INTRODUCTION
Rationale of this research
The ongoing pandemic has created significant challenges for organizations, reminiscent of historical traumas like The Black Death and World War II This crisis has not only led to distress but also fostered resilience and a sense of community (Suedfeld, 1997) From January to November 2020, 44,400 businesses shut down, resulting in 1.2 million job losses and 4.5 million workers facing reduced hours (www.gso.gov.vn; www.thoibaotaichinhvietnam.vn) Amidst this global crisis, continuous changes and challenges have severely impacted various activities.
In the manufacturing sector, companies often resort to job cuts and downsizing in response to crises, placing immense pressure on employees to meet tight deadlines and targets While motivational factors can be beneficial, they may not suffice in chaotic situations, except for eustress, a term defined by Hans Selye as a positive form of stress that aids in accomplishing challenging tasks Subsequent research has expanded on this concept, highlighting how eustress can foster creativity, enthusiasm, and motivation, ultimately driving individuals and organizations to overcome business challenges Studies have also focused on cultivating eustress in the workplace to elicit positive responses to unavoidable stress In the wake of the pandemic, manufacturers are increasingly recognizing the value of eustress in enhancing productivity and job performance, with evidence suggesting that it correlates with a favorable work environment and improved employee output, leading to reduced costs for manufacturers.
2013) Stress also has a significantly positive relationship with life satisfaction and hope (O'Sullivan, 2010)
Research highlights the positive impact of eustress on well-being and productivity Morgan (2018) emphasizes the need for further studies to explore methods for cultivating eustress and transitioning from distress Lazarus (1993) notes that eustress fosters positive emotions and enhances health, while Cavanaugh et al (2000) found that it can significantly boost productivity.
While many associate stress primarily with distress, as defined by Hans Selye, it's important to recognize that some stress, known as eustress, is beneficial for optimal performance (Muse et al., 2003) According to Yerkes and Dodson's law, eustress occurs when stress is managed at an ideal level Kalaimathi and Jessy (2014) highlight the detrimental effects of distress and suggest effective stress management techniques, including regular exercise, a healthy lifestyle, yoga, and time management Additionally, Gibbons et al (2007) provide insights into experiences that contribute to both distress and eustress, offering students strategies to effectively cope with stressors.
Research has primarily focused on identifying the factors that influence eustress, yet variations in research contexts can yield differing outcomes To strengthen the understanding of this relationship, it is essential to conduct further studies across diverse contexts, including geography and social sciences.
Research in Vietnam has primarily examined the impact of distress on organizational operations For instance, Tran Kim Dung et al (2015) found a negative correlation between a poor work environment and employee engagement Additionally, Vu Viet Hang and Phan Thi Cam Linh (2015) highlighted that emotional intelligence influences stress levels, with self-efficacy significantly reducing work-related distress and promoting eustress Furthermore, Nguyen Minh Ha and Nguyen Hoang Tien (2015) emphasized the substantial effect of the work environment on employee distress.
(the beta reaches 0.76, p-value at 1%), which also means that a good work environment has a positive relationship to eustress
The master's thesis titled "The Impact of Self-Efficacy and Work Environment on Job Performance through Eustress" aims to bridge the theoretical gap in eustress research by employing quantitative analysis to draw practical conclusions.
Research objectives
This research aims to settle the following three main objectives:
This study investigates the influence of self-efficacy and work environment factors on job performance, analyzing both direct and indirect effects through eustress, grounded in established hypotheses and prior research.
The second objective is to develop a research model that examines the relationship between eustress and job performance, while also assessing the extent to which eustress mediates this relationship.
The primary objective is to offer actionable managerial insights derived from research findings, aimed at guiding managers in effectively harnessing eustress to enhance both workplace performance and overall life satisfaction.
Research questions
Following the rationale of the research, the paper focuses on solving the following questions:
Self-efficacy and work environment factors significantly influence eustress, with strong effects on an individual's ability to manage stress positively High levels of self-efficacy enhance an employee's confidence, leading to improved job performance Additionally, a supportive work environment fosters motivation and satisfaction, further amplifying the positive impacts on productivity Together, these elements create a conducive atmosphere for optimal job performance and well-being.
- Question 3: What is the mediating role of eustress in explaining the indirect impact of work environment and self-efficacy factors on job performance?
- Question 4: What can be concluded from the research and how are recommendations and managerial implications made?
The subject and the scope of research
The objective of the research is to find the relationship between the self-efficacy factor and work environment factor with eustress and job performance
1.4.2.1 Content: This research concentrates on surveying how self-efficacy and work environment impact job performance through eustress
1.4.2.2 Scope: This research was conducted in Hochiminh City in the field of office work
1.4.2.3 Time: The secondary data (if any) was collected from 2017 to 2020, and the primary data was collected from employees who are working in Hochiminh City in
Methods
The research combines two methods: qualitative research and quantitative research
The desk study method is employed to synthesize primary and secondary sources for developing a survey questionnaire focused on eustress, self-efficacy, work environment, and job performance By gathering secondary data from reputable scientific journals, we can identify relevant studies that link these concepts This process enables us to select appropriate theoretical models and relationships to construct effective scales and survey instruments.
Given the varying research subjects and contextual factors like culture, customs, and economic development across countries, the preliminary scale derived from previous studies may not adequately fit the Vietnamese context Consequently, qualitative research is conducted to refine and enhance the draft scale, ensuring its relevance and suitability for this specific research in Vietnam.
Individual depth interviews (IDI) were conducted to adapt the original scale into a contextually relevant topic scale for surveying in Ho Chi Minh City The IDIs involved 10 participants, including 4 CEOs and 6 managers with experience in organizational behavior Each interview lasted between 1 to 2 hours, focusing on refining the draft scale to create an official scale that aligns with the research context.
The study primarily employs the method of interviewing workers and employees via a survey questionnaire The formal research process consists of two phases:
- Phase 1: Conduct a mass survey with more than 300 respondents based on survey questionnaires Data is processed by SPSS and PLS-SEM software according to descriptive statistical analysis steps
- Phase 2: Data processing and analysis following three steps:
Step 1: Examine the measurement model's reliability and validity
Step 2: Evaluate the structural model by looking at VIF, R 2 , f 2 , Q 2 , and q 2 (if applicable)
Step 3: Mediation Analysis by inspecting:
According to Nguyen Minh Ha and Vu Huu Thanh (2020), mediation analysis is done as follows:
+ Bootstrapping is used to determine the significance of direct effect, specific indirect effect, total indirect effect, and total effect
+ The VAF index is used to determine the magnitude of each mediation
+ If necessary, bootstrapping to compare mediating effects.
Study significance
First, the study tested the direct relationship between self-efficacy, work environment, and eustress After that, evaluate how those three factors affect job performance
This article explores the interconnections between self-efficacy, work environment, eustress, and job performance, aiming to clarify their relationships It emphasizes the importance of understanding how self-efficacy and a supportive work environment can enhance eustress and subsequently improve job performance Additionally, the study seeks to address existing research gaps in this area, providing practical insights for organizations looking to optimize employee performance and well-being.
The research highlights the role of self-efficacy and work environment factors in enhancing eustress, while also measuring and demonstrating the causal effects of these elements on job performance.
The study suggests strategies to enhance eustress during challenging production phases, offering valuable insights for researchers and strategic managers These findings help to identify key factors that contribute to eustress, ultimately improving organizational effectiveness.
The research findings highlight key positive factors that contribute to eustress, offering valuable insights for managers to enhance organizational behavior Additionally, these results serve as a foundation for innovative strategies aimed at maximizing effectiveness while optimizing limited organizational resources.
