The positive and negative impacts of technology overload on gig workers’ task performance via challenge stressors and hindrancestressors the case of gig workers in hcmc

According to IS research, technology overload is typically linked to several negative workplace outcomes, including increased stress, burnout, interruptions at work, decreased productivi

INTRODUCTION

Research objective

A study conducted in Ho Chi Minh City aimed to assess the effects of technology overload on gig workers' performance, focusing on both the positive and negative aspects through the lens of challenge and hindrance technostressors The research seeks to provide a deeper understanding of how technology influences gig workers' productivity, ultimately leading to actionable recommendations for optimizing their performance and outcomes.

Determining how technology overload can affect the task performances of gig workers in Ho Chi Minh city, through the technostressors;

Measuring the level of influence that technology overload has on how gig workers in Ho Chi Minh city perform through techonostressors;

This article explores how technology overload impacts gig workers, examining both the positive and negative relationships between factors such as burnout, creativity, and the challenges posed by technological stressors It aims to assess the extent of these effects on gig workers' overall well-being and productivity.

Based on these objectives, this research investigates how creativity and burnout status of gig workers in Ho Chi Minh city change corresponding to each type of techonostressors.

Research questions

In which way can the technology overload influence how the gig workers in Ho Chi Minh City perform through types of tcchnostrcssors, creativity, and burnout status?

To what extent does technology overload influence how gig workers in Ho Chi Minh City perform through types of technostressors, creativity, and burnout status?

What management implications are proposed for administrators to balance and maximize the performances of gig workers ?

Subject and scope of research

Research subjective: The effects of technological overload, both good and bad on gig workers' ability to complete tasks in Ho Chi Minh City through challenge and hindrance technologies.

Survey participants: Gig workers in Ho Chi Minh City of various ranges of age, primarily from 18 to 27.

The research was carried out in Ho Chi Minh City over a five-week period from January 1 to February 4, 2024, during which data was collected at different times throughout the day on both weekdays and weekends.

Research method

This thesis employs both qualitative and quantitative methods, utilizing online platforms such as Zalo, Instagram, and Messenger for data collection The research is structured into two primary stages.

In qualitative research, it is essential to conduct relevant prior studies at both local and international levels, filter data from real-world sources, construct a measuring scale, and prepare a preliminary survey questionnaire, which is then translated into Vietnamese.

A questionnaire is utilized to gather data for formal quantitative research, featuring closed-ended questions designed on a 5-point Likert scale, with 1 indicating strong disagreement and 5 signifying strong agreement.

The data that has been collected will all be processed using SPSS 20 and Excel 2020

It entails assessing measuring scales, checking assumptions and assessing the structural model.

Some contributions in terms of theory and practice derived from the results of this research arc summarized as follows:

This study enhances the understanding of technostress and its impact on gig workers, particularly in Ho Chi Minh City It expands existing theoretical frameworks by investigating the effects of technology overload on task performance, focusing on the mediating roles of challenge and hindrance technostressors Ultimately, the research aims to deepen academic insights into how technological demands affect gig workers' performance in this unique context.

The study highlights the significant impact of technology overload on gig workers in Ho Chi Minh City and the companies that engage them By analyzing the pros and cons of technological stress on task performance, it offers valuable insights for independent contractors on managing technostress Additionally, the research provides actionable recommendations for companies to develop strategies that mitigate the negative effects of technology overload, specifically by addressing technostressors that create challenges Ultimately, the study aims to bridge the gap between theory and practice, enhancing the performance and well-being of gig workers amidst technological pressures.

This chapter emphasizes the importance of the topic, outlining the general objectives and specific goals of the research It identifies the research subjects and defines the scope, while also detailing the methods employed Additionally, the chapter highlights the theoretical and practical significance of the study and provides an overview of the research structure.

Chapter 2: Literature review and hypothesis development

This section explores the theoretical basis linking technology overload to the job performance of gig workers It evaluates existing hypotheses and prior research models to create a solid groundwork for argumentation and the advancement of the research model.

This chapter outlines the research process, qualitative and quantitative research methods used to analyze and measure research concepts, and the construction of measurement scales.

Chapter 4: Data analysis and results:

This part presents the research findings, information about the survey sample, model validation, and measurement of research concepts It analyzes and evaluates the obtained results.

This final section provides a summary of the research outcomes, highlighting the contributions and limitations of the study in a practical context It also suggests directions for further research.

CHAPTER 1 - SUMMARY: Overall, the writers have provided a summary of the main ideas of the study subject in chapter 1 In particular, the background and reasons have been spelled out in detail, along with the general and specific aims, methodologies, and practical implications of the research article Most importantly, this chapter has a clear and concise explanation of the research paper's direction and primary issues.

CHAPTER 02: LITERATURE REVIEW AND HYPOTHESIS

Cognitive Load Theory, established by Sweller, Ayres, and Kalyuga in 2011, is an instructional framework based on human cognition that has evolved since the 1980s This theory leverages aspects of cognitive architecture to create both experimental and instructional outcomes, which are evident when comparing innovative teaching methods to traditional ones in randomized controlled trials When a new instructional approach leads to improved learning outcomes, as shown by test scores, it highlights a significant effect grounded in cognitive science Consequently, these innovative instructional strategies offer valuable options for educators and instructional designers seeking to enhance learning experiences.

Cognitive Load Theory highlights the importance of human cognition in instructional design, yet many approaches overlook cognitive architecture Instructional design often neglects the relevance of human cognition, treating it as an afterthought By grounding instructional design in established cognitive structures like working memory and long-term memory, educators can create more effective learning experiences Understanding these cognitive characteristics can lead to testable hypotheses that may yield innovative instructional methods when supported by experimental evidence.

Cognitive Load Theory effectively utilizes insights into the connections between working memory and long-term memory to develop instructional strategies that may initially appear counterintuitive Furthermore, a diverse range of instructional methods, often perceived as unrelated, can be understood as interconnected, all grounded in a shared theoretical framework based on human cognitive architecture.

The theory of task-technology fit (TTF) explores the relationship between digital technology and the tasks it aims to support TTF is a variability theory that examines the interaction among technology functionality, task requirements, and individual abilities at a given time Originating from information systems research, TTF has been applied in various fields, including knowledge work, managerial decision-making, team performance, virtual teams, and education.

The concept of task-technology fit was formulated by Goodhue and Thompson

Goodhue and Thompson (1995) highlight the importance of aligning technology, tasks, and individual capabilities, while Zigurs and Buckland (1998) focus on the interaction between tasks and technology to improve group performance They propose the Task-Technology Fit (TTF) framework as a means to evaluate how technology usage impacts performance outcomes The TTF perspective suggests that users can assess the fit between technology and tasks during usage, which can predict their performance TTF is measured by examining user experiences across various dimensions, including data quality, usability, and technology reliability, with the assumption that users evaluate both the functionality of the technology and its relevance to task completion and their personal skills (Dishaw, 1999).

The Technology-Task-Fit (TTF) theory posits that a strong alignment between technology, task requirements, and individual capabilities significantly boosts performance and efficiency in task completion (Goodhue, 1995) When users recognize that technology enhances their task execution, it leads to increased adoption and usage, ultimately resulting in improved performance This optimal task-technology fit occurs when technology aligns with the specific characteristics of the task and matches users' individual skills, facilitating a more seamless execution of tasks (Lee, Cheng, & Cheng, 2007; Spies, Grobbelaar, & Botha, 2020).

Definitions

The traditional model of full-time employment has been challenged by the growing labor force, advances in digital technology, and recent economic downturns, making it harder for job seekers to find permanent positions As a result, many individuals are turning to contractual work as independent workers, commonly known as freelancers or gig workers Gig labor is often defined by its reliance on technology, which facilitates these flexible job opportunities.

A "platform" serves as a bridge between the supply and demand for services, as highlighted by Spreitzer, Lindsey, and Lyndon (2017) Expanding on this idea, researchers like Gleim et al (2019) include direct sellers, such as those on Etsy, in the gig economy According to Dubai (2017), to be classified as a gig worker, an individual must provide goods or services on demand while being unemployed or lacking benefits.

The World Bank's report "Without Borders" estimates that the global population of online gig workers ranges from 154 million to 435 million, representing about 4.4% to 12.5% of the total workforce As a rapidly growing segment of the non-standard workforce, nearly one in four workers has engaged in gig work, highlighting its significance in today's dynamic economy, according to the McKinsey Global Institute.

Gig labor has rapidly gained prominence in organizational studies, moving from fourth and tenth place in previous years to third on the Society for Industrial and Organizational Psychology's (SIOP) Top 10 Workplace Trends in 2020 In 2021, gig work was recognized as the top workplace trend, highlighting the increasing significance of remote and flexible work arrangements in today's workforce.

More individuals are opting for gig work to enhance their income, as it provides multiple revenue streams rather than depending on a single job with a fixed salary The appeal of gig work extends beyond financial benefits; it also offers flexibility and autonomy, enabling workers to have better control over their schedules.

