||||||
| High Touch | Pearson Correlation | 0.036 | 1 | ||||||
| Sig. (2-tailed) | 0.731 | ||||||||
| N | 92 | 92 | |||||||
| Job Enrichment | Pearson Correlation | 0.12 | -0.108 | 1 | |||||
| Sig. (2-tailed) | 0.254 | 0.307 | |||||||
| N | 92 | 92 | 92 | ||||||
| Better Working Conditions | Pearson Correlation | .329** | 0.181 | .323** | 1 | ||||
| Sig. (2-tailed) | 0.001 | 0.085 | 0.002 | ||||||
| N | 92 | 92 | 92 | 92 | |||||
| Higher Pay | Pearson Correlation | 0.204 | -0.006 | .541** | .397** | 1 | |||
| Sig. (2-tailed) | 0.053 | 0.956 | 0 | 0 | |||||
| N | 92 | 92 | 92 | 92 | 92 | ||||
| Job Security | Pearson Correlation | .305** | 0.139 | .406** | .422** | .643** | 1 | ||
| Sig. (2-tailed) | 0.003 | 0.185 | 0 | 0 | 0 | ||||
| N | 92 | 92 | 92 | 92 | 91 | 92 | |||
| Privacy | Pearson Correlation | .375** | 0.175 | .318** | .462** | .532** | .656** | 1 | |
| Sig. (2-tailed) | 0 | 0.094 | 0.002 | 0 | 0 | 0 | |||
| N | 92 | 92 | 92 | 92 | 91 | 92 | 92 | ||
| Working with Strangers | Pearson Correlation | .310** | -0.088 | .380** | .407** | .478** | .583** | .597** | 1 |
| Sig. (2-tailed) | 0.003 | 0.406 | 0 | 0 | 0 | 0 | 0 | ||
| N | 92 | 92 | 92 | 92 | 91 | 92 | 92 | 92 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
The results of the correlation analysis show that work experience is moderately associated with better working conditions (0.329), job security (0.305), privacy (0.375), and working with strangers (0.310), while it is weakly associated with job enrichment (0.120) and higher pay (0.204).
Figure 2. Results of the relationship analysis between technological characteristics and work experience
Job EnrichmentJob EnrichmentHigh TouchHigh Touch 0.036
Better Working ConditionsBetter Working Conditions 0.120
Higher PayHigher Pay 0.329
Job SecurityJob SecurityWork ExperienceWork Experience 0.204
PrivacyPrivacy 0.305
Working with StrangersWorking with Strangers 0.375
0.310
Work experience is moderately associated with better working conditions, job security, privacy, and working with strangers, while it is weakly associated with job enrichment and higher pay.
Table 9. Regression analysis
| Variables Entered/Removed | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Better Working Conditions, Job Security, Privacy, Working with Strangers, Job Enrichment, Higher Paya | . | Enter |
| a. All requested variables entered. |
| Model Summary | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | .588a | .346 | .291 | .55821 | .346 | 16.270 | 7 | 83 | .000 |
| a. Predictors: (Constant), Better Working Conditions, Job Security, Privacy, Working with Strangers, Job Enrichment, Higher Paya |
| ANOVAb | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 13.676 | 7 | 1.954 | 16.270 | .000a |
| Residual | 25.863 | 83 | .312 | |||
| Total | 39.538 | 90 | ||||
| a. Predictors: (Constant), Better Working Conditions, Job Security, Privacy, Working with Strangers, Job Enrichment, Higher Paya | ||||||
| b. Dependent Variable: Technology |
| Coefficientsa | ||||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | |||
| B | Std. Error | Beta | Lower Bound | Upper Bound | ||||
| 1 | (Constant) | 2.353 | .377 | 6.244 | .000 | 1.603 | 3.102 | |
| High Touch | .021 | .016 | .133 | 1.268 | .008 | -.012 | .053 | |
| Job Enrichment | .182 | .068 | .270 | 2.660 | .009 | .046 | .318 | |
| Better Working Conditions | .183 | .078 | .239 | 2.338 | .022 | .028 | .339 | |
| Higher Pay | .348 | .064 | .496 | 5.390 | .000 | .219 | .476 | |
| Job Security | .331 | .059 | .510 | 5.623 | .000 | .214 | .449 | |
| Privacy | .380 | .076 | .464 | 4.975 | .000 | .228 | .532 | |
| Working with Strangers | .349 | .076 | .434 | 4.572 | .000 | .197 | .500 | |
| a. Dependent Variable: Technology |
From the ANOVA table, the F ratio is 16.270, which is a positive and acceptable value. The hypothesis significance level was tested at less than 0.05. The result, Sig = 0.000, confirms that the model is highly significant. The analysis also shows that the t-statistic is statistically meaningful and clearly different from zero, indicating that the model estimates are statistically significant.
