The New Trend: Why Indonesian Digital Start-Up Employees are

Opting for Quiet Quitting?

ADE SUHENDAR, RONALD SETIADI, ARTATI ARTATI, ABDUL ROHMAN

Department of Management,

Binus Business School Master Program,

Bina Nusantara University,

11480, Jakarta,

INDONESIA

Abstract: - Our study examined variable JS, OC, and OCB on QQ within the context of Indonesian digital

start-ups. A survey was conducted on 269 employees from digital start-ups in various sectors, such as

transportation and logistics, food delivery, e-commerce, fintech, digital payments and wallets, and online

learning platforms. Quantitative analysis with SmartPLS 4.0 was used to process the primary data and obtain

the SEM. It showed that six out of seven hypotheses were accepted. The direct effect simulation revealed that

JS and OC significantly influenced OCB and QQ. Additionally, OCB significantly impacted QQ and mediated

the effect of OC on QQ but failed to mediate the relationship between JS and QQ. This research indicates that

mitigating the QQ phenomenon requires a comprehensive focus on improving JS, fostering OC, and

encouraging OCB. In addition, the findings can be leveraged to devise more effective human resource

strategies, including competitive compensation packages, performance-based bonuses, and market-aligned

salaries to increase JS. Also, the study underscores the need for promoting a positive work culture and

employee development opportunities to augment OC and OCB. Ultimately, these insights guide the creation of

human resource policies that can enhance employee performance and commitment, thereby contributing to a

company's overall success and productivity.

Key-Words: - Quiet quitting (QQ), Job satisfaction (JS), Organizational commitment (OC), Organizational

citizenship behavior (OCB).

Received: July 13, 2022. Revised: May 26, 2023. Accepted: June 26, 2023. Published: July 24, 2023.

1 Introduction

Indonesia is experiencing a remarkable economic

shift in today's increasingly digital world with the

rise of digital start-ups that are becoming significant

players in the Southeast Asian digital economy, [1],

[2]. Indonesia's digital economy witnessed a

remarkable surge of 22% from the previous year,

reaching almost US$77 billion in total value in the

previous year, accounting for nearly 40% of digital

economy transactions in the region, [3]. However,

despite this growth and promise, the rise of digital

start-ups has led to a critical issue in the workplace

that affects not only these start-ups but other

industries in the country. This issue is called QQ, a

growing concern among Gen Z and millennial

employees in Indonesia and other parts of the world,

negatively impacting the productivity and growth of

organizations, [4], [5].

The pandemic covid-19 resulted in popular

substantial transformations within the work

environment and has profoundly impacted the

equilibrium between work as well as personal

living, often associated with the QQ phenomenon,

[6], [7]. While the pandemic may have exacerbated

this behavior, previous studies have examined

similar behaviors under various names, such as

disengagement, neglect, and withdrawal, [8], [9],

[10].

Different models and methods have been

employed to explore and understand these

behaviors. However, the efficacy of these methods

and models, particularly in the context of digital

startups in Indonesia, remains a subject of further

examination. QQ is defined by [11], as the gradual

withdrawal of an individual from the organization

and reduced participation in work-related activities.

It is considered an individual response to job stress

and dissatisfaction and a coping mechanism to

reduce or avoid work-related negative emotions. QQ

behaviors can negatively affect employees and

organizations, such as decreased productivity,

increased costs, and reduced morale, [12], [13].

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In studying this diverse body of research, a

classification of the literature reveals that studies on

QQ and similar behaviors can be divided into those

examining the psychological and individual aspects

of QQ, those that delve into organizational factors

and environments, and those considering the

societal and cultural contexts of the phenomenon. A

comparative analysis, however, brings forth a

significant gap in our understanding of how these

behaviors specifically manifest and are dealt with

within the unique context of digital start-ups in

Indonesia.

Recognizing the vital contribution of digital

start-ups to Indonesia's economic landscape, this

research intends to explore the QQ phenomenon,

specifically within digital start-ups, [14]. Start-ups

are known for experimenting with and refining a

business model until it becomes feasible and

scalable, [15]. However, start-ups operate in

conditions of great uncertainty, possess limited

resources, and prioritize innovation and rapid

expansion, [16]. Furthermore, as defined by [17],

digital entrepreneurship uses digital technology to

establish new enterprises and transform existing

ones.