Gig workers experience a wide range of emotions, including joy, stress, and anxiety, largely due to the unique characteristics of gig work Factors such as uncertainty, frequent travel, and financial instability contribute significantly to these feelings.

In 2019, intense competition among gig workers led many to work excessively long hours, compromising their flexibility in the quest for higher earnings As a result, growing concerns have surfaced about the negative effects of gig work on workers' overall well-being.

Gig work offers individuals the potential for a better work-life balance, which can enhance overall well-being However, gig workers face significant time constraints and workloads, as their earnings depend on the number of tasks completed (Christie & Ward, 2019) Alarmingly, during the Movement Control Order (MCO), two-thirds of the 2,576 road fatalities involved motorcyclists (Abdullah, 2021) Additionally, the benefits and working conditions for gig workers are often inferior to those of permanent employees, with low-paid self-employed workers in the UK receiving limited incentives like housing benefits and working tax credits (Hutton, 2016) In contrast, high-earning self-employed individuals struggle to obtain financial references for renting, mortgages, or loans Therefore, it is crucial to prioritize the well-being of gig workers in the evolving gig economy.

Technostress, a term coined by Craig Brod in 1984, refers to the stress experienced from the use of information technology Recent research highlights the negative aspects of technology, emphasizing how it can contribute to increased stress levels among users.

Research has identified five key techno-stressors—techno complexity, techno-uncertainty, techno-insecurity, overload, and techno-invasion—that significantly affect workplace outcomes and employee strain These factors have become a focal point for Information Systems (IS) researchers, primarily due to the negative effects of information technology on worker behaviors and attitudes in the workplace Understanding these techno-stressors is essential for addressing the challenges posed by technostress and improving employee well-being.

The rise of digital work environments has introduced both benefits and health risks for workers Research indicates that IT professionals and remote workers are particularly vulnerable to technostress, which can lead to negative consequences such as lower job satisfaction, decreased productivity, increased workload, and conflicts between work and home life While teleworkers often appreciate the flexibility and autonomy their roles provide, they may also face challenges like social isolation and heightened technostress.

Prolonged exposure to technology can lead to a plethora of adverse impacts, including distractibility, reduced concentration, and impaired learning abilities (C Bruno,

M Canina, 2019) Furthermore, the failure to regulate online time can contribute to long term chronic stress and burnout (M Griffiths, 2000) Research within the realm ofInformation Systems (IS) on job burnout emphasizes the reliance of gig work on general organizational behavior theory and aims to develop concise models applicable to various occupations and social contexts, such as online platforms Most IS studies exploring burnout adopt a global approach, aiming to evaluate common stressors, symptoms, and consequences of burnout in the context of information technology (S.p Shih, J.J Jiang, G Klein, E Wang, 2013).

Research in Information Systems (IS) has consistently shown that technology overload is associated with negative workplace outcomes such as increased stress, burnout, work interruptions, reduced productivity, and impaired creativity (S Addas, A Pinsonneault, 2015; Chandra et al., 2019) Studies by Mahapatra and Pati, as well as Yu et al (2018), have confirmed a positive correlation between technology overload and burnout This issue is particularly significant for online platform workers, who experience constant access to communication and information systems, resulting in a heightened perception of additional workload and a pervasive sense of technology-induced overload (K.J Hanis).

A Lambert, R.B Harris, 2013) Additionally, excessive technology use has been linked to negative psychological implications, such as exhaustion, as well as behavioral consequences, including discontinuance of information system usage (X Cao, J Sun, 2018).

Techno-overload negatively impacts work productivity and leads to work-life imbalance, along with various health issues such as addiction, anxiety, and sleep disorders (L.D Rosen et al., 2012) This phenomenon is intensified in digital work environments, where platform-mediated work fosters a 24-hour economy across different time zones (J Berg et al., 2018) The continuous accessibility of these platforms extends working hours, contributing to heightened feelings of overload (S.R Barley et al.).

2011) and exacerbating stress and cognitive burnout (L.K Barber, A.M Santuzzi, 2015).

Increased technological demands, coupled with gig workers' deep engagement with these tools, may lead to cognitive overload and burnout Consequently, these effects are anticipated to negatively impact gig workers' task performance.

Prior relevant studies

(I) Algorithmic control and gig workers: a legitimacy perspective of Uber drivers (Martin Wiener, w Alec Cram and Alexander Benlian, 2023)

Organizations are increasingly adopting algorithmic control (AC) for automated managerial oversight, especially in the gig economy where independent workers complete specific tasks or "gigs." This reliance on AC raises important questions about how gig workers perceive the legitimacy of such practices Understanding these perceptions is vital, as they significantly influence key worker behaviors, including turnover and non-compliance.

This study explores the intricate dynamics of legitimacy in gig work through a three-dimensional framework focusing on autonomy, fairness, and privacy By analyzing survey data from 621 Uber drivers, the research highlights how workers' perceptions of algorithmic control (AC)—specifically gatekeeping and guiding—affect their legitimacy judgments and behavioral responses Contrary to prior assumptions, the findings reveal that guiding AC positively influences micro-level legitimacy judgments, suggesting that workers do not uniformly perceive AC as harmful The results illuminate the complex relationship between AC, worker perceptions, and behaviors, offering valuable theoretical insights and practical implications for organizations in the evolving gig economy.

Figure 2.1 Research model of Martin Wiener, w Alec Cram and Alexander

(2) Absorbed in technology but digitally overloaded: Interplay effects on gig workers' burnout and creativity (Aldijana Bunjak, Matej Cerne, Ales Popovic, 2021)

The integration of technology has transformed platform-mediated work, encouraging individuals to engage with various digital tools However, platform workers face challenges related to digital overload, characterized by an overwhelming influx of information and demands, which can negatively impact their well-being and job performance, particularly increasing burnout levels This research focuses on understanding the interplay between cognitive absorption in technology and technology overload, aiming to clarify their combined effects on the creative output of gig workers By analyzing empirical data from 263 Amazon Mechanical Turk workers, the study seeks to enhance the understanding of the complex relationships between technology, cognitive engagement, and creativity in the gig economy.

Recent findings indicate a significant positive correlation between cognitive absorption in technology and the creative output of gig workers This suggests that greater cognitive engagement with technology enhances creativity However, the research employs a sophisticated moderated-mediation analysis, revealing a more complex relationship Specifically, when gig workers experience high levels of technology overload, the initially positive link between cognitive absorption and creativity becomes more intricate and nuanced.

In the context of technology overload, cognitive absorption acts as a double-edged sword, positively influencing creative output while also leading to increased burnout among workers This interplay highlights the complex dynamics of platform-mediated work, revealing how elevated technology overload can diminish creativity Understanding these factors is crucial for organizations aiming to enhance the creative potential of gig workers By recognizing the balance between cognitive absorption and technology overload, companies can implement strategies to reduce burnout and foster an environment that supports sustained creativity, ultimately improving performance and well-being in the evolving landscape of platform work.

Figure 2.2 Research model of Aldijana Bunjak, Matej Cerne, Ales Popovic (2021)

(3) Psychological Well-being ofGig Workers: A Preliminary Study (Siew Woon Lee, Yeh Ying Cheah, Chew Sze Cheah, Sook Fern Yeo, 2023)

This research aims to investigate the factors affecting the mental health of gig workers in Malaysia Data was collected through an online survey utilizing a snowball sampling method, involving thirty respondents The findings indicate that inadequate pay is the most significant factor adversely impacting the psychological well-being of gig workers, followed closely by limited social support from friends and parents.

This study has notable limitations, particularly due to its small sample size, highlighting the need for future research to include a larger and more diverse group of gig workers Such an extension is essential for enhancing the robustness and generalizability of the findings The research offers valuable insights into the factors affecting the psychological well-being of gig workers, which can inform the Malaysian government and other organizations in addressing the psychological challenges faced by this workforce.

This study offers valuable insights into the various factors affecting the psychological well-being of gig workers in Malaysia By identifying these variables, it enhances our understanding of the gig economy and lays the groundwork for future research and initiatives aimed at improving the overall welfare of gig workers.

Figure 2.3 Research model of Siew Woon Lee, Yeh Ying Cheah, Chew Sze Cheah and Sook Fern Yeo (2023)

(4) Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior Completed Research Paper (W Alec Cram, Martin Wiener, Monideepa Tarafdar, Monideepa Tarafdar, 2020)

This study examines the impact of algorithmic restrictions on worker behavior and well-being within gig economy platforms, focusing on the relationship between different types of algorithmic control and technostress experienced by gig workers By analyzing data from a survey of 621 Uber drivers in the US, the research reveals that algorithmic behavior and output controls are positively associated with challenge technostressors, while algorithmic input controls relate to hindrance technostressors The findings highlight how these algorithmic control modes act as job demands that induce stress, linking the fields of information systems control and technostress The study underscores the potential negative consequences of increased stress, such as reduced work engagement and a higher likelihood of utilizing workarounds, which could adversely affect both individual workers and the gig economy as a whole.