Figure 3. Results of the relationships between technology and its characteristics
In terms of the B coefficients, technology in business organizations is moderately associated with working with strangers (0.349), job security (0.331), privacy (0.380), and higher pay (0.348). It shows weaker associations with better working conditions (0.183), job enrichment (0.182), and high touch (0.021).
The mathematical form of the above model can be expressed as follows:
Y (technology) = 2.353 + 0.021 × high touch + 0.182 × job enrichment + 0.183 × better working conditions + 0.348 × higher pay + 0.331 × job security + 0.380 × privacy + 0.349 × working with strangers
Table 10. Analysis of the relationship between work experience and technology
| Variables Entered/Removedb | |||
| Model | Variables Entered | Variables Removed | Method |
| 1 | Technologya | . | Enter |
| a. All requested variables entered. | |||
| b. Dependent Variable: Work Experience |
| Model Summary | |||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | ||||
| R Square Change | F Change | df1 | df2 | Sig. F Change | |||||
| 1 | .429a | .184 | .175 | .67637 | .184 | 20.318 | 1 | 90 | .000 |
| a. Predictors: (Constant), Technology |
| ANOVAb | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 9.295 | 1 | 9.295 | 20.318 | .000a |
| Residual | 41.173 | 90 | .457 | |||
| Total | 50.467 | 91 | ||||
| a. Predictors: (Constant), Technology | ||||||
| b. Dependent Variable: work experience |
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 1.957 | .475 | 4.117 | .000 | |
| Technology | .483 | .107 | .429 | 4.507 | .000 | |
| a. Dependent Variable: work experience |
From the ANOVA table, the F ratio is 20.318, which is a positive and acceptable value. The hypothesis significance level was tested at less than 0.05. The result, Sig = 0.000, confirms that the model is highly significant. The regression analysis indicates that work experience is relatively strongly associated with technology (0.483), with t = 4.507 and sig = 0.000, showing statistical significance.
Figure 4. Strength of the relationship between technology and work experience
Table 11. Analysis of the relationship between work experience and technology
| Correlations | |||
| Work Experience | Technology | ||
| Work Experience | Pearson Correlation | 1 | .429** |
| Sig. (2-tailed) | .000 | ||
| N | 92 | 92 | |
| Technology | Pearson Correlation | .429** | 1 |
| Sig. (2-tailed) | .000 | ||
| N | 92 | 92 | |
| **. Correlation is significant at the 0.01 level (2-tailed). |
The results of the correlation analysis indicate that work experience has a moderately strong relationship with technology (0.429).
The findings show that work experience is positively related to technological characteristics such as better working conditions, job security, privacy, working with strangers, job enrichment, and higher pay. Therefore, work experience appears to be substantially associated with the business organization’s technology variables of better working conditions, job security, privacy, working with strangers, job enrichment, and higher pay.
The regression analysis shows that technology has a relatively strong effect on work experience, with beta = 0.483, F = 20.318, sig = 0.000, and t = 4.507, all statistically significant. From this, it can be concluded that business-organization technology does affect work experience.
The integrated results show that the relationships among work experience, technology, and its characteristics vary. Therefore, it is concluded that business leaders should carefully plan technology selection, development, and human resource work-experience issues based on a detailed analysis of the relationships among these factors, and should allocate job functions accordingly.
Figure 5. Integrated results of the relationship analysis among technology, its characteristics, and work experience
Recommendations for company operations
In recent years, increasing attention has been paid to how technology affects work experience. This study confirms that technology directly influences work experience. It may therefore be necessary to investigate other factors through which technology affects a company’s work experience in greater depth and detail.
The impact of technology should first be carefully observed to determine whether it improves or diminishes work experience. Overall, I believe that technology has substantially improved work experience.
Table 12. The impact of technology on work experience
| Impact on Work Experience | Impact on Work Outcomes | Manageability | |
| Higher Pay | + 6 | No effect | High |
| Job Enrichment | + 5 | No effect | High |
| Better Working Conditions | + 4 | Uncertain | High |
| High Touch | + 3 | Moderately positive | High |
| Less Travel | + 2 | Positive | Medium |
| Job Security | + 1 | Uncertain | Medium |
| Protection | 0 | Uncertain | Medium |
| Working with Strangers | - 1 | None | Low |
| Privacy | -1 | Negative | Low |
| Identity | -1 | Negative | Medium |
| Multiple Careers | -2 | Negative | Low |
| Stress and Choice | -2 | Uncertain | Medium |
| Dealing with Change | -3 | Slightly positive | Medium |
Conclusion and limitations
Impact of technology on work experience. It is clear that technology leads to improved productivity. In that case, the question arises as to how each change in work experience affects productivity. In general, changes in work experience brought about by technology have a limited impact on productivity; when the outcome is negative, the effect is negative as well. Finally, when considering the management of technology’s effects on work experience, each technological characteristic can be managed individually, and only a few require a high level of managerial control.