The thesis includes the following 5 chapters:
Chapter 1-Introduction to the Research Topic: stating the rationale for carrying out this research thesis, presenting the objectives, scopes, and objects of the study, summarizing the research method, and demonstrating practical significance
Chapter 2-Research model: Explain key concepts such as self-efficacy, work environment, and stress, as well as how they affect job performance In addition, in this chapter, the study points out the proposed research model through the argument of the hypotheses, which is the relationship of the factors in the model summarized from previous research
Chapter 3-Research Methodology: As mentioned in Chapter Two, presenting the methods used for data processing, results analysis, and correlations of the factors in the theoretical model is covered in Chapter Three
Chapter 4-Research results: Presenting the results which are obtained after data processing, result analysis, and hypothesis testing using statistical software such as the Statistical Package for the Social Sciences (SPSS) and Partial Least Square Structural Equation Modeling (PLS-SEM) Finally, drawing conclusions about the factors' correlations
Chapter 5-Conclusions and recommendations: The content of this chapter presents the conclusions and implications of governance in terms of science and practice, based on the results outlined in the previous chapter This final chapter also presents some limitations of the thesis, proposing the next research directions.
The research’s structure
This chapter aims to clarify key definitions and concepts related to eustress, self-efficacy, work environment, and job performance It reviews theoretical models from previous research and summarizes various studies that explore these concepts The chapter identifies correlations between these factors, emphasizing the significance of eustress in the context of modern life By examining scholarly papers, it highlights the essential role of eustress within the broader spectrum of stress Additionally, it discusses relevant events, conditions, and situations related to eustress, ultimately proposing a research model based on historical findings.
The term "eustress," coined by endocrinologist Hans Selye in 1956, refers to "good stress" or "positive stress," derived from the Greek prefix "eu-," meaning "good."
"Stress Without Distress", Hans Selye (1974) specifies the type of stress and argues that stress can create not only unpleasant results but also pleasure for people who experience stress
Eustress is a positive response to stress, characterized by viewing stressors as challenges rather than threats It is influenced by factors such as one's sense of control, the context of the stressor, its desirability, and timing Research indicates that eustress fosters coping mechanisms infused with hope, vigor, and a sense of purpose, leading to enhanced well-being and life satisfaction Furthermore, eustress is positively correlated with feelings of hope and overall satisfaction in life (Sullivan, 2010).
Eustress is combined with hope and active engagement (Hargrove et al.,
Eustress plays a crucial role in enhancing the body's energy levels and promoting physiological well-being by activating essential biological processes linked to physical recovery and immune function (Achor et al., 2013).
LITERATURE REVIEW AND RESEARCH MODEL
Concepts and definitions
The term "eustress," coined by endocrinologist Hans Selye in 1956, refers to "good stress" or "positive stress," derived from the Greek prefix "eu-," meaning "good."
"Stress Without Distress", Hans Selye (1974) specifies the type of stress and argues that stress can create not only unpleasant results but also pleasure for people who experience stress
Eustress is a positive form of stress that arises when individuals view stressors as challenges rather than threats It is influenced by factors such as feelings of control, the environment, the desirability of the stressor, and its timing Research indicates that eustress is associated with coping mechanisms characterized by hope, energy, and a sense of purpose Furthermore, eustress contributes to overall well-being and life satisfaction, fostering a beneficial relationship with hope and fulfillment (Sullivan, 2010).
Eustress is combined with hope and active engagement (Hargrove et al.,
Eustress plays a crucial role in enhancing the body's energy levels and promoting physiological well-being by activating essential biological processes tied to physical recovery and immune function (Achor et al., 2013).
Eustress plays a crucial role in today's fast-paced and challenging economic environment, promoting positive emotions, physical fitness, and mental resilience Defined as a beneficial form of stress, eustress is closely linked to health, well-being, hope, and life satisfaction, offering numerous advantages for both personal and professional growth In the wake of the coronavirus pandemic, many workplaces face heightened stress levels, making the recognition and cultivation of eustress essential for enhancing job performance and supporting employee health Emphasizing eustress can lead to significant managerial improvements, helping organizations achieve their goals while fostering a healthier work environment.
Self-efficacy, as defined by Bandura (1977), refers to individuals' belief in their ability to perform actions necessary for achieving outcomes This concept evolved to encompass beliefs about capabilities in reaching life goals (Bandura, 1989) and includes the motivation and cognitive strategies needed to meet job demands (Bandura, 1990) In organizational settings, self-efficacy reflects confidence in one's ability to fulfill tasks that contribute to achieving organizational objectives Luthans et al (2015) identify five key characteristics of self-efficacy: setting high personal goals, thriving in adversity, high motivation levels, persistent effort towards objectives, and resilience in overcoming obstacles Ultimately, individuals' actions and motivation are influenced more by their beliefs than by objective realities; a lack of belief in the effectiveness of their actions can diminish motivation Thus, self-efficacy serves as a crucial foundation for action, guiding individuals in their pursuit of desired goals (Nguyen Minh Ha and Ngo Thanh Trung, 2018).
Self-efficacy is a crucial intrinsic factor that empowers individuals within an organization to pursue challenging goals and strategic objectives, particularly during and after the pandemic This concept serves as a source of inspiration, enhancing an individual's capability to meet organizational goals and improve job performance.
The work environment theory, introduced by Kohun in 1992, encompasses both physical factors, such as equipment and machinery, and non-physical elements, including an organization’s history, culture, and behaviors This concept highlights the significance of physical, social, and psychological factors that shape working conditions, as noted by Jain and Kaur in 2014 Furthermore, the working environment is characterized by the relationships among individuals within the organization and the overall atmosphere present, as described by Kotter and Heskett.
1992) The work environment theory of Kohun (1992) also states that a change in the workplace leads to a change in the employee’s and organizational performance Raziq
Maulabakhsh (2015) emphasizes the significance of the work environment, which encompasses factors such as working time, safety, security, employee relationships, esteem needs, and managerial behavior The work environment is divided into two main areas: the nature of the work itself, including task performance and training, and the settings, which refer to both physical and social conditions (Skalli et al., 2008) Overall, the work environment is a crucial extrinsic factor that influences organizational activities and enhances job performance within organizations.
Job performance is crucial for organizational success, reflecting the extent to which goals are achieved (Al-Omani, 2017) It encompasses the behaviors and activities employees engage in to fulfill organizational objectives (Motowidlo et al., 1999; Aguinis, 2005) High job performance not only drives profitability but also enhances employee satisfaction as they meet their targets (Bevan, 2012; Muchhal, 2014) Conversely, poor job performance can lead to decreased productivity and effectiveness, negatively impacting the organization (Cooke, 2000; Okoyo & Ezejiofor, 2013) In today's workplace, job performance serves as a fundamental metric for evaluating operational success (Viswesvaran and Ones, 2000).
Job performance is the primary indicator of an organization's success, making it essential to identify the factors that influence it Understanding these contributing elements is crucial for enhancing overall organizational effectiveness.
Theoretical background and hypotheses
This paper synthesizes various sparse studies to provide a comprehensive understanding of literature reviews, focusing on the conceptual model developed from the underlying theories of eustress.
2.2.1 The Theory of the Yerkes-Dodson Law
The Yerkes–Dodson law (1908) establishes a crucial link between arousal or stress and performance, indicating that an optimal level of pressure can enhance performance through eustress (Adya, 2019) However, insufficient pressure may lead to boredom, while excessive pressure can result in burnout, negatively impacting overall performance (Le Fevre et al., 1986).