Figure 2.4 Research model of w Alec Cram, Martin Wiener, Monideepa

(5) Effect of Gig Workers ’ Psychological Contract Fulfillment on Their Task

Performance in a Sharing Economy—A Perspective from the Mediation of

Organizational iden tification and the Moderation of Length ofService (Wenlong Liu,

Changqing He, Yi Jiang, Rongrong Ji and Xuesong Zhai, 2020)

In the sharing economy, gig employment can lead to worker isolation, negatively affecting company perception and job performance This study examines how psychological contract fulfillment influences gig workers' performance, focusing on organizational identification and service duration Analyzing data from 223 Didi drivers in China, the findings reveal that both transactional and relational psychological contract fulfillment directly impacts job performance, with organizational identification playing a mediating role Notably, transactional contract fulfillment significantly enhances organizational identification for workers employed less than a year, while long-serving drivers are more influenced by relational contract fulfillment These insights have important implications for effectively managing gig work within the sharing economy.

Figure 2.5 Research model of Wenlong Liu, Changqing He, Yi Jiang, Rongrong Ji and Xuesong Zhai (2020)

Research hypothesis

2.4.1 The relationship between technology overload and gig workers’ challenge technostressors.

Technology presents both challenges and opportunities that drive workers to enhance their skills and effectiveness (Tarafdar et al., 2019) These challenges motivate employees to exceed their limits, achieving performance levels beyond their independent capabilities A crucial aspect of this process is the perception that managerial demands can aid in task accomplishment and personal growth, framing these demands as motivational factors When organizational objectives align with personal goals, workers feel inspired to excel for their employer while also recognizing opportunities for personal advancement (Hargrove, Nelson, & Cooper, 2013) Additionally, technology overload relates to challenge technostressors, providing real-time insights into performance and motivating staff when necessary Research indicates a positive link between technology connectedness and flexibility (Leung, 2011), with flexible technology use correlating with increased job satisfaction and reduced work-life conflicts (Diaz, Chiaburu, Zimmerman).

Technology overload, characterized by automated verifications of registration and insurance, can positively impact gig workers by imposing minimum-level barriers that must be met for work engagement This binary nature allows workers to strive for improvement, enhancing their skills and performance Rather than being a hindrance, technology overload can act as a catalyst for skill development and adaptability, fostering a dynamic environment that encourages rapid change and competence Ultimately, when viewed as a transformative mechanism, technology overload can lead to significant skill enhancement and improved performance for workers.

Hl: Technolog)' overload is positively related to a worker's challenge technostressors.

2.4.2 The relationship between technology overload and gig workers’ hindrance technostressors.

Technology overload is closely linked to technostressors that hinder productivity Historically, the integration of technology in workplaces has been associated with increased negative stress levels Research shows that in environments with strict technological control, employees face greater task inflexibility, resulting from constant connectivity demands that reduce their sense of autonomy This leads workers to feel that technology compromises their privacy, particularly gig workers who perceive the pressure to conform to company standards as intrusive Furthermore, the ongoing pressure to maintain customer ratings and acceptance rates exacerbates technostress through feelings of techno-overload and techno-invasion, highlighting the significant impact of technology reliance on worker well-being.

Rapid technological changes can lead to increased stress among workers, particularly in online platform organizations that exhibit characteristics of hindrance technostressors Research shows that highly centralized and innovative companies, often found in the gig economy, contribute to higher levels of technostress Additionally, technology that hinders the formation of workplace relationships further exacerbates this stress In the gig economy, where algorithmic control often leads to independent work, workers face limited opportunities for communication and interaction, highlighting the need to address these stressors effectively.

H2: Technolog)' overload is positively related to a worker's hindrance technostressors.

2.4.3 The relationship between hindrance technostressors and burn out

Online platform companies that utilize algorithmic restrictions may contribute to technostress, particularly in creative and centralized environments, as highlighted by Wang et al (2008) Gig economy platforms like Uber exemplify this issue, where technology-induced stress can hinder employees' ability to build relationships with colleagues (Stich et al., 2019) Califf et al (2020) establish a link between negative psychological responses and various technostressors, including overload and insecurity High job expectations compel workers to exert more effort, leading to psychological and physical consequences such as fatigue and irritation (Bakker et al., 2001) While workers may attempt to recover through rest or less demanding tasks, insufficient recovery can lead to chronic engagement and eventual burnout (Taris et al., 2014) Furthermore, excessive technology use can exacerbate overload, negatively impacting mental health, particularly among gig workers who experience cognitive absorption, ultimately resulting in cognitive overload and burnout (Cao & Sun, 2018).

H3: Hindrance technostressors are positively related to burn out

2.4.4 The relationship between challenge technostressors and creavity

Challenge technostressors are expected to align with the positive intentions of continuing online operations, as previous research highlights a link between fulfilling jobs and increased job satisfaction alongside constructive work challenges (Crawford et al., 2010; Ramu-Nathan et al., 2008) Similarly, Califf et al (2020) found that challenging technostressors, such as perceived usefulness, lead to favorable psychological responses, enhancing competition and personal motivation Tarafdar et al (2019) define challenge technostressors as technology-related challenges that individuals feel motivated to address, believing that overcoming these challenges is achievable and beneficial Furthermore, Califf et al noted that these stressors are associated with higher task completion rates.

In a study conducted in 2020, employees at a technology company faced the challenge of adapting to a new programming framework, which ultimately spurred the research team to innovate and enhance the product's value Tarafdar et al (2019) suggest that challenge techno-stressors, or stressors from technology viewed as challenges, can lead to positive outcomes such as increased effectiveness and creativity Thus, these challenge technostressors play a crucial role in fostering creativity and enhancing individual capabilities.

H4: Challenge technostressors are positively related to creativity

2.4.5 The relationship between burnout and creativity.

(C Cochis, E Mattarelli, F Bertolotti, A.c Scapolan, F Montanari, p Ungureanu,

Burnout has been shown to adversely affect employee creativity by impairing the ability to think critically and synthesize information from various sources, ultimately reducing gig workers' engagement in the creative process Existing literature indicates a potential negative correlation between stressors and creativity, highlighting the detrimental effects of burnout on innovative capabilities (K Byron, Khazanchi, Nazarian, 2010; Sosik, Kahai, Avolio, 1999).

H5: Burnout is negatively related to a worker’s creativity.

2.4.6 The relationship between burnout and task performance.

Burnout is a prolonged response to job-related stress, characterized by feelings of fatigue, loss of enthusiasm, decreased motivation, inattention, and apathy, which can significantly disrupt an individual's behavior Studies show that those suffering from anxiety, fatigue, and a lack of control over their work are more likely to make mistakes and perform at lower levels.

2005) Thus, the primary hypothesis posited is as follows:

H6: There exists a negative association between burnout and task performance.

2.4.7 The relationship between creativity and task performance

In an era marked by increasing automation and globalization, businesses face heightened unpredictability and competition, necessitating a strong focus on innovation from senior management (Akhtar and Sushil, 2018; Slater et al., 2014) Employee creativity plays a pivotal role in enhancing organizational performance, as creative workers can devise novel solutions to workplace challenges, ultimately leading to superior outcomes (George and Zhou, 2002; Amabile, 1996) By empowering employees to engage in creative thinking, companies can boost productivity and job performance, as these workers effectively combat fatigue and burnout (Eschleman et al., 2014) Creativity, characterized as an act of discovery, fosters intrinsic motivation and encourages exploration, further driving innovation and performance (Csikszentmihalyi, 1997; Ryan and Deci, 2000; Amabile, 1988; Gilson et al., 2005) Moreover, gig workers benefit from access to diverse knowledge and resources through technology, enabling them to overcome creative blocks and develop practical solutions (Bruno et al., 2019; Kelley et al., 2013; Sun et al., 2015).

H7: Creavity are positively related to Task performance

Research model

Figure 2.6 Research framework and hypotheses (by authors)

CHAPTER 2 - SUMMARY: Chapter 2 introduces the concepts and theories of objects related to gig workers’ task performance and technology overload In addition, the authors have explored related models and theories as well as the prior studies that can support for the proposed hypotheses Based on these theories and research, theauthors have presented a research model of the positive and negative impacts of technolo gy overload on gig workers' task performance via challenge technoslressors and hindrance technostressors: the case of gig workers in Ho Chi Minh city.

RESEARCH METHOD

Research process

The current thesis employs a mixed methodology that integrates both quantitative and qualitative approaches to enhance the validity and coherence of the questionnaire To establish a measurement scale for constructs such as technology overload, challenge technostressors, hindrance technostressors, creativity, burnout, and task performance, a comprehensive literature review was conducted Subsequently, the measurement scale was adapted to align with the specific context of the research.

All items in these constructions were translated into Vietnamese, the official language of the study setting A questionnaire was developed based on the measurement scale and underwent revisions to ensure clarity before its finalization.