I believe that a deeper understanding of the impact of technology on work experience is needed. However, it may also be possible to learn directly from experience without requiring a special study or project. If we have strong networks and communication, we can manage technology-driven changes effectively, step by step, as they occur.
Do not lose sight of the relationship between technology and work experience. People do not change very much, but they adapt remarkably well. Do not try to change people when they are adapting to change and to a technology-based work environment. People adjust to technology in different ways, and a company’s adaptation approach cannot accommodate every employee in exactly the same way.
It is natural for social scientists and psychologists to pay some attention to issues related to job attitudes, job satisfaction, and the causes of dissatisfaction in connection with work outcomes. These individuals often have the time and motivation to support and publish their own “research.” In the past, their perspectives may have had an excessive influence on personnel thinking related to the impact of technology on work experience.
Human resource management personnel should continuously seek out and learn the necessary, accurate, and useful information and support regarding the effects of technology on work experience. I believe companies should approach technological issues carefully and listen closely to their employees. Although companies help people adapt to change, workplace evidence shows that employees cannot always adapt well without the assistance and involvement of their employers.
Elhanan Helpman and Antonio Rangel (1999) Adjusting to a New Technology: Experience and Training, Journal of Economic Growth Vol. 4, No. 4 (Dec., 1999), pp. 359-383 (25 pages)
Engelberger, J. F. (1980). Robotics in practice: management and applications of industrial robots. Springer Science & Business Media.
Griffiths, T., Benke, M., Guile, D., Herlau, H., Madsen T., Marhuenda, F., McKenna, P. (forthcoming) Work experience as a strategy for learning in Europe
Griffiths, T. and Marhuenda, F. (forthcoming) 'Interpretation of the relevance of work experience for future-oriented educational strategies as a challenge for research' in Qualifications, Competences and Learning Environments for the Future: European Reference Publication, Thessaloniki: CEDEFOP
Griffiths, T, Miller, A and Peffers, J, eds. (1992) European Work Experience: Principles and Practice, Centre for Education and Industry: University of Warwick,
Guile, D. (forthcoming) Work, Organisational Change and Work Experience: the question of employability within the European Labour Market
Guile, D. and Griffiths. T,. (2001) Learning through work experience
Katherine, S.Welfare, Matthew, R.Hallowell, Julie A.Shah, and Laurel D.Riek (2019) Consider the Human Work Experience when Integrating Robotics in the Workplace
Jeffrey K. Liker, Carol J. Haddad and Jennifer Karlin (1999) Perspectives on Technology and Work Organization, Annual Review of Sociology, Vol. 25 (1999), pp. 575-596 (22 pages)
Manulang. (2013). Definisi Pengalaman Kerja. 5–24.
Marwansyah. (2015). Pengalaman Kerja dan Lingkungan Kerja.
Miller, A. (1996) 'To boldly go ... work experience but not as we know it?' in Miller, A. and Forrest, G., eds. Work experience for the 21st. century, CEI: University of Warwick
Miller, A. and Forrest, G., (1996) eds. Work experience for the 21st. century, CEI: University of Warwick
Miller, A., Watts, A.G. and Jamieson, I. (eds.) (1991) Rethinking Work Experience, Brighton, Falmer Press
Mimelientesa Irman, Erwin Agus Suhendra, Haugesti Diana (2021) Work experience, professionalism, independence and the application of Information technology on auditor performance in order to increasing audit quality at the financial audit agency of the republic of indonesia representative of the riau province.
Oates, T. and Fettes, T. (1997) 'Work experience and key skills' in Forrest, G., (ed.) Work experience for the 21st. century: changing priorities, changing practice, CEI: University of Warwick
Peffers, J., Griffiths, T. and Romain, L. (1997) Health and Safety in European Work Experience: A Teacher's Guide, CEI: University of Warwick
Robert E. Sibson Strategic Planning for Human Resources Management. 1992
Qualifications and Curriculum Authority (1998) Learning from Work Experience. A guide to successful practice London: QCA
Watts, A.G. (1983) Work Experience and Schools, London, Heinnemann
Watts, A.G. (1991) 'The concept of work experience' in Miller, A., Watts, A.G. and Jamieson, I., eds. Rethinking Work Experience, Brighton: Falmer Press