Figure 2 1: Yerkes and Dodson Law (1908)
The illustration demonstrates that job performance improves with increasing stress levels up to an optimal point, where performance peaks at a specific stress level Beyond this point, excessive stress leads to decreased performance This optimal stress level varies among individuals due to intrinsic factors and across tasks due to extrinsic factors Peifer et al (2014) developed the YDL model, based on the works of Csikszentmihalyi (1975) and Rheinberg (2008), highlighting that physiological pressure ranges from low levels of boredom to high levels of anxiety, ultimately suggesting that a moderate level of physiological pressure can lead to a high flow experience.
Figure 2 2: Physiological arousal during flow-experience between stress and relaxation
In 2018, the original figure of Yerkes & Dodson (1908) is developed and depicted by Martin (2018) which specify stages of pressure level
Figure 2 3: Illustration of stress stages of Yerkes and Dodson (1908), depicted by Martin
The diagram illustrates the optimal zone for achieving high performance under moderate pressure According to Martin's model (2018), it effectively outlines the stress stages that lead to peak performance.
Remarks: The demonstration of the YDL theory gives a clear viewpoint on stress, and at an appropriate level, stress is good for performance (Yerkes & Dodson, 1908) 2.2.2 The finding of eustress
Over the past four decades, extensive research has focused on the relationship between stressors and stress, primarily emphasizing the negative impacts of distress This focus has led to biased managerial recommendations aimed at reducing or eliminating stress, often overlooking the positive aspects of stress known as eustress Despite Hans Selye, the pioneer of stress research, highlighting the distinction between eustress and distress in the mid-1970s, the positive element of stress remains underexplored in managerial strategies.
Figure 2 4: Hans Selye’s concept of stress (depicted by Simmons, 2000)
Remarks: The finding of eustress by Hans Selye (1975, 1976a, 1976b) has a significant viewpoint for identifying eustress and distinguishing eustress from distress in the stress concept
2.2.3 The positive mindset of stress
The term "eustress," first introduced by Selye in 1975, has been extensively researched over the years Hargrove's 2013 study highlights the benefits of a positive mindset, aligning with the holistic stress model proposed by Simmons and Nelson in 2007, which focuses on the constructive aspects of stress while disregarding its negative impacts.
Figure 2 5: Model of Holistic Stress (Hargrove, 2013) by eliminated negative stress of
Hargrove (2013) introduced a novel approach to researching stress, encouraging future scholars to explore this area further However, Hargrove's model lacked quantitative analysis This paper builds upon his framework by focusing specifically on eustress, aiming to conduct quantitative analysis within a defined timeframe The goal is to uncover the positive aspects of stress, in contrast to previous studies that predominantly emphasized negative stress.
This study aims to address the research gaps in empirical surveys of eustress by quantitatively measuring its role as a mediator between self-efficacy and the work environment, and their collective impact on job performance By employing a straightforward research model, this innovative approach seeks to explore the positive effects of stress among workers in Ho Chi Minh City.
Reviewing previous studies and research hypothesis
2.3.1 Reviewing previous studies a) Research of Vu Viet Hang et al., (2016) about “The impact of emotional intelligence (EI) on accountants’ work stress in Hochiminh city (HCMC)”
The paper tests the impact of emotional intelligence, including self-efficacy, on work stress and includes a survey of 291 accountants in Hochiminh City
This study found a significant negative correlation of -0.455 (p < 0.01) between self-efficacy and distress, indicating that higher self-efficacy is associated with lower levels of distress While the research highlights this relationship, it does not explicitly address the impact of self-efficacy on eustress, suggesting that self-efficacy may positively influence eustress levels.
Enhancing self-efficacy is crucial for accountants as it helps reduce distress and promote eustress in the workplace To achieve this, managers should focus on strategies that boost employees' self-confidence and belief in their abilities, ultimately leading to a more positive and productive work environment.
The study by Trinh Thuy Anh et al (2017) examines the relationship between environment, attitude, and employee performance, focusing on Tan Son Nhat Airport It highlights how self-efficacy negatively affects distress, suggesting that a positive work environment and supportive attitudes can enhance employee performance.
The research identifies the relationship between work environment, attitude, behavior, and job performance of employees and a survey of 220 staff working at Tan Son Nhat Airport
The study highlights that the work environment, encompassing organizational culture, the dynamics between managers and staff, and the overall atmosphere, significantly influences job performance This impact occurs both directly and indirectly by shaping employee behavior.
This research highlights key managerial strategies for fostering a positive work environment that boosts job performance It underscores the importance of understanding the relationship between work environment and job performance, particularly within the contemporary context of employees in Ho Chi Minh City.
Figure 2 7: Model of work environment impact on job performance c) Research of Nguyen Minh Ha et al., (2018) about “Psychological Capital: Theory and Measurement”
The research examines psychological capital concepts, fundamental components, background theory, and associations of local and international related studies
The research involved synthesizing psychological capital scales from previous studies and conducting qualitative analyses through expert surveys to develop comprehensive scales These scales encompass key components, including self-efficacy, which are central to this study.
JobPerformance d) Research of Hargrove et al (2013) about “Generating Eustress by challenging employees: Helping people savor their work”
The article explores the concept of eustress, its historical significance in management research, and presents a research model aimed at helping supervisors create a healthy and positive organizational environment through the promotion of eustress or beneficial stress.
The research established a connection between eustress and enhanced job performance, highlighting its positive impact in the workplace Additionally, the study recommended various strategies for managers to enable employees to embrace eustress, ultimately fostering a more productive and optimistic work environment.
Figure 2 8: Model of Hargrove et al (2013) e) Research of Spector (1998) about “A control theory of the job stress process” p153-p168
The study elucidated the job stress process model, highlighting how the work environment and self-efficacy contribute to stress, which can manifest as either eustress or distress Although the author did not perform a quantitative analysis to measure the effects of these factors on stress types, the research provided a comprehensive overview of the job stress process Consequently, the author offered significant managerial recommendations focused on improving the work environment and boosting self-efficacy.
Positive affect Hope Vigor Meaningfulness Manageability
Health Well-Being Citizenship Behaviors Commitment Performance
(if in excess) deal with work stress, and this approach led to beneficial effects on job performance and organizational efficiency
Figure 2 9: Model of occupational stress of Spector (1998) {depicted by Le Fevre et al
(2003)} f) Research of Na-Nan (2019) about “Self-efficacy and employee job performance”
The study clarified the correlation between self-efficacy and employee job performance The study surveys 250 students from the Engineering Faculty in ThaiLand
This study also showed that self-efficacy influences and increases employee job performance through mediator factors such as perceived workplace support, motivation to transfer, and transfer of training
The findings highlight significant managerial implications, offering a nuanced understanding of how self-efficacy impacts job performance by enhancing perceived workplace support, motivation to transfer skills, and the overall transfer of training.
Figure 2 10: Model of Self-efficacy vs Job performance of Na-Nan (2019)
In today's world, stress is an inherent aspect of daily life and work, as emphasized by Hans Selye, who noted its unavoidable nature Acknowledging that stress is a constant presence, it becomes crucial to recognize its various levels and stages This understanding enables leaders to develop effective policies that enhance organizational behavior, promoting eustress rather than distress in the workplace.
A review of existing research has yielded valuable insights for developing a new research model, incorporating elements from five studies by Hargrove et al (2013), Spector (1998), Vu Viet Hang et al (2016), Trinh Thuy Anh et al (2017), and Na-Nan (2019) Based on this foundation, the author proposes the inclusion of two key factors—self-efficacy and work environment—alongside eustress and job performance factors in the research model.