In the second phase of the research, gig workers in Ho Chi Minh City were interviewed using paper-based methods, and the collected data was analyzed using SPSS The research process is visually outlined in Figure 3.1.

Measurement scale

To ensure the reliability of the measurement tool, the scales utilized in this study were adapted from prior research, aligning with the study's objectives The independent variable, Technology Overload, is informed by the findings of Bunjak, Cerne, and Popovic (2021), focusing on the challenges faced by gig workers Additionally, technostressors—both challenge and hindrance types—are assessed through four questions each, as outlined by Cram et al (2020) The negative variable of Burnout is also derived from Bunjak, Cerne, and Popovic (2021), featuring four questions that gauge the exhaustion experienced by gig workers due to technology overload Lastly, the Creativity variable is analyzed using insights from Coelho and Augusto (2010) and Moon et al (2019).

I have a higher workload because of increased technology complexity.

Aldijana Bunjak, Matej Cerne, Ales Popovic

1 am forced by technology to work with very tight time schedules.

I am forced to change my work habits to adapt to new technologies.

TO4 I am forced by technology to do more work than I can handle.

The technology challenges me to enhance my skills as a gig-worker. w Alec Cram et al (2020)

The technology challenges me to perform my tasks better.

The technology challenges me to improve my performance ratings.

Overall, the technology challenges me in a positive way.

HTl do not know enough about the new technology to handle my job satisfactorily. w Alec Cram et al (2020)

[ need a long time to understand and use new technologies.

The new technology forces me to work much faster.

I have a higher workload because of the complexity of the new technology.

I try to be as creative as I can in my ob Moon, T w„ Hur, w.

I experiment with new approaches in performing my job.

My boss praises that I am creative in performing my job.

On the job, I am inventive in overcoming barriers.

I feel emotionally exhausted because of my work with new technology.

Aldijana Bunjak, Matej Cerne, Ales Popovic

[ feel exhausted at the end of a working day with new technology.

I feel tired when I get up in the morning and have the next working day with new technology.

1 feel worn out because of my work on the platforms.

New technology helps me fulfill all Ihe responsibilities specified in my task description

New technology helps me pay attention to detail and avoid making mistakes.

TP3 look for improved ways to accomplish the assigned work with new technology

My performance is better than when 1 did not use new technology

Questionnaire design

The paper-based questionnaire was structured into three sections: the first section explained technology overload, gig workers' task performance, and included screening questions for eligible respondents The second section measured the research constructs, while the final section gathered demographic information from participants To ensure clarity and effectiveness, the questionnaire was pre-tested with a diverse group of gig workers in Ho Chi Minh City, primarily focusing on younger individuals aged 18 and above, before being finalized and distributed.

Qualitative Research Methods

A preliminary research outline has been developed, as detailed in Appendix 01, aligning with the stated research objectives This outline encompasses questions and statements from the research model designed to evaluate participant feedback regarding the relevance of the content, timing, and location The interview process consists of two key components: first, gaining a general understanding of gig workers and the factors influencing their task performance; and second, assessing and refining the assertions of the research model scale.

Qualitative research reveals that many gig workers are facing technological overload, which could hinder their creativity and increase the risk of burnout Most participants expressed a desire to enhance their skills to keep pace with ongoing technological advancements.

Most participants agree with the proposed measurement scale for the variables; however, some suggest that the questionnaire should be refined for clarity and that certain scientific terms need definitions to ensure accurate responses Consequently, the questionnaire will be revised to enhance its clarity, simplicity, and precision before distribution to survey participants.

The quantitative research approach utilized a survey questionnaire for data collection, which was refined based on insights from a qualitative study The reliability and quality of the findings hinge on the principles of quantitative research, which allows for precise measurement and quantification of data through numerical values Key procedures in this methodology include designing a sample, collecting data via surveys, analyzing descriptive data, and performing regression analysis.

The convenient sampling method allows researchers to select easily accessible survey subjects, optimizing time and cost; however, it lacks a defined sampling error due to the absence of a specific sampling list While there is no universally accepted sample size, Hair et al (2006) recommend a minimum of five times the number of observed variables for Exploratory Factor Analysis (EFA), suggesting at least 120 participants for this study with 24 observed variables Nguyen Dinh Tho (2011) further advises a ratio of 5:1 for observation to measurement variables, ideally 10:1, necessitating a minimum of five observations per measurement variable Consequently, the estimated minimum sample size ranges from 120 to 240 observations To ensure robust data for analysis, this study collected around 290 survey responses to accommodate potential deficiencies and ensure a comprehensive representation of respondents' views.

The data collection survey targets gig workers in Ho Chi Minh City, focusing primarily on younger individuals aged 18 and above An online questionnaire created by the author using Google Forms will be utilized for respondents to provide their input.

The time period from 1 January to 4 February 2024 (The findings of the questionnaire replies from the screened survey participants will be used to gather data)

To ensure thorough data analysis, it's essential to review collected data for encoding errors before entering and consolidating it using Excel 2010 and SPSS 20.0 The analysis will include statistical descriptions and screening of questionnaires based on response criteria, such as answer quantity and duplicates Reliability will be evaluated using Cronbach's Alpha, where a higher value indicates greater reliability Additionally, exploratory factor analysis (EFA) will be performed to remove inappropriate variables and avoid spurious factors While Cronbach's Alpha assesses correlations among measurement variables, the correlation coefficient will be utilized to eliminate variables that minimally contribute to the measured concept (Hoang Trong and Chu Nguyen Mong Ngoc, 2008).

Several key criteria for conducting Exploratory Factor Analysis (EFA) include:

The Kaiser-Meyer-Olkin (KMO) index is a crucial metric for evaluating the appropriateness of factor analysis, with values ranging from 0.5 to 1.0 A KMO value greater than 0.5 indicates that the data is suitable for factor analysis, while a value below 0.5 suggests that the data may not be appropriate for this method.

Bartlett's test is utilized to assess the correlation between variables within the same factor A significant result (p < 0.05) suggests a strong overall correlation among the observed variables (Hoang Trong and Chu Nguyen Mong Ngoc, 2008).

The factor loading coefficient indicates the strength of the correlation between variables and factors, playing a crucial role in determining the effectiveness of Exploratory Factor Analysis (EFA) A coefficient of 0.5 or higher confirms the practicality of EFA, while loading levels are categorized as acceptable (greater than 0.3), important (greater than 0.4), and significant (greater than 0.5) According to Hair et al (2006), for a sample size of 350 or more, a factor loading coefficient above 0.3 is acceptable; for a sample size of 100, a coefficient above 0.55 is necessary; and for a sample size of around 50, a coefficient exceeding 0.75 is required.

Pearson correlation analysis is essential for examining the relationship between independent and dependent variables in linear regression analysis If no association exists, it's crucial to consider removing these variables from the model Additionally, this analysis identifies correlations among independent variables, which can lead to multicollinearity, significantly affecting the outcomes of regression analysis (Chu Nguyen Mong Ngoc and Hoang Trong, 2008).

Multiple regression analysis is essential for evaluating the suitability of a research model Researchers often rely on the R² (R Square) coefficient to determine model acceptance If the dependent variable significantly correlates with all independent variables (p < 0.05), the model is considered appropriate Additionally, the F test in the variance analysis table further confirms this relationship.

The dependent variable: Y and the linear regression equation have the following form:

In which: po, pi, p2, p3, pn are the regression parameters.

X is the independent variable (X1, X2, X3, Xn)

The ANOVA analysis reveals that the F value demonstrates a significant level of significance, indicating that the regression model is suitable for the collected data when the significance level is less than 0.05 Furthermore, the included variables exhibit statistical significance at a 95% confidence level.

CHAPTER 3 - SUMMARY: In general, the research technique was provided by the authors in Chapter 3 The writers used two primary methodologies to carry out the research process: the "quantitative research method" and the "qualitative research method." The authors conducted "qualitative research" by means of in-depth interviews with clients who had varying degrees of technological expertise The authors postulated technology overload as the independent variable and four additional factors as mediators impacting the task performance of gig workers based on the findings of their qualitative investigation.

The formal research phase employed quantitative research methods, utilizing multiple-choice surveys and individual interviews Chapter 3 discusses key principles and standards in data analysis, detailing essential components of the quantitative research process, including sampling design, data collection, data analysis, and regression analysis.

Cronbach's Alpha reliability test of the measurement scale (Hà)

To ensure the reliability of the survey's observed variables, it is essential to evaluate the reliability of the constructed measurement scales The research team will eliminate irrelevant variables to achieve statistically significant results in the assessment of these scales for further analysis.

The research team identified five elements (Independent Variables) that affect the task performance of gig workers (Dependent Variable) in the theoretical basis chapter:

(1) Technology overload (TO); (2) Challenge Technostressors (CT); (3) Hindrance

Technostressors (HT), creativity, and burnout (BO) are key factors influencing task performance, which serves as the dependent variable in this study It is essential to test the scale, as the elements of the research are derived from established definitions, previous studies, and the authors' insights.