Though there has been considerable research on QQ

in the broader context of work, [18], specific studies

focusing on digital start-ups in Indonesia still need

to be made available. Existing literature has

highlighted the significance of JS, OC, OCB, and

employee withdrawal behavior in the workplace,

[19]. However, integrating these variables within the

context of QQ in digital start-ups still needs to be

explored.

Identifying this gap, our study seeks to

investigate QQ within the niche of digital start-ups.

It uniquely examines the interplay of JS, OC, and

OCB within this dynamic. No existing research has

holistically examined this complex relationship

within the digital start-up context, particularly in

Indonesia. Our focused investigation endeavors to

enhance the current knowledge repository,

comprehensively comprehend this phenomenon and

develop strategies to address its potential adverse

consequences on the digital start-up sector. By

clearly delineating our research's specific scope and

unique methodology, we clarify how it distinguishes

itself from the existing literature and contributes to

it. Through this inquiry, we aim to pioneer a more

localized and context-specific understanding of QQ

in the digital start-up sector, thereby filling a critical

research void and broadening the existing academic

discourse.

2 Literature Review

2.1 Quiet Quitting (QQ)

Withdrawal behavior, a theory similar to QQ, has

been a topic of interest in organizational behavior

for several decades, and it pertains to employees'

psychological or physical disengagement from their

workplace, [20], [21]. QQ, on the other hand, is a

voluntary behavior exhibited by employees in

response to negative work experiences or stressors,

such as absenteeism, turnover, or other forms of

disengagement, [22], [23]. It serves as a means for

employees to escape unpleasant work environments

caused by job dissatisfaction or a lack of OC, [24].

QQ can take many forms, including absenteeism,

showing up late for work, or leaving the

organization altogether, [19], [25]. According to

[26], they have conceptualized QQ as an employee's

intention to reduce or terminate their work

involvement by calling in sick, reducing work

effort, or quitting their job. Understanding QQ is

crucial, and managers need to comprehend the QQ

phenomenon as it can negatively impact OCB and

JS, reducing productivity and increasing expenses

associated with hiring and training their employees.

2.2 Job Satisfaction (JS)

Management, [27], characterizes JS as an emotional

condition marked by positivity, encapsulating an

individual's comprehensive assessment of their job,

the satisfaction of their needs, and the realization of

their anticipations. Meanwhile, based on, [28], the

concept of JS holds significant importance as it

reflects an employee's emotional state towards their

job and the overall work environment. JS is a

multidimensional construct encompassing both

affective and cognitive components, [29]. It

represents an individual's satisfaction and pleasure

from their job and work environment, encompassing

a sense of contentment with their job and work

experiences, [22]. As described by, [23], JS relates

to an individual's positive or negative emotions

associated with their job and job-related encounters.

Numerous employment-related aspects substantially

impact JS, as outlined by, [24].

2.3 Organizational Commitment (OC)

OC, a vital element within management studies, is

typified as an individual's recognition and

participation within a distinct organization, [15]. It

symbolizes the degree of an employee's

identification, active involvement, and preparedness

to offer extra effort to achieve an organization's

goals, [30]. As stated by, [31], OC signifies an

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employee's intensity of dedication, association with

an organization, and intent to strive for its

objectives. Previous studies by, [26], [32], [33],

have persistently emphasized the significance of OC

as a measure of an employee's mental bonding to

their organization and their willingness to contribute

to its goals actively. A heightened sense of OC has

been demonstrated to positively impact employee

results, such as JS and OCB, and diminish the

potential for QQ.

2.4 Organizational Citizenship Behavior

(OCB)

Scholars such as, [28], [29], [31], [34], have all

explored OCB and found that it can be defined as

voluntary actions that exceed an individual's formal

job requirements and support the efficient operation

of an organization. OCB behaviors include assisting

colleagues, taking on extra responsibilities, and

being punctual and reliable, [15]. Even though the

official reward system does not formally

acknowledge it, participating in OCB is crucial in

promoting effective work, as emphasized by [24].

Hence, OCB holds immense significance for the

overall success of any organization. Understanding

the factors that impact OCB can assist organizations

in cultivating a positive workplace atmosphere that

nurtures employee commitment and satisfaction.

This study examined the relationship between JS,

OC, OCB, and QQ in Indonesian digital start-ups.