Figure 2 11: Proposal of Research Model
Research hypothesis
a) Relations of self-efficacy and job performance
Self-efficacy is a crucial factor in enhancing motivation and performance by fostering a sense of control over one's circumstances (Bandura, 1986) It significantly impacts individuals' confidence and perceived competency in executing tasks, driving them to put forth their best effort to reach their goals (Bandura, 1997) This belief in the effectiveness of sustained effort leads to successful outcomes, as employees with high self-efficacy demonstrate greater capacity, persistence, and intensity in their roles, actively seeking out more challenging objectives (Bandura, 2006).
Meta-analysis reveals a strong positive correlation between self-efficacy and job performance, supported by significant research from Stajkovic & Luthans (1998), Miraglia et al (2017), De Clercq (2017), and Na-Nan.
Connecting all the relationships between the factors of self-efficacy and job performance as mentioned above, the study proposes the following hypothesis:
H1: Self-efficacy has a positive direct effect on job performance
H5 b) Relations of work environment and job performance
A positive work environment encompasses various factors, including workplace safety, job reassurance, strong coworker relationships, recognition for performance, motivation, and a clear role in the organization's execution process When top management emphasizes the importance of employees to the organization, it fosters commitment and a sense of ownership (Spector, 1997) The ecology theory of aging, as defined by Lawton and Nahemow (1973), highlights that individual competence and environmental features together determine optimal functioning Research by Khuong et al (2014) shows that employees who feel comfortable in their work environment are more effective and enjoy their jobs, suggesting that supervisors should enhance workplace conditions Ultimately, a supportive work environment significantly impacts organizational activities and can lead to improved job performance.
Connecting all the correlations between the factors of work environment and job performance as mentioned above, the research proposes the following hypothesis:
H2: Good work environment has a positive direct effect on job performance c) Relations of eustress and job performance
Eustress, defined by Hans Selye in 1976, plays a crucial role in enhancing employees' job performance, as highlighted by Nelson & Simmons in 2003 Job performance is multifaceted, requiring employees to engage in various positive behaviors to meet multiple organizational goals Eustress fosters excitement and flow, ultimately improving work efficiency and encouraging employees to perform at their best, as noted by Kundaragi & Kadakol in 2015 The term "eustress," derived from the Greek prefix "Eu," meaning "good" or "positive," signifies the motivational force that helps individuals tackle challenges and achieve their objectives more effectively By stimulating employees positively, eustress drives productivity and contributes to a more dynamic work environment.
Connecting all the correlations between eustress and job performance as mentioned above, the study proposes the following hypothesis:
H3: Eustress has a positive effect on job performance d) Relations of self-efficacy and eustress
Theoretical research indicates that self-efficacy plays a crucial role in eustress within occupational settings Defined as the belief in one's ability to handle challenging tasks, self-efficacy also regulates various health domains A high level of self-efficacy can lead to positive outcomes, including effective pain management and improved stress coping mechanisms (Luszczynska et al., 2005).
Research highlights the significant role of self-efficacy in stress management, indicating that it can alleviate negative stress, enhance job satisfaction, and improve physical health and commitment levels (Jex & Bliese, 1999) High self-efficacy is also linked to reduced depression, worry, and anxiety (Beas & Salanova, 2006) By effectively managing personal emotions, self-efficacy offers numerous advantages in addressing stress (Sebastian, 2013).
Self-efficacy refers to the ability to manage pressure and control both external stressors and internal emotions Employees with high self-efficacy can effectively regulate their feelings, thereby minimizing unnecessary workplace stress and approaching challenges with a thoughtful mindset (Vu Viet Hang et al., 2015) Typically, top-performing employees exhibit strong self-efficacy, characterized by their cheerfulness, enthusiasm, emotional intelligence, and adeptness at managing work-related stress and problem-solving (CM Tri et al.).
2017) Obviously, self-efficacy holds a vital role in reduce distress or increase eustress
Connecting all the relationships between the factors of self-efficacy, eustress, and job performance as mentioned above, the study proposes the following hypothesis:
H4: Self-efficacy has a positive indirect effect on job performance through eustress e) Relations of work environment and eustress
The work environment includes all kinds of physical and non-physical factors in an organization that influence people in the workplace (Kohun, 1992; Jain & Kaur,
A positive working environment fosters a favorable perception of an organization and its strategies, while a negative environment can lead to detrimental attitudes among employees Research by Nguyen Huu Thu (2009) highlights that stress acts as a significant factor in the interaction between the environment and individuals, influencing their ability to cope and adapt to changes Key physical elements of the work environment, such as lighting, noise, temperature, and air quality, play a crucial role in employee well-being When workers feel unsafe, it adversely affects their emotions, resulting in decreased satisfaction and commitment to their roles.
Research indicates that workplaces with windows or exposure to sunlight for just 15 minutes a day can significantly enhance employee satisfaction with lighting and improve focus The physical environment of a firm, including its design and layout, plays a crucial role in shaping employee behavior Additionally, psychological factors such as cultural, organizational, social, and mental influences are vital in determining the working conditions for employees When these positive psychological factors are present, employees experience greater well-being and are better equipped to manage work-related stress.
Connecting all the relationships between the factors of work environment, eustress and job performance as mentioned above, it is proposed the following hypothesis:
H5: Good work environment has a positive indirect effect on job performance through eustress
Chapter summary
Stress is often viewed negatively, leading researchers to focus on its causes and solutions for prevention and coping However, this perspective overlooks the potential benefits of stress, which can be crucial for productivity, especially during challenging times like the pandemic Acknowledging the positive aspects of stress can enhance our understanding and utilization of it, ultimately fostering productivity growth in various activities.
This study explores the role of eustress in enhancing job performance through quantitative analysis The findings highlight the importance of understanding eustress as a beneficial form of stress that can positively influence employee productivity Additionally, the research offers managerial insights and emphasizes the significance of eustress in fostering organizational growth and development.
METHODOLOGY 3.1 Research designs
Research methods
To validate the research methodology, additional processes will be undertaken in alignment with the study's objectives Subsequently, both qualitative and quantitative analyses will be performed to refine the research scales and data collection methods The research scales will be assessed based on findings from prior studies and adjustments made during the pilot study.
The qualitative research aims to adapt the original scale to align with the context of Hochiminh City, utilizing the individual-depth-interview (IDI) method due to social distancing constraints The study involves 10 participants, including 4 CEOs with over five years of experience and 6 managers with at least two years of experience Participants are interviewed to assess their perceptions of self-efficacy, work environment, eustress, and job performance, while also evaluating their understanding of the questions posed Insights and recommendations from the experts lead to adjustments in the research questions, enhancing the study's relevance and depth.
The interview questionnaires are structured in two key sections: the first outlines the study's objectives, while the second includes targeted questions aimed at eliciting responses from participants regarding self-efficacy, work environment, eustress, and job performance.
The research model outlined in Chapter Two undergoes further exploration and refinement through direct interviews with experts and participants This process allows for the adjustment of draft scales and models, ensuring their relevance to the research context and facilitating their application in quantitative analysis.
The draft scales for direct interviews are developed based on prior research outlined in Chapter Two, utilizing observed variables for self-efficacy from Sherer et al (1982), work environment variables from Amabile (1989), and eustress variables synthesized from studies by Branson et al (2019), Pindek & Spector (2016), Seeger et al (2019), and Rodríguez et al (2013) Additionally, observed variables for job performance are extracted from the research of Welbourne et al (1998).