Table 4.7 Results of testing the reliability of the scale for the variable Technology overload (TO)

Scale Mean If Item Deleted

Scale Variance If Item Deleted

Cronbach's Alpha if Item Deleted

The Technology Overload (TO) scale demonstrated strong reliability, achieving a Cronbach's Alpha of 0.839, surpassing the acceptable threshold of 0.6 The lowest Corrected Item-Total Correlation was 0.775, indicating a close relationship among the variables, which effectively measure Technology Overload Both metrics confirm the reliability of the scale, supporting its use in further analyses.

Corrected Item - Total Correlation of 4 observed variables from 0.775 to 0.834 is all greater than 0.3.

Table 4.8 Results of testing the reliability of the scale for the variable Challenge

Cronbach's Alpha If Item Deleted

The Challenge Technostressors (CT) scale demonstrated strong reliability, with a Cronbach's Alpha of 0.906, indicating high internal consistency among the observed variables The lowest Corrected Item-Total Correlation was 0.750, confirming that the variables are closely related and effectively measure Challenge Technostressors Furthermore, when analyzing four observed variables, their Corrected Item-Total Correlations ranged from 0.750 to 0.834, all exceeding the acceptable threshold of 0.3, thus ensuring reliability for subsequent analyses.

Table 4.9 Results of testing the reliability of the scale for the variable Hindrance

Scale Mean if Item Deleted

Scale Variance if Item Deleted

The Hindrance Technostressors scale demonstrated strong reliability, achieving a Cronbach's Alpha of 0.875, significantly surpassing the acceptable threshold of 0.6 The lowest Corrected Item-Total Correlation for the observed variables was 0.680, indicating a close relationship among the variables, confirming their effectiveness as measurement tools for Hindrance Technostressors Furthermore, when four observed variables were included, their Corrected Item-Total Correlation ranged from 0.680 to 0.770, all exceeding the minimum threshold of 0.3, thus ensuring their reliability for further analysis.

Table 4.10 Results of testing the reliability of the scale for the variable Creativity (CR)

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Creativity (CR): Reliability was attained for the observed variable of the

The Creativity (CR) scale demonstrated a high reliability with a Cronbach’s Alpha of 0.905, exceeding the acceptable threshold of 0.6 Additionally, the variable with the lowest Corrected Item-Total Correlation was 0.760, suggesting that the items within this scale are closely related and serve as effective measurement variables for assessing creativity.

Creativity (CR) Additionally, both ensure reliability and are used in subsequent analyses

On the other hand, when 4 observed variables are included, the Corrected Item - Total

Correlation of 4 observed variables from 0.760 to 0.808 is all greater than 0.3.

Table 4.11 Results of testing the reliability of the scale for the variable Burn out

The Burnout (BO) scale demonstrated high reliability, achieving a Cronbach’s Alpha of 0.918, well above the acceptable threshold of 0.6 Additionally, the variable with the lowest Corrected Item-Total Correlation was 0.780, confirming that the items within the scale are closely interconnected and effectively measure burnout.

(BO) Additionally, both ensure reliability and are used in subsequent analyses On the other hand, when 4 observed variables are included, the Corrected Item - Total

Correlation of 4 observed variables from 0.780 to 0.831 is all greater than 0.3.

Table 4.12 Results of testing the reliability of the scale for the variable Task performance (TP)

Cronbachs Alpha if Item Deleted

The Task Performance (TP) scale demonstrated high reliability, with a Cronbach's Alpha of 0.925, indicating strong internal consistency The lowest Corrected Item-Total Correlation was 0.793, suggesting that the variables within this scale are closely related and effective for measuring Task Performance Both the reliability of the scale and its components support their use in further analyses.

Corrected Item - Total Correlation of 4 observed variables from 0.793 to 0.859 is all greater than 0.3.

Exploratory Factor Analysis (EFA) Analysis

4.3.1 EFA analysis for the Technology overload (TO) variable

To ensure the reliability of the scales, exploratory factor analysis (EFA) was conducted using the Principal Components method with Varimax rotation for factor extraction According to Hoang Trong and Chu Nguyen Mong Ngoc (2008), a suitable factor analysis requires a KMO value between 0.5 and 1; values below 0.5 indicate that factor analysis may not be appropriate for the dataset.

Exploratory Factor Analysis (EFA) was performed on both independent and dependent variables to pinpoint those that inaccurately measure their associated factors The analysis aimed to refine the scales and enhance reliability by identifying the latent variables represented by the observed variables.

Table 4.13 KMO and Bartlett’s Test results for the Technology overload (TO) variable

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

The results from Table 4.13 indicate a high Bartlett’s Test coefficient of 0.857, which is above the 0.5 threshold and below 1 Additionally, the Bartlett's Test shows a significance level of 0 (Sig = 0.000), allowing us to reject the null hypothesis that suggests no correlation between the observed variables Therefore, we conclude that the Exploratory Factor Analysis (EFA) is highly appropriate for this study.

Table 4.14 The total extracted variance for Technology overload (TO) variable

Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % ofVariance I Cumulative %

Extraction Method Principal Component Analysis.

The analysis revealed that the Eigenvalues exceed 1, and through the Principal Components extraction method with Varimax rotation, one factor was extracted from four observed variables All factor loadings surpassed 0.5, resulting in a total extracted variance of 84.649%, which significantly exceeds the 50% threshold This indicates that the five extracted factors account for 84.649% of the data variance, demonstrating their practical significance for the research findings.

Table 4.15 EFA factor matrix for Technology attitude (TO)

The factor loadings of the variables are all greater than 0.5 so it's meet the demands of the results

4.3.2 Hindrance Technostressors and Challenge Technostressors

Table 4.16 KMO and Bartlett’s Test results for Hindrance Technostressors and

Approx Chi-Square df Sig

Assuming that there is no statistically significant correlation between the observed variables of Hindrance Technostressors and Challenge Tcchnostrcssors in the population, the null hypothesis HO is true.

The results from Table 4.16 indicate that the Bartlett's Test coefficient for the factor analysis is significant, with a value of 0.851, which is greater than 0.5 and less than 1 Additionally, the significance level is 0.000, confirming that the null hypothesis (H0) is rejected This suggests a correlation between the variables in the population, indicating that the observed variables are interconnected Consequently, it is justified to use factor analysis to combine these variables.

Table 4.17 The total extracted variance for Hindrance Technostressors and

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums Of Squared Loadings Total % Of Vallance Cumulative % Total % Of Variance Cumulative % Total % ofVanance Cumulative %

Extraction Method Principal Component Analysis

Using the Principal Components extraction method with Varimax rotation, two components were identified from eight observed variables, as indicated by Eigenvalues exceeding 1 The total variance extracted was 75.822%, significantly surpassing the 50% threshold, with all factor loadings greater than 0.5.

Table 4.18 EFA factor matrix for Hindrance Technostressors and Challenge

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in

Therefore, Table 4.18 confirms that the scales meet the requirements and remain unchanged There are 2 factors and 8 observed variables as follows: (1) Technology overload (TO); (2) Challenge Technostressors (CT).

• Factor 1, corresponding to the concept ofChallenge Technostressors , includes 4 observed variables CT1, CT2, CT3, CT4.

• Factor 2, corresponding to the concept of Hindrance Technostressors, includes 4 observed variables HT1, HT2, HT3, HT4.

4.3.3 Burn out (BO) and Creativity (CR)

Table 4.19 KMO and Bartlett’s Test results for Burn out and Creativity variables (Quantitative research results)

Assuming that there is no statistically significant correlation between the observed variables of Hindrance Technostressors and Challenge Technostressors in the population, the null hypothesis HO is true.

The results from Table 4.19 indicate a high Bartlett's Test coefficient, which exceeds 0.5 and is below 1 (0.5 < 0.895 < 1), meeting the necessary criteria for factor analysis Additionally, the significance level is less than 0.5 (Sig = 0.000), allowing us to reject the null hypothesis (HO) This rejection suggests a correlation among the variables in the population, indicating that the observed variables are interconnected Consequently, it is justified to use factor analysis for combining these variables.

Table 4.20 The total extracted variance for Purchase intention (PI) variable

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % or Vallance Cumulative % Total % of Variance Cumulative % Total % ofVanance Cumulative %

Extraction Method Principal Component Analysis

Using the Principal Components extraction method and Varimax rotation, two components were identified from a set of eight observed variables, with Eigenvalues exceeding one The total variance extracted was 78.924%, significantly surpassing the 50% threshold, and all factor loadings for the variables were above 0.5.

Table 4.21 EFA factor matrix for Purchase intention (PI) variable

Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in

Therefore, Table 4.21 confirms that the scales meet the requirements and remain unchanged There are 2 factors and 8 observed variables as follows: (1) Creativity (CR), Burn out (BO).

• Factor 4, corresponding to the concept of Creativity, includes 4 observed variables

• Factor 5, corresponding to the concept of Burn out, includes 4 observed variables BOI, BO2, BO3, BO4.