The hypotheses put forward in this study are as

follows:

H1: JS significantly affects OCB.

A study by [28], discovered that female

educators expressing greater JS were more prone to

display OCB, suggesting a positive relationship

between these two variables. Concurrently, [35],

illustrated that JS influences and is also affected by

OCB, establishing a directly proportionate

relationship between the two. This finding aligns

with previous studies conducted by [24], [29], [34],

[36], which similarly highlight a positive

relationship between JS and OCB. When employees

experience satisfaction in their roles, they exhibit

behaviors that contribute to the betterment of the

organization. This can include assisting colleagues,

willingly taking on additional responsibilities, and

surpassing the expectations of their assigned tasks.

H2: JS does not significantly affect QQ.

Recent investigations by [11], have illuminated the

inverse relationship between JS and QQ, with

preventable absenteeism and the intention to leave

being the vital outcomes of focus. Their study

underscores that employees expressing higher JS are

likely to display reduced preventable absenteeism

and less inclination to leave than those with lower

JS levels. Correspondingly, other researchers,

including, [37], [38], have also identified a

considerable negative correlation between JS and

QQ, using indicators such as intent to leave and

absenteeism. These insights are relevant to

organizational leaders aiming to enhance employee

contentment and maintain staff retention.

H3: OCB mediates the effect of JS on QQ.

Recent studies by [24], [25], have shed light on the

relationship between JS and OC as well as OCB.

The findings suggest that JS has a negative

correlation with turnover and a positive correlation

with OCB. This suggests that when employees

experience a sense of loyalty and connection to the

organization, they are less inclined to leave, which

can be viewed as a manifestation of the QQ issue.

Turnover can be viewed as a physical withdrawal

behavior, whereas QQ is a psychological

detachment from the organization. These results

highlight the importance of fostering a positive

work environment that promotes JS, which can

increase employee commitment and engagement in

OCB.

H4: OC significantly affects OCB.

Research by [15], has demonstrated a positive

linkage between OC and OCB, implying that

dedicated employees are more likely to involve

themselves in discretionary actions that benefit the

company's targets and aims. This assertion is further

backed by [31], who illustrated that employees

exhibiting higher OC levels are more disposed to

partake in OCB. Engagement in OCB can ultimately

aid in enhancing the organization's sustainability

performance, emphasizing the necessity for

organizations to concentrate on nurturing OC and

advocating for OCB among their workforces.

H5: OC does not significantly affect QQ.

OC is a crucial aspect of employee behavior in

the management field. However, it is noteworthy

that OC could significantly and negatively affect

QQ—a behavior where employees disengage from

their jobs without actively seeking alternative

employment. To understand this relationship better,

researchers have found that the loyalty indicator, a

sub-dimension of OC, is the most significant factor

in determining the association between OC and QQ,

[39]. Interestingly, OC is also highly associated with

absenteeism, but the behavior is the opposite of QQ.

Employees who exhibit high levels of OC tend to

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have minimum absenteeism, reflecting their

dedication to their work and organization.

H6: OCB mediates the effect of OC on QQ.

Studies have determined that OC notably affects

employee performance via OCB, a key element in

boosting job productivity, [40]. This discovery

emphasizes the likelihood of psychological

withdrawal appearing as diminished job

productivity, which can be alleviated by cultivating

a robust sense of OC among employees. In addition,

a research piece by, [41], showed that OC exerts a

direct and favorable effect on OCB, ultimately

contributing to enhanced employee performance. As

a result, individuals exhibiting stronger OC are more

likely to display elevated levels of OCB and

superior performance, leading to a reduced

propensity for QQ.

H7: OCB does not significantly affect QQ.

As mentioned in, [42], prior studies have

consistently shown a notable correlation between

OCB and employee turnover. The absence of

displaying OCB can increase the likelihood of silent

resignations within a group, indicating that turnover

is one of the characteristics of QQ. Interestingly, a

high level of OC is significantly correlated with

lower turnover intention and is positively linked to

OCB. These findings suggest that enhancing

employee OC and OCB can positively reduce

turnover and promote silent resignation from the

group.

Based on the explanations, the study model

framework can be illustrated in Figure 1.