The contents of the questionnaire include two parts:
Part one: Showing how the synthesis-selection-adjustment with 26 observed variables was consulted by IDI
In the second part of the study, interviews were conducted with ten respondents, including four CEOs or directors and six experienced managers from various fields They were asked to provide additional insights and refine the wording and meaning of the observed variables relevant to the research context Utilizing the original scales and feedback from the focus group discussions, the study revised and finalized the draft scales, which were then incorporated into a survey questionnaire to gather primary data.
As a result, the total of 63 items in the initial questionnaires is reduced to 26 items due to multiplicity and duplication The final pilot interview results show that basically
26 scales have been understood in the same sense These scales show a relatively complete and accurate representation of research objectives
This research investigates the influence of self-efficacy and work environment on job performance, both directly and indirectly through eustress Focusing on the diverse workforce in Hochiminh City, the study targets employees currently working in the area, making the sample representative Utilizing a questionnaire, the study aims to gather data from at least 300 respondents, adhering to the guidelines set by Tabachnick and Fidell (1996) Due to time constraints, a non-probability convenience sampling method is employed The findings from this quantitative analysis will lead to conclusions and managerial implications, enabling managers to enhance employee self-efficacy and improve the work environment, ultimately fostering eustress and boosting job performance.
The questionnaire is formed for this research using scales that are adjusted from the original scales The questionnaire includes three parts, as follows:
Part 1: The Introduction and Screening Questions for Respondents: The introduction outlines the purposes of the survey and invites the respondents to participate The questions are filtered to ensure that the survey is performed on the right respondent who is characterized by past or current eustress
Part 2: Questions about the demographics of respondents, such as gender, age, occupation, etc
Part 3: The main questions: Questions to elicit feedback from customers on their experience, self-efficacy, emotions, perceptions and behavior toward the work environment, and job performance
The scales utilized in this research model are derived from existing studies and are grounded in the theoretical frameworks of self-efficacy, work environment, eustress, and job performance To measure the variables within the structural model, a five-point Likert scale is employed, requiring respondents to evaluate each multiple-choice question by indicating their level of agreement, ranging from "strongly disagree" (1) to "strongly agree" (5).
To enhance the relevance of observed variables within the research context, it is essential to adjust and complete the scale in the model This process includes the incorporation of a self-efficacy scale among other observed variables.
The self-efficacy variable, denoted as SE, comprises seven observed variables (SE01 to SE07) derived from the scales developed by Sherer et al (1982) and Chen et al (2001) for undergraduates, with adjustments made based on focus group interviews This research focuses on evaluating self-efficacy experiences in the workplace, highlighting its significance as a key factor contributing to eustress, as shared by experts, managers, and employees during group interviews The survey findings facilitated the refinement of the original scale, leading to the identification of seven observed variables tailored for this study.
Code Original scale Modified scale Authors
SE01 Failure just makes me try harder Failure just makes me try harder Sherer et al., (1982) SE02 I am a self-reliant person I am a self-reliant person ditto
SE03 I give up easily I do not give up easily ditto
SE04 When facing difficult tasks, I am certain that I will accomplish them.
When facing difficult tasks, I am certain that I will accomplish them.
SE05 I believe I can succeed at most any endeavor to which I set my mind.
For me, there is nothing called impossible. ditto
SE06 I am confident that I can perform effectively on many different tasks.
I am confident that I can perform effectively on any task. ditto
SE07 Even when things are tough, I can perform quite well
I always think positively ditto b.) Work environment scale
The work environment, referred to as WE, has been extensively studied, with significant contributions from researchers like McGuire and McLaren (2009), Badayai (2012), and Raziq (2015) Notably, the research conducted by Amabile also adds valuable insights into this important variable.
In 1989, Amabile introduced a comprehensive scale encompassing both physical and non-physical environments relevant to research contexts Following a pilot study, this research has adapted Amabile's scale to better align with its objectives The study identifies five key observation variables related to the work environment.
Code Original scale Modified scale Authors
WE01 I felt that I am working on important projects
Every job I do is considered important
WE02 The task in my work are challenging
All the task in my work are mostly challenging ditto
WE03 There is an open atmosphere in this organization
The facilities are good that satisfies my work ditto
WE04 New ideas are encouraged in this organization
New ideas are encouraged in this organization ditto
WE05 I feel that top management is enthusiastic about my project
Top management is supportive ditto c.) Eustress scale
The eustress variable, denoted as EU, comprises eight observed indicators ranging from EU01 to EU08, derived from the studies of Branson et al (2019), Pindek & Spector (2016), Seeger et al (2019), and Rodríguez et al (2013) The pilot survey results, obtained after conducting a focus group interview, facilitated the careful selection, rejection, and adjustment of the original scale to align with the research objectives The proposed observed variables for measuring eustress are as follows:
Code Original scale Modified scale Authors
EU01 I felt motivated I felt motivated to the pressure of work
EU02 I felt the outcome was worth the effort
I felt the outcome was worth the effort ditto
EU03 I can usually overcome situational constraints
EU04 Felt that stress improved your health and vitality?
I felt that stress was positive that improved my health and vitality
EU05 Felt that being under pressure made you think more clearly and focused?
I felt that being under pressure made me think more clearly and focused ditto
EU06 Felt that a stressful situation had a positive impact on you?
I felt that a stressful situation had a positive impact on me ditto
EU07 Felt that stress enhanced your performance and productivity?
I felt that work stress enhanced my performance and productivity ditto
EU08 Take my work home I am willing to take my work home or work overtime to meet the deadline
Job performance, referred to as JP, is a variable extensively studied, with various scales developed to measure it Notably, research by Welbourne et al (1998) and Na-nan et al (2018) identifies three key factors influencing job performance: quality, quantity, and timing of work In this study, the author utilizes IDI and pilot research to refine and adapt the scales proposed by these researchers, ultimately establishing six observation variables to effectively measure job performance.
Code Original scale Modified scale Authors
JP01 Finding improved ways to do things
I always find improved ways to do things
JP02 Responding to the needs of others in his/her work group
I always respond to the needs of others in my work group ditto
JP03 Quantity of work output I always fulfill quantity of work assignment ditto
JP04 Tasks are performed attentively and correctly
I always perform tasks attentively and correctly
JP05 Tasks are completed as per the specifications and standards
I always complete tasks as per the specifications and standards ditto
JP06 Tasks are normally completed on schedule
I always complete tasks on schedule ditto
Determining sample size
Sampling methods can be categorized into two main types: probabilistic and non-probabilistic sampling Due to the constraints imposed by research conditions and the adverse pandemic situation in Ho Chi Minh City, this study employs non-probabilistic sampling, commonly referred to as "convenient sampling."
There are various proposals for sample size determination Some researchers propose a minimum sample size of 100 - 200 observations (Hair et al., 1979; Gorsuch,
1983) Some researchers state that sample sizes of less than 50 are terrible; – less than
A sample size of 100 is deemed inadequate, while around 200 responses is acceptable, 300 is good, 500 is great, and over 1000 is excellent (Comfrey & Lee, 1992) Roscoe (1975) suggests that a sample size between 30 and 500 is sufficient for making logical conclusions Additionally, Haque et al (2016) state that surveys with more than 200 responses are effective for drawing valid conclusions.
In scientific research, many researchers utilize complex methods to determine sample size, incorporating parameters like population (P), prior judgment values (p̂’), and margin of error (ME) to manage confidence levels and sample sizes (LeBlanc, 2004; Rumsey, 2002; Jamal, 2013) However, this paper focuses on social science research, which can effectively employ simpler scientific methods as suggested by Hair et al.