4.3.2 EFA factor analysis for task performance variable

This study focuses on the task performance of gig workers as the dependent variable, which is a key observed variable in the analysis The factor analysis reveals a high KMO coefficient of 0.843 and a significant Bartlett's test (Sig = 0.000), confirming the data's suitability for exploratory factor analysis (EFA) at a 95% confidence level An eigenvalue cut-off of 3.270 is established, utilizing the Principal Components extraction method with Varimax rotation The analysis identifies a single factor with four observed variables that align with the concept of gig workers' task performance, accounting for a total extracted variance of 81.741% The EFA matrix indicates that all observed variables meet the factor loading requirement, with values exceeding 0.5.

Table 4.22 The results of the KMO and Bartlett's tests for the task performance of gig workers

Kaiser-Meyer-Olkin Measure of Sampling

Assuming, the null hypothesis HO is that the observed variables of Purchase factor are not correlated with each other in the population.

The results in Table 4.12 show that the Bartlett's Test coefficient in factor analysis is high and meets the required criteria, being greater than 0.5 and less than 1 (0.5 < 0.849

The Bartlett's Test results show a significance level of 0.000, which is less than 0.05, leading to the rejection of the null hypothesis (H0) This indicates a correlation among the observed variables, confirming that factor analysis is appropriate for grouping these variables within the population.

Table 4.23 The total extracted variance for the concept of the task performance of gig workers

Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance 1 Cumulative % Total % of Variance Cumulative %

Extraction Method: Principal Component Analysis.

Table 4.24 EFA factor matrix for dependent variable

The analysis of EFA results led to the development of a model illustrating the positive and negative effects of technology overload on gig workers' task performance This model distinguishes between challenge technostressors and hindrance technostressors, specifically focusing on the experiences of gig workers in Ho Chi Minh City.

Figure 4.1 Research model after EFA analysis

Results of correlation testing, regression analysis, and model hypothesis testing

4.4.1 Analysis of correlation matrix between variables

To determine the correlation between independent variables and the dependent variable of task performance among gig workers, it is essential to explore the relationship between these variables Before conducting regression analysis, assessing the linear correlation between them is crucial for accurate results.

Table 4.25 The matrix represents the linear relationship between the variables in the model

TO CT HT CR BO TP

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0 05 level (2-tailed).

The correlation matrix shows the relationships among the independent variables in the model as well as those between the dependent variable and each of them.

The Technology Overload (TO) variable demonstrates relationships with CT and

The analysis reveals a significant correlation between the variables, with HT showing a correlation coefficient of r = 0.121 and 0.314, both significant at p < 0.005 Additionally, the relationship between CT and CR is indicated by a correlation of r = 0.302 Although HT has a connection with BO at r = 0.297, it remains significant at the 5% level with a p-value below 0.05.

The dependent variable of task performance (TP) demonstrates a significant association with the mediators of creativity (CR) and burnout (BO), with correlation coefficients of r = 0.644 and r = 0.726, respectively, and a significance level of sig = 0.000, which is less than 0.05 Therefore, multiple regression studies utilizing this model can effectively employ the data.

4.4.2 Multiple Linear Regression (MLR) Analysis

Next, the authors performed multiple linear regression analysis to test research hypotheses and measure the impact of independent variables on the dependent variable using simultaneous regression method

4.4.2.I Regression analysis between Technology Overload (TO) and Challenge

Model Summaryb Table 4.26 Table of criteria to evaluate the fit of the model

Std Error of the Estimate Durbin-

1 ■785a 616 614 63359 2.309 a Predictors: (Constant), TO b Dependent Variable: CT

The analysis investigates the linear relationship between the dependent variable, Challenge Technostressor, and the independent variable, Technology Overload The findings reveal that R squared is 61.4%, indicating that 61.4% of the variation in Challenge Technostressor can be explained by Technology Overload.

Table 4.27 ANOVA model fit test

Squares df Mean Square F Sig.

Total 238.140 229 a Dependent Variable: CT b Predictors: (Constant), TO

The ANOVA panel test results indicate a significance value of 0.000, which is less than the 0.05 threshold, confirming that the model effectively fits the research data This ensures that the interpretation of the regression results is both safer and more accurate Specifically, the F-statistic is 365.221, leading to the rejection of the null hypothesis in the F test Consequently, this suggests that the Technology Overload (TO) factor significantly contributes to the variations observed in Challenge Technostressor (CT).

Table 4.28 Table of regression results Coefficients

B Std Error Beta Tolerance VIF

The fluctuations observed in Challenge Technostressor (CT) variable (dependent variable) are accounted for Technology Overload (TO) independent variable.

The regression equation according to the Unstandardized coefficients:

The regression equation according to the Standardized coefficients:

4.4.2.2 Regression analysis between Technology Overload (TO) and Hindrance

Model Summaryb Table 4.29 Table of criteria to evaluate the fit of the model

Std Error of the Estimate

1 • 648a 420 418 79818 1.953 a Predictors: (Constant), TO b Dependent Variable: HT

The analysis investigates the linear relationship between the dependent variable, Hindrance Technostressor, and the independent variables This testing aims to determine whether a significant linear correlation exists between the dependent and independent variables.

R squared is 42%, that is, the variation of Hindrance Technostressor (dependent variable) is explained by 1 independent variables: Technology Overload

Squares df Mean Square F Sig-

Total 250.492 229 a Dependent Variable: HT b Predictors: (Constant), TO

The ANOVA panel test reveals a significance value of 0.000, which is less than the 0.05 threshold, indicating that the model effectively fits the research data As a result, the interpretation of the regression outcomes is deemed more reliable and precise According to Table 4.19, the F-statistic is 165.236 with a significance value of 0.000, leading to the rejection of the null hypothesis in the F test This finding suggests that the Technology Overload (TO) factor significantly contributes to the variations observed in Hindrance Technostressor (HT).

The fluctuations observed in the Hindrance Technostressor (HT) variable

(dependent variable) are accounted for Technology Overload (TO) independent variables.

4.4.2.3 Regression analysis between Hindrance Technostressor (HT) and Burn out (BO)

Table 4.32 Table of criteria to evaluate the fit of the model

1 705a 497 495 71573 1.971 a Predictors: (Constant), HT b Dependent Variable: BO

A study was conducted to assess the linear relationship between burnout, the dependent variable, and hindrance technostressor, the independent variable The analysis revealed an R-squared value of 49.7%, indicating that hindrance technostressor accounts for nearly half of the variation in burnout levels.

Total 232.312 229 a Dependent Variable: BO b Predictors: (Constant), HT

The ANOVA panel test reveals a significance value of 0.000, which is less than the 0.05 threshold, indicating that the model is well-suited to the research data Consequently, this enhances the reliability and accuracy of the regression results According to Table 4.19, the F-statistic is 225.492 with a significance value of 0.000, leading to the rejection of the null hypothesis (H0) in the F test This outcome implies that the Hindrance Technostressor (HT) factor significantly contributes to the variations observed in Burn Out (BO).

B bld Error Beta 1 ole rance VIF

The fluctuations observed in the Burn Out (BO) variable (dependent variable) are accounted for Hindrance Technostressor (HT) independent variables.

4.4.2.4 Regression analysis between Challenge Technostressors (CT) and Creativity (CR)

1 613a 386 384 85089 2.035 a Predictors: (Constant), CT b Dependent Variable: CR

A linear relationship test was conducted to assess the connection between the dependent variable, Creativity, and the independent variable, Hindrance Technostressor The results indicated that R squared is 38.6%, meaning that 38.6% of the variation in Creativity can be explained by Hindrance Technostressor.

Total 264.685 229 a Dependent Variable: CR b Predictors: (Constant), CT

The ANOVA panel test yields a significance value of 0.000, which is less than the 0.05 threshold, indicating that the model effectively fits the research data Consequently, this enhances the reliability and accuracy of the regression results As shown in Table 4.19, the F-statistic is 137.582, and with a significance value of 0.000, we reject the null hypothesis of the F test This indicates that the Challenge Technostressor (CT) factor significantly contributes to variations in Creativity (CR).

The fluctuations observed in the Creativity (CR) variable (dependent variable) are accounted for Challenge Technostressor (CT) independent variables.

4.4.2.5 Regression analysis between Challenge Technostressors (CT) and Creativity (CR)

1 799a 638 635 61197 2.225 a Predictors: (Constant), BO, CR b Dependent Variable: TP

The analysis investigates the linear relationship between Task Performance, the dependent variable, and various independent variables The objective is to determine if a significant linear correlation exists between them.

R squared is 63.8%, that is, the variation of Task Performance (dependent variable) is explained by 2 independent variables: Creativity (CR) and Burn out (BO)

Total 234.887 229 a Dependent Variable: TP b Predictors: (Constant), BO, CR

The ANOVA panel test results indicate a significance value of 0.000, which is less than the 0.05 threshold, confirming that the model effectively fits the research data This enhances the reliability and accuracy of the regression results As shown in Table 4.19, the F-statistic is 200.098 with a significance value of 0.000, leading to the rejection of the null hypothesis (HO) in the F test Consequently, it can be concluded that the factors of Creativity (CR) and Burnout (BO) significantly contribute to the variations observed in Task Performance (TP).

B Md Error Beta 1 olerance VIF

The fluctuations observed in the Task Performance (TP) variable (dependent variable) arc accounted for Creativity (CR) and Burn out (BO) independent variables.

This regression model has 2 variables that are significant, which is also very consistent with reality The primary factors influencing the task performance of gig workers are as follows:

The strongest impact (Standardized Coefficients p = 0.643) is Creativity factor

The regression analysis reveals that creativity significantly enhances the task performance of gig workers, with results demonstrating a positive impact at the 5% significance level This underscores the importance of creativity as a vital factor influencing the effectiveness of gig workers in their tasks.

The final impact (Standardized Coefficients p = -0.215) is Burnout factor (BO) (4 observed variables): BOI, BO2, BO3, BO4: Regression results on hypothesis:

Price expectations negatively impact the task performance of gig workers, as evidenced by a quantitative study that showed significant results at the 5% level This indicates that burnout adversely affects the productivity and overall performance of gig workers, ultimately hindering their outcomes.

Multicollinearity is the phenomenon where the independent variables have a strong relationship with each other, leading to inaccurate interpretation of regression estimates

If the VIF values of the regression coefficients are < 10, there is no mullicollinearity In the regression results table 4.32, we see the variables’ variance inflation factors in turn are

VIF = (1.896, 1.819) < 10, respectively The V1F values of Table 4.20, Table 4.23, Table 4.26, Table 4.29 are 1,000 and all of which are < 10.

Discussion of results

The gig economy is experiencing rapid growth, attracting individuals of all ages and significantly contributing to its expansion However, gig workers face challenges due to the swift advancement of technology, which notably affects their productivity Despite this, there is a lack of research examining how technological overload influences gig workers' task completion abilities To address this gap, the authors have created and applied a tailored model and scale to systematically measure these factors, drawing on previous research and theoretical frameworks to provide valuable insights into the gig economy.

The study highlights that challenge technostressors, stemming from technology overload, can enhance task performance among gig workers by presenting opportunities for skill development and growth This supports the idea that certain stressors can serve as motivators, encouraging individuals to adapt and excel in the face of technological challenges When managed effectively, the technological demands on gig workers can positively influence their performance, fostering a sense of competence and mastery.

The study reveals that excessive technological demands can create hindrance technostressors for gig workers, negatively impacting their personal development and task accomplishment These demands lead to decreased task performance, emphasizing the need to acknowledge the adverse effects of technology overload, including increased cognitive fatigue and reduced job satisfaction, which ultimately hinder optimal performance.

To effectively manage technology overload among gig workers in Ho Chi Minh City, a balanced approach is essential Service providers and platform managers should recognize both the benefits and challenges of technostressors, actively addressing hindrance technostressors to create a supportive work environment Implementing targeted interventions, including training programs, resource allocation, and workload management strategies, can help mitigate the adverse effects of technology overload on gig workers' task performance.

This study provides valuable insights into how technology overload and different types of technostressors affect gig workers' task performance in Ho Chi Minh City Based on the findings, recommendations were developed to help gig workers improve their performance Data was collected through direct interviews and online questionnaires, employing both qualitative and quantitative assessment methods.

Technology Overload (TO) encompasses four observed variables: TOI, TO2, TO3, and TO4 Qualitative research consistently highlights its significant impact, while quantitative studies confirm its vital role in influencing challenges faced by users.

Our research on gig workers in Ho Chi Minh City explores the dual nature of technostressors, distinguishing between Challenge Technostressors (CT) and Hindrance Technostressors (HT) related to technology overload Preliminary findings reveal that while some elements of technology overload can foster skill development and growth, others hinder task performance and personal advancement Recognizing this complex relationship is crucial for creating effective strategies to manage technology overload, enabling gig workers to leverage its positive aspects and improve their overall task performance in the vibrant gig economy of Ho Chi Minh City.

Challenge Technostressors (CT) (4 observed variables: CT1, CT2, CT3, CT4):

This study highlights the significant impact of challenging technostressors on the creativity of gig workers in Ho Chi Minh City, revealing that both qualitative and quantitative research consistently supports this finding These technostressors can serve as catalysts for creativity, prompting gig workers to generate innovative ideas and adapt to new technologies The positive relationship between challenging technostressors and creativity is particularly evident when workers navigate technical challenges in their daily tasks, suggesting that technological demands can foster creative thinking and contribute to a dynamic work environment.

Hindrance Technostressors (HT), identified through four observed variables (HT1, HT2, HT3, HT4), play a vital role in both qualitative and quantitative research, significantly influencing Burnout (BO) among gig workers in Ho Chi Minh City These technological obstacles contribute to the overall experience of burnout, as preliminary findings suggest that obstructive technostressors hinder effective task completion and personal development The negative correlation between burnout and various technostressors highlights the adverse effects of certain technology demands on the well-being of gig workers To mitigate the impact of these technostressors and foster a healthier work environment, it is essential to understand and address this relationship, enabling targeted interventions that enhance productivity and sustainability for gig workers in the region.

In our study on the effects of technology overload on gig workers in Ho Chi Minh City, we found a significant link between Burnout (BO) and Task Performance (TP) Burnout, represented by variables BOI, BO2, BO3, and BO4, can impede task performance, similar to how cost considerations affect user experience Conversely, the positive emotions associated with successful task completion can motivate workers, akin to the joy and excitement they experience Addressing this complex relationship is essential to ensure that technology overload enhances task performance while reducing burnout among gig workers.

Managerial implications

The study reveals that technology overload significantly influences challenge technostressors, with a Beta value of 0.785 This indicates that managers should develop strategies to utilize technology overload as an opportunity for skill enhancement and growth among gig workers By implementing focused training programs, promoting adaptability, and fostering a supportive work environment, organizations can improve the resilience and job satisfaction of gig workers, ultimately enhancing their effectiveness in managing the ever-evolving technological landscape.

Hindrance technostressors (HT), with a Beta value of 0.648, significantly impact individuals due to technology overload It is crucial for supervisors to recognize the potential negative effects of high technology expectations, which can hinder both work performance and personal development To alleviate these technostressors, strategies such as effective workload management, providing coping tools, and cultivating a supportive work environment are essential These measures help gig workers sustain their well-being and productivity, even amidst the challenges posed by technological overload.

Creativity (CR) significantly influences task performance, with a beta of 0.643, indicating its crucial role in enhancing productivity within organizations To leverage this, companies should prioritize initiatives that nurture creativity among their gig workforce By implementing idea-sharing platforms, fostering a culture that values creative thinking, and providing skill development opportunities, organizations can maximize creativity's positive impact, leading to improved task performance and innovation in the gig economy.

Burnout (BO) has a significant negative impact on task performance, with a Beta value of -0.215, highlighting the need for managers to prioritize strategies to mitigate its effects To enhance the sustained productivity of gig workers, it is essential to implement well-being initiatives, promote work-life balance, and establish robust support systems that address burnout in their dynamic work environment.

Limitations and further research

Limitations: The investigation into the impacts of technology overload on gig workers in

Ho Chi Minh City highlights how challenge and hindrance technostressors affect task performance, yet the study has limitations Its focus on the gig economy in this city means the findings may not be applicable to other regions or sectors, emphasizing the importance of cultural and economic context in understanding these influences.

Vietnam could influence the nature and prevalence of technostressors, potentially limiting the broader applicability of the research outcomes.

The study's cross-sectional design limits its ability to capture the dynamic nature of technostress and technology overload among gig workers, as it only collects data at a single point in time This approach also fails to reflect the long-term effects of these factors on task performance Additionally, the participant recruitment method may not have ensured a diverse representation of gig economy workers, raising concerns about the findings' representativeness and depth.

Due to the specific research boundaries, any generalizations should be approached with caution, as they pertain to a narrow segment of the global workforce within a limited timeframe, potentially affecting both the reliability of the findings and their applicability across different time periods or geographical locations.

To address the limitations identified, future research should expand by conducting comparative analyses of gig workers' experiences in different cities or countries This approach will enhance our understanding of technostress within a global context.

Expanding on this, longitudinal research can vastly improve comprehension of the causality and persistence of technostressors' effects on worker performance over time.

Such studies could monitor changes in the gig economy, assess sustained impacts of technology overload, and capture any fluctuating dynamics related to challenge and hindrance technostressors.

Future research should employ sampling techniques that capture a broader and more diverse demographic, enhancing the representativeness of findings This approach could uncover rich, nuanced insights into the varied experiences of gig workers regarding technology stress.

A mixed-method approach that combines qualitative data with quantitative findings can provide a more comprehensive and empathetic understanding of gig workers' experiences amid technology overload To fully grasp the range of technostressors, future research should also examine the impact of emerging digital tools and platforms that continually transform the gig economy.