According to research from 2018, determining the minimum sample size can be effectively achieved using a 5:1 ratio, with a higher ratio yielding better results Additionally, Tabachnick and Fidell (1996) recommend that for a viable analysis, the sample size should exceed 300 participants.
This social science research utilizes straightforward methods for calculating sample size, revealing that the required sample is relatively small due to the limited observation variables Following the guidance of Tabachnick and Fidell (1996), the study aims for a minimum of 300 observations to ensure robust results, despite the smaller sample size suggested by Hair et al (2018).
Data collecting
Data collection will utilize a convenient sampling method due to time and resource constraints The participants will consist of individuals employed in Ho Chi Minh City Survey questionnaires will be distributed directly to each respondent, who will complete them using Google Forms or similar online survey platforms.
Data analysis
As show on the path model, this research uses SmartPLS software to analyse the partial mediation structural model, there would be following three main steps of analysis:
• Step 1: Measurement model assessment: by inspecting Reliability and Validity
To analyze the measurement model in PLS-SEM, it is essential to inspect internal consistency reliability, which reflects the homogeneity of indicators on a scale (Price et al., 2015; Devillis, 2016) PLS-SEM employs both Cronbach Alpha (α) and Composite Reliability (CR) to evaluate this measurement model effectively.
Cronbach (1951) built the α index to assess the reliability of indicators The formulation of α index is as follows: α = 𝐾
K : number of indicators α 𝑌𝑖 2 : variance of the concrete indicator Yi α 𝑋 2 : total variance of all the indicators
Acceptance range of α α ≥ 0.9 : very good 0.9 > α ≥ 0.8 : good 0.8 > α ≥ 0.7 : acceptable 0.7 > α ≥ 0.6 : not sure
According to DeVellis (2016), if the α value is too high (α ≥ 0.95), there could be multiple indicators with the same meaning, and the indicator should be removed
Although the α coefficient is widely used, it also has certain limitations Sijtma
The α coefficient is deemed unreliable for measuring internal consistency due to two main issues: first, its value is influenced by the number of indicators, often leading to inflated results when fewer indicators are used, which can mislead analysts into removing indicators to artificially boost the α value, a practice criticized by Dunn et al (2014) Second, the α coefficient assumes uniform reliability across all indicators, while the PLS-SEM method prioritizes indicators with higher reliability (Hair et al., 2016) Consequently, it is recommended that analysts utilize the composite reliability (CR) index as a more accurate alternative to the α coefficient when employing the PLS-SEM model (Nguyen Minh).
Ha and Vu Huu Thanh, 2020) The equation of CR is as follows:
CR : composit reliability λ 𝑗 : standardized outer loading of indicator j in measurement model σ 𝑗 2 : variance of measurement error of indicator j : σ 𝑗 2 = 1 − λ 𝑗 2
Acceptance range of CR 0.9 < CR : unexpected 0.7 < CR ≤ 0.9 : good
0.6 < CR ≤ 0.7 : acceptable if use for exploratory research 0.6 > CR : unacceptable
According to Hair Jr et al., (2016) suggestion, during the assessment of CR, the analyst needs to consider indicator reliability based on the outer loading λi, Hair et al
In 2016, it was recommended that factor loadings (λi) should be at least 0.708 However, analysts often face indicators with outer loadings below 0.7, and they may choose to retain or discard these weak indicators, as noted by Hulland in 1999 Additionally, Hair Jr et al (2016) advised that analysts should not hastily eliminate indicators with factor loadings between 0.4 and 0.7.
Indicators should be removed from the measurement model if their outer loading λi is less than 0.4, provided that this action does not compromise the content validity of the model (Bagozzi et al., 1991).
After inspecting and removing the weak indicators, and confirming that the CR meets the condition of internal consistency reliability, the analyst suggested assessing the convergent validity and discriminant validity
++ Inspecting the convergence validity through the average variance extract (AVE), this index accounts for the average explanatory level of the latent variable to the indicator
AVE : average variance extract λ 𝑖 : standardized outer loading of indicator (i) in measurement model
M : number of indicator in a variable
0.5 ≤ AVE : it can conclude the set of indicators meet the convergent validity
++ Inspecting the discriminant validity: based on the correlation coefficients of heterotrait-heteromethod (HTMT), Henseler et al (2015) proposed the equation of HTMT to assess the discriminant validity as follows:
HTMT 𝑖𝑗 : stand for the discriminant validity between the indicator set i and j
𝐶𝑜𝑟̅̅̅̅̅̅ 𝑖𝑗 : average correlation coefficients of heterotrait-heteromethod
𝐶𝑜𝑟̅̅̅̅̅ 𝑖 : average correlation coefficients of heterotrait-heteromethod i
𝐶𝑜𝑟̅̅̅̅̅ 𝑗 : average correlation coefficients of heterotrait-heteromethod j
0.9 ≤ HTMT : it’s hard to meet the discriminant validity It means the indicator set i may similar to the indicator set j
HTMT ≤ 0.85: it meets the discriminant validity between indicator set i and j
This step, the analysts examining the following indexes:
The Variance Inflation Factor (VIF) is utilized to assess high multicollinearity levels among indicators, as outlined by Hair et al (2019).
VIF ≥ 5 : the variable indicator has a problem of multicollinearity, the model can encounter serious problem
5 > VIF ≥ 3 : the variable indicator may have a problem of multicollinearity VIF < 3 : the variable indicator has no multicollinearity matter
++ R 2 index: R 2 shows a level of the input coefficient which explain for the change of the dependent variables
According to Henseler et al (2009) the standards to evaluate R square as follows:
The f² index, also known as the effect size index, as defined by Cohen (1988), indicates that the exclusion of exogenous variables from a model may lead to a higher level of unexplained variance among the remaining exogenous variables, although it does not affect the change in the dependent variable.
Chin (1988) proposes an equation to compute the value of f 2 index as follows: f 𝑖 2 = R 𝑖𝑛𝑐𝑙𝑢𝑑𝑒𝑑
2 −R 𝑒𝑥𝑙𝑢𝑑𝑒𝑑 2 1−R 𝑖𝑛𝑙𝑢𝑑𝑒𝑑 2 f 𝑖 2 : effective coefficient of input variance i
R 𝑖𝑛𝑐𝑙𝑢𝑑𝑒𝑑 2 : R 2 value of model when the exogenous included in the model
R 𝑒𝑥𝑐𝑙𝑢𝑑𝑒𝑑 2 : R 2 value of model when the exogenous excluded in the model
Cohen (1988) suggests the standards to evaluate the explanatory level of independent variable to dependent variable (f 2 ) as follows: f 2 ≥ 0.35 : High level
The Q² index, as defined by Stone and Geisser, serves as a key evaluation criterion for the cross-validated predictive relevance of PLS path models, according to Tenenhaus et al This index is instrumental in assessing the overall quality of the structural model, specifically focusing on the dependent variable.
The bootstrapping method is a non-parametric technique utilized to assess the statistical significance of various PLS-SEM results, including path coefficients, Cronbach’s alpha, HTMT, and R² values, as outlined in the SmartPLS 3.3 manual This procedure involves generating numerous sub-samples by randomly selecting observations from the original dataset These sub-samples are then employed to estimate the path model within the SmartPLS software, with the process being repeated until approximately 5000 random sub-samples are generated.
++ to ascertain the significance of the direct effect, specific indirect effect, total indirect effect, and total effect
++ to determine the strength of each mediation This process is to compute the variance accounted for value (VAF) to discover the contribution of the mediator.
Chapter summary
This chapter outlines the initial research process, methodologies, and findings related to the development of scales for formal research It details the formal quantitative research methods used to assess these scales, evaluate the model, and establish the necessary conditions for data analysis The forthcoming chapter will provide insights into the research outcomes, including the characterization of research samples, scale testing results, and the findings from hypothesis testing.
This chapter outlines the outcomes of scale testing, model testing, and hypothesis testing within the research framework Utilizing SPSS 25 for data cleansing and descriptive statistics, the research also employs Partial Least Square Structural Equation Modeling (PLS-SEM) with Smart PLS 3.0 software to evaluate and validate the research model.
Descriptive analysis
In total collected 369 observations, there are 16 observations with missing value which were rejected The remaining 353 observations meet qualification for data analysing b.) Samples descriptive analysis
35 - 44 years old 120 34% over 44 years old 40 11%
(Source: a computing results from SPSS software)
The survey results indicate a nearly equal gender distribution, with males representing 52% of the population (182 individuals) and females making up 48% (171 respondents) Additionally, the findings reveal a diverse range of age groups, particularly highlighting participants aged 18 years and older.
44 years old accounts for 89% of the total The highest rates belong to the age groups
In the age groups of 25-34 and 35-44, respondents represent 38% and 34% of the total, respectively Among those surveyed, 54% are college students, totaling 192 individuals Additionally, 23% of respondents are graduates, comprising 80 individuals, while postgraduates account for 8%.
28 respondents c.) Quantitative variable descriptive analysis
Table 4 2: Statistical analysis of observe variabes
(Source: an extraction from SmartPLS software)
The majority of indicators exceed the expected value on the 5-level Likert scale, indicating that most respondents align with the statements related to the four main variables Additionally, the low standard deviation of the observations suggests a concentrated distribution of data around the mean value.
Measurement Model Assessment
The Smart PLS software is used to analyze data and evaluate the measurement model After the first run of the PLS Algorithm, by applying the following parameters
The path weighting scheme, as outlined by SmartPLS GmbH (2021), yields the highest R² values for endogenous latent variables and is suitable for various path model specifications and estimations.
- For the maximum iterations and stop criterion: applying the value of 300 iterations at 10^-7^ stop criterion After running, we have the initial path model as shown in appendix No.6
Based on the partial mediation construct model, the research needs to apply the following assessment analyses as follows:
Table 4 3: Outer Loadings of measurement model indicators
(Source: Outer Loading indexes of all indicators after Algorithm 1 st run on SmartPLS)
The analysis of the outer loading reveals that the job performance latent variable exhibits outer loading indexes predominantly exceeding 0.708, with all remaining indicators maintaining a factor loading above 0.4 This indicates that the majority of the scale indicators satisfy the reliability criteria.
The analysis indicates that the factor loadings for EU01, EU02, EU08, SE01, SE03, SE05, and SE07 range between 0.4 and 0.708 According to Hair et al (2016), indicators within this range should not be automatically discarded However, since removing certain indicators does not compromise content validity and can enhance composite reliability, this study opted to eliminate SE03 and EU08, which exhibited the lowest factor loadings Following this adjustment, the PLS algorithm was rerun with the same initial parameters, resulting in an updated path model.
Figure 4 1: Path Model after Algorithm 2nt run – an extraction from Smart PLS 3.3.3
(Source: Composit Reliability indexes extracted from SmartPLS)
According to the table of the above composite reliability indexes, most index values are greater than 0.7, which means that the internal consistency reliability of those factors is acceptable
4.2.3 Inspecting Convergent Validity through AVE index
Table 4 5: Average Variance Extracted indexes
Variable name Average Variance Extracted (AVE)
(Source: Average Variance Extracted indexes from SmartPLS)
The average variance extracted (AVE) indexes for most factors exceed 0.5, indicating that they satisfy the criteria for convergent validity as established by Hair et al (2009).
4.2.4 Checking Discriminant Validity by examing Cross Loading
(Source: Cross Loading Analysis extracted from SmartPLS)
According to Hair et al (2011), for a valid cross-loading test, the outer loading of an observable variable on its corresponding latent variable should be at least 0.1 higher than its loading on other latent variables The results indicate that most observable variables exhibit adequate discriminant validity, except for EU02 and EU03, which do not meet this criterion Consequently, EU02 and EU03 are excluded, and the PLS Algorithm is rerun to ensure compliance with Hair et al (2011)’s standards for cross-loading.
Table 4 7: Adjusted Cross Loading Analysis
(Source: Adjusted Cross Loading extracted from SmartPLS)
4.2.5 Inspecting Heterotrait-Monotrait Ratio (HTMT)
Table 4 8: Heterotrait-Monotrait Ratio Indexes
(Source: HTMT indexes extracted from SmartPLS)
According to Henseler et al (2015), the above HTMT of all pairs of scales are less than 0.85 which means the discriminant validity of all pairs is supported
After that, the bootstrapping is run with parameters: Sub-samples = 5000;
Amount of results = complete bootstrapping
Figure 4 2: Path Model after Bootstrapping 1st run – an extraction from Smart PLS 3.3.3
Construct model analysis
(Source: Inner VIF indexes extracted from SmartPLS)
The inner VIF values for all variable indicators are below 3, indicating that the reflective measurement model is unlikely to face multicollinearity issues, suggesting that multicollinearity may not be present in the structural model either.
Table 4 10: R Square and Adjusted R Square indexes
Variable name R Square R Square Adjusted
(Source: R 2 and adjusted R 2 indexes extracted from SmartPLS)
Henseler et al (2009) found that the R square value for job performance is 0.522, indicating a medium effect and suggesting that factors beyond self-efficacy and the work environment also influence job performance Similarly, eustress has an R square value of 36.7%, which is considered acceptable, especially given that the research has yet to explore additional unlisted factors that may impact eustress (Hair et al., 2019).
Table 4 11: The Effect Size index
(Source: Effect size indexes (f 2 ) extracted from SmartPLS)
Cohen (1988) indicates that the effect size (f²) for most impacts on job performance and emotional state is generally minor to moderate This suggests that eliminating exogenous variables from the model is unlikely to enhance the explanatory power of the remaining exogenous variables regarding changes in the dependent variable Notably, among the two key factors influencing job performance, self-efficacy emerges as the most significant contributor.
Utilizing the blindfolding method in SmartPLS software, we computed Stone-Geisser's Q² value, a key evaluation criterion for assessing the cross-validated predictive relevance of the PLS path model, yielding significant results.
(Source: Q square indexes extracted from SmartPLS)
The Q² index, as outlined by Tenenhaus et al (2005), evaluates the overall quality of a structural model A structural model is considered to meet global quality standards if all endogenous latent variables exhibit a Q² value greater than 0.
According to Hair et al (2019), all of the latent variables have a 0.25 < Q 2 < 0.5, which also means the predictive power is at a medium level.
Statistical significance assessment
(Source: Path coefficients extracted from SmartPLS)
The direct effect of self-efficacy on job performance is 0.335 with a P value < 0.01 The regression coefficient has statistical significance, meaning that self-efficacy has a positive direct effect on job performance
The effect of Eustress on job performance is 0.174 with a P value < 0.01 The regression coefficient has statistical significance, which means that the eustress has a positive effect on job performance
The work environment significantly influences job performance, with a direct effect of 0.327 and a P value of less than 0.01, indicating a positive correlation The statistical significance of the regression coefficient underscores the importance of a conducive work environment in enhancing employee performance.