Sweller, J., Ayres, p., Kalyuga, s., Sweller, J., Ayres, p., & Kalyuga, s (2011).

Measuring cognitive load Cognitive load theory, 71-85.

Sweller, J (1988) Cognitive load during problem solving: Effects on learning Cognitive science, 72(2), 257-285.

Goodhue, D L., & Thompson, R L (1995) Task-technology fit and individual performance MIS quarterly, 213-236.

Gebauer, J., Shaw, M J., & Gribbins, M L (2010) Task-technology fit for mobile information systems Journal ofinformation technology, 25, 259-272.

Vanduhe, V z., Nat, M., & Hasan, H F (2020) Continuance intentions to use gamification for training in higher education: Integrating the technology acceptance model (TAM), social motivation, and task technology fit (TTF) IEEE Access, 8, 21473 21484.

Kuo, R z., & Lee, G G (2011) Knowledge management system adoption: exploring the effects of empowering leadership, task-technology fit and compatibility Behaviour &

Goodhue, D L., Klein, B D., & March, s T (2000) User evaluations of IS as surrogates for objective performance Information & management, 38(2), 87-101.

Fuller, R M., & Dennis, A R (2009) Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks Information Systems Research, 20(\), 2-17.

Zigurs, L, & Khazanchi, D (2008) From profiles to patterns: A new view of task technology fit Information systems management, 25(1), 8-13.

McGill, T J., & Klobas, J E (2009) A task-technology fit view of learning management system impact Computers & Education, 52(2), 496-508.

Dishaw, M T., & Strong, D M (1999) Extending the technology acceptance model with task-technology fit constructs Information & management, 36(1), 9-21.

Spies, R., Grobbelaar, s., & Botha, A (2020, April) A scoping review of the application of the task-technology fit theory In Conference on e-Business, e-Services and e-Society

(pp 397-408) Cham: Springer International Publishing.

Wiener, M., Cram, w A., & Benlian, A (2023) Algorithmic control and gig workers: a legitimacy perspective of Uber drivers European Journal of Information Systems, 32(3), 485-507.

Bunjak, A., Cerne, M., & Popovic, A (2021) Absorbed in technology but digitally overloaded: Interplay effects on gig workers’ burnout and creativity Information &

Verina, N., & Titko, J (2019, May) Digital transformation: conceptual framework In

Proc, of the Int Scientific Conference “Contemporary Issues in Business, Management and Economics Engineering (pp 9-10).

Tarafdar, M., Cooper, c L., & Stich, J F (2019) The technostress trifecta-techno eustress, techno distress and design: Theoretical directions and an agenda for research

Hargrove, M B (2013) Hargrove, MB, Nelson, DL, and Cooper, CL (2013) Generating eustress by challenging employees: Helping people savor their work Organizational

Leung, L (2011) Effects of ICT connectedness, permeability, flexibility, and negative spillovers on burnout and job and family satisfaction Human Technology: An

Interdisciplinary Journal on Humans in ICT Environments.

Diaz, L, Chiaburu, D s., Zimmerman R D., & Boswell, w R (2012) Communication technology: Pros and cons of constant connection to work Journal of Vocational

Croitor, E., & Benlian, A (2019) Perceived input control on online platforms from the application developer perspective: Conceptualisation and scale development Journal of decision systems 28(\) 19-40.

Carayon, p (1994) Effects of electronic performance monitoring on job design and worker stress: Results of two studies International Journal of Human-Computer

Corbett, J M., Martin, R., Wall, T D., & Clegg, c w (1989) Technological coupling as a predictor of intrinsic job satisfaction: A replication study Journal of Organizational Behavior 91-95.

Ayyagari, R., Grover, V., & Purvis, R (2011) Technostress: Technological antecedents and implications MIS quarterly 831-858.

Tarafdar, ML, Tu, Q., Ragu-Nathan, B s., & Ragu-Nathan, T s (2007) The impact of technostress on role stress and productivity Journal of management information systems,

Shu, Q., Tu, Q., & Wang, K (2011) The impact of computer self-efficacy and technology dependence on computer-related technostress: A social cognitive theory perspective International Journal of Human-ComputerInteraction, 2 7(10), 923-939.

Wang, K., Shu, Q., & Tu, Q (2008) Technostress under different organizational environments: An empirical investigation Computers in human behavior, 24(6), 3002 3013.

Stich, J F., Tarafdar, M., Stacey, p., & Cooper, c (2019) Appraisal of email use as a source of workplace stress: A person-environment fit approach Journal of the

Lee, s w., Cheah, c s., Cheah, Y Y., & Yeo, s F (2023) Psychological Well-being of Gig Workers: A Preliminary Study Global Business & Management Research, 15.

Ra, s., Shrestha, u., Khatiwada, s., Yoon, s w., & Kwon, K (2019) The rise of technology and impact on skills International Journal of Training Research, 17(supl), 26-40.

Umair, A., Conboy, K., & Whelan, E (2019) UNDERSTANDING THE INFLUENCE

OF TECHNOSTRESS ON WORKERS ’JOB SATISFACTION IN GIG-ECONOMY:

Schaufeli, w B., & Taris, T w (2014) A critical review of the Job Demands-Resources Model: Implications for improving work and health In Bridging occupational, organizational and public health (pp 43-68) springer Netherlands.

Demerouti, E., Bakker, A B., Nachreiner, F., & Schaufeli, w B (2001) The job demands-resources model of burnout Journal of Applied psychology, 86(3), 499.

X Cao, J Sun, Exploring the effect of overload on the discontinuous intention of social media users: An S-O-R perspective, Computers in Human Behavior 81

Mahapatra, M., & Pati, s p (2018, June) Technostress creators and burnout: A job demands-resources perspective In Proceedings of the 20 J8 ACM SIGMỈS conference on computers and people research (pp 70-77).

Pattnaik, s c., & Sahoo, R (2021) Employee engagement, creativity and task performance: role of perceived workplace autonomy South Asian Journal ofBusiness

Motowidlo S.J., van Scotter J.R Evidence that task performance should be distinguished from contextual performance J Appl Psychol 1994;79:475-531 doi: 10.1037/0021

In their 2017 study, Chiaburu et al explore the significance of both affiliative and change-oriented citizenship behaviors, alongside task performance, in predicting overall job performance Published in the Human Resource Management Review, the research highlights how these factors contribute to a more comprehensive understanding of employee effectiveness, emphasizing the need for organizations to recognize and foster diverse performance dimensions to enhance overall productivity The findings underscore the importance of balancing interpersonal relationships and proactive change initiatives within the workplace.

Veluchamy, R., Reddy, p., Pillai, R., & Singh, R (2021) A study on work life integration of gig workers An Anthology of Multi-functional perspectives in Business and Management Research, f 23-32.

Watson, G p., Kistler, L D., Graham, B A., & Sinclair, R R (2021) Looking at the gig picture: Defining gig work and explaining profile differences in gig workers' job demands and resources Group & Organization Management, 46(2), 327-361.

Zhao, Y A., Bandyopadhyay, s., & Bandyopadhyay, K (2023) Learning Complex

Technology Online: Effect of Challenge and Hindrance Techno-stressors on Student

Satisfaction and Retention Communications of the Association for information Systems,

Tarafdar, M., Cooper, c L., & Stich, J F (2019) The tcchnostrcss trifccla-tcchno eustress, techno distress and design: Theoretical directions and an agenda for research

Chandra, s., Shirish, A., & Srivastava, s c (2019) Does technostress inhibit employee innovation? Examining the linear and curvilinear influence of technostress creators.

Communications of the Association for Information Systems, 44(1), 19.

Tarafdar, M., Cooper, c L., & Stich, J F (2019) The technostress trifecta-techno eustress, techno distress and design: Theoretical directions and an agenda for research

J Berg, M Furrer, E Harmon, u Rani, M.S Silberman, Digital Labour Platforms and the Future of Work: Towards Decent Work in the Online World, ILO, 2018

M.G Martinez, Solver engagement in knowledge sharing in crowdsourcing communities: Exploring the link to creativity, Research Policy 44 (8) (2015)

Ostoj, I (2021) The structure of gig workers in the global online gig economy by country of origin and its institutional foundations Ekonomia i Prawo Economics and Law, 20(2), 337-351.

Karriker, J H., Hartman, N s., Cavazotte, F., & Grubb III, w L (2021) Identity in the gig economy: Affect and agency Journal of Organizational Psychology, 27(2), 146-159.

Tiêu đề	The Positive And Negative Impacts Of Technology Overload On Gig Workers’ Task Performance Via Challenge Stressors And Hindrance Stressors: The Case Of Gig Workers In Hcmc
Trường học	Đại Học Kinh Tế Thành Phố Hồ Chí Minh
Chuyên ngành	Thương mại - Quản trị kinh doanh
Thể loại	Báo cáo
Năm xuất bản	2024
Thành phố	Hồ Chí Minh

Định dạng
Số trang	118
Dung lượng	2,62 MB