Mediation analysis
To determine the contribution of the mediator eustress, we calculate the variance accounted for (VAF) value Following the bootstrapping process, we obtained results for both indirect effects and total effects.
(Source: Total indirect effects extracted from SmartPLS)
Self-Efficacy -> Eustress -> Job Performance 0.055 0.002 Work Environment -> Eustress -> Job
(Source: Specific indirect effects extracted from SmartPLS)
(Source: Total effects extracted from SmartPLS) a) For the indirect effect of self-efficacy on job performance through eustress:
Total effect of SE -> JP = 0.39
Total indirect effect of SE -> JP = 0.055
Variance accounted for value of original sample of SE->JP
The findings indicate that the indirect effect of self-efficacy on job performance through eustress is statistically significant, with a VAF index of 0.141, suggesting a positive relationship This implies that eustress serves as an intermediary factor that explains the weak average level of self-efficacy's indirect effect on job performance Additionally, the analysis extends to the indirect effect of the work environment on job performance through eustress, highlighting its importance in the overall performance framework.
Total effect of WE ->JP = 0.388
Total indirect effect of WE -> JP = 0.061
Variance accounted for value of original sample of WE->JP
The analysis reveals that the indirect effect of the work environment on job performance through eustress is statistically significant, as the 95% confidence interval (from 2.5% to 97.5%) does not include zero The Variance Accounted For (VAF) index is calculated at 0.1572, indicating a positive indirect effect of the work environment on job performance via eustress This suggests that eustress serves as a crucial intermediary in elucidating the work environment's influence on job performance, averaging a VAF of 7%.
Discussion
This research aims to explore the relationships between self-efficacy, work environment, eustress, and job performance among employees in Hochiminh City It specifically examines how self-efficacy and work environment directly influence job performance, while also considering the mediating role of eustress in these dynamics Despite previous studies on self-efficacy and work environment, there is a notable lack of research on the structural model of eustress in this context The study utilizes the SmartPLS quantitative tool for measurement, with the hypothesis that self-efficacy positively impacts job performance.
The analysis reveals a direct effect of self-efficacy on job performance, with a coefficient value of 0.335, which is statistically significant (P < 0.01) This supports hypothesis H1, indicating that higher self-efficacy positively influences job performance These findings align with Bandura's theories from 1986, 1997, and 2006, and corroborate previous research by Stajkovic & Luthans (1998), Miraglia et al (2017), De Clercq (2017), and Na-Nan (2019), all demonstrating a significant correlation between self-efficacy and enhanced job performance.
Leaders in Hochiminh City should focus on enhancing their employees' self-efficacy, as it significantly influences job performance Research indicates that self-efficacy accounts for 39% of the variation in job performance, highlighting its critical role Additionally, the work environment positively impacts job performance, supporting the hypothesis that a conducive work setting enhances employee effectiveness.
The analysis reveals a direct effect of the work environment on job performance, with a coefficient value of 0.327 and statistical significance (P < 0.01), supporting hypothesis H2 This positive coefficient indicates that a good work environment enhances job performance, aligning with findings from Spector (1997) and Lawton and Nahemow (1973) Furthermore, this study corroborates the results of Khuong et al (2014), demonstrating that employees who feel comfortable in their work environment are more likely to perform effectively and enjoy their jobs compared to those who do not.
To enhance job performance in Ho Chi Minh City, managers must prioritize the improvement of the work environment, which significantly influences employee productivity, accounting for 38.8% of performance variation Additionally, the hypothesis H3 suggests that eustress positively impacts job performance, highlighting the importance of fostering a supportive and motivating workplace atmosphere.
The analysis reveals a coefficient value of 0.174 for the direct effect of eustress on job performance, with a statistical significance indicated by a P value of less than 0.01 This supports the acceptance of hypothesis H3, confirming that eustress positively influences job performance These findings align with Hans Selye's (1976) eustress theory and are consistent with Nelson & Simmons (2003) Additionally, this study reaffirms the conclusions of Kundaragi & Kadakol (2015), highlighting that eustress energizes employees and enhances their job performance.
Managers in Ho Chi Minh City can enhance their contributions to eustress, which plays a crucial role in motivating individuals to achieve goals and navigate challenges effectively Eustress positively influences employee productivity and job performance, fostering a sense of hope, well-being, and life satisfaction Additionally, the hypothesis H4 suggests that self-efficacy indirectly boosts job performance through the facilitation of eustress.
The VAF analysis indicates a self-efficacy VAF index of 0.141, demonstrating a statistically significant positive effect on job performance, thereby supporting hypothesis H4 This suggests that self-efficacy positively influences eustress, which in turn enhances job performance These findings align with previous research by Luszczynska et al (2005), Jex & Bliese (1999), and Sebastian (2013), highlighting self-efficacy as a means of managing emotions beneficially in stress contexts Additionally, this study reaffirms Beas & Salanova's (2006) findings that high self-efficacy can mitigate depression, anxiety, and worry while promoting eustress.
Organizations in Ho Chi Minh City should prioritize opportunities for employees to enhance their competencies, particularly self-efficacy Research by Vu Viet Hang et al (2015) and CM Tri et al highlights that when employees possess strong self-efficacy, they are better equipped to manage their emotions, thereby reducing unnecessary stress.
Research from 2017 highlights that employees with high self-efficacy exhibit positive traits such as cheerfulness, enthusiasm, and emotional intelligence, enabling them to manage work-related stress effectively and solve problems efficiently This indicates that self-efficacy is crucial for enhancing eustress, which in turn boosts job performance Therefore, we hypothesize that the work environment positively influences job performance indirectly through the facilitation of eustress.
The VAF analysis indicates that the work environment has an indirect effect on job performance, with a VAF index of 0.1572, which is statistically significant This supports and accepts hypothesis H5.
A work environment that fosters eustress has a positive correlation, with a value of +0.1572, indicating its beneficial impact on employee well-being This effect extends to job performance, suggesting that a supportive work atmosphere indirectly enhances performance through the promotion of eustress These findings align with Kohun's hypothesis regarding the relationship between work environment and job efficiency.
This study, conducted in 1992, reaffirms previous findings by Navia and Veitch (2003), McGuire and McLaren (2009), Jain & Kaur (2014), and Nguyen Huu Thu (2009), highlighting that a positive environment significantly influences individuals' thinking.
Organizations in Hochiminh City must prioritize the enhancement of both physical and psychological working conditions to foster eustress among employees This includes improving cultural factors, organizational behaviors, social dynamics, and mental well-being, all aimed at alleviating distress and promoting a positive work environment.
This chapter highlights the crucial role of eustress as a predictor of job performance, revealing significant correlations between eustress, job performance, work environment, and self-efficacy Respondents with higher eustress levels tend to exhibit enhanced job performance, with eustress accounting for 17.4% of the variance in job performance, showing statistical significance Despite existing research on eustress's impact on job performance, particularly in academic settings, there remains a lack of comprehensive studies Many investigations focus solely on the negative aspects of stress, often overlooking the positive influence of eustress These findings contribute significantly to understanding job performance, particularly regarding eustress's mediating role in the relationship between self-efficacy, work environment, and job performance.
Chapter summary
This chapter presents the findings from the assessment of the measurement model, evaluation of the construct model, and mediation analysis The results demonstrate that most indicators and latent variables are both reliable and valid, confirming that the proposed research model aligns with the collected data Additionally, all four hypotheses put forth in the study have been accepted The following chapter will provide a summary of the study, discuss managerial implications, identify limitations, and suggest avenues for future research.