Fig. 1: QQ Research Model

3 Methodology of Research

3.1 Methodological

Our research used quantitative methodology,

utilizing a large sample size to ensure

comprehensive data collection. A Google Form

questionnaire that employed a 5-point Likert scale

was utilized. This approach allowed for efficient and

standardized data collection, enabling the

acquisition of extensive and informative primary

data. The questionnaire included detailed indicators

for the dependent and independent variables and

gathered information about the respondents' profiles.

According to [43], in [44], it was proposed that

employing 5-point rating scales can minimize

confusion and enhance the response rate. In

addition, this survey was developed by distributing

the questionnaire that had been compiled to several

respondents of digital startup companies, which

were then passed back to other respondents'

coworkers in the company (using snowball

sampling). Table 1 shows sample questions for each

dimension of each variable.

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Table 1. Sample Questions

3.2 Population and Sampling

This study explored the workforce of digital start-

ups and their unique characteristics by utilizing a

cross-sectional questionnaire design with minimal

intervention. The sample group consisted of Gen Z

individuals aged 18 to 25 and millennials aged 26 to

42, making for a diverse and representative pool of

employees. In addition, the research employed a

snowball non-probability sampling technique,

leading to a sample of 269 participants who fulfilled

the necessary criteria. Snowball sampling, as

described in [45], is a non-probability sampling

method where existing units assist in recruiting new

units to be included in the sample.

3.3 Data Analysis

Our data analysis leverages the Partial Least

Squares (PLS) method, a versatile and powerful tool

for exploring complex interrelationships among

various variables. PLS aids in making predictions,

confirming information, and testing hypotheses, a

critical advantage when dealing with multifaceted

datasets, [46], such as ours. In choosing PLS, we

adhered to specific recommendations. For instance,

the ideal sample size for PLS should fall between

150 to 300 observations, and the model should

contain seven or fewer constructs or key

components. We ensured our dataset met these

parameters. We also agreed that measured variables

should share at least 50% of their variance for

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reliable results. Additionally, to prevent ambiguity

and increase the accuracy of our analysis, we

carefully identified all constructs in our model

before the analysis. Following these guidelines

helped us effectively analyze the relationships

among our variables of interest, leading to robust,

well-informed conclusions. As we proceed, we will

also discuss various diagnostic and validation

techniques we used to ensure the reliability of our

results. By sharing our methodology and findings

transparently, we aim to bolster the reliability and

replicability of similar future research.

4 Results

4.1 Demographic Results

The respondents comprised 47.6% of start-up

employees aged 18 to 25, while 52.4% were 26 to

42. In addition, 71.7% were in Jakarta, 58.4% were

women, and 41.3% were men. Regarding the

working period, 46.8% of respondents had worked

for 2 to 5 years, while 46.5% had less than two

years of working experience.

4.2 Confirmatory Factor Analysis (CFA)

CFA tests measure whether a construct is consistent

with a latent variable. To provide an in-depth

analysis, we meticulously refined our model by

excluding indicators that did not meet the validity

requirements, thus ensuring the precision and

reliability of our findings. Figure 2 presents the

SEM analysis results on the relationship between

variables. In addition, Table 2 shows the validity

test results after omitting invalid question indicators,

namely JS2, JS10, OC4, OC6, OC10, QQ4, and

QQ7.

Fig. 2: SEM analysis results

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Table 2. Outer Loadings

The study's findings indicate that all indicators

for each variable demonstrate validity. The JS

variable exhibits the highest loading factor value of

0.911 on the JS6 indicator, which pertains to

satisfaction with superior supervision. The OC

variable's most significant loading factor value is

0.866, observed on the OC1 and OC8 indicators,

representing affective and continuance commitment

dimensions, respectively. The OCB variable's most

significant loading factor value is 0.875 on the

OCB3 indicator, corresponding to the civic virtue

dimension. In relation to the QQ variable, the

indicator with the highest loading factor value is

QQ5, which corresponds to the physical dimension.

The loading factor value of 0.900 indicates a strong

association between the QQ5 indicator and the

physical dimension of the variable.

4.3 Validity Test

Two approaches were used: convergent and

discriminant. In assessing convergent validity,

factor loadings were examined to ensure they

exceeded the threshold of 0.708. The factor loading

values in Table 2 all met this criterion, indicating

their validity. Moreover, Table 3 shows that all

constructs included in the study model had Average

Variance Extracted (AVE) values above 0.5. The

OC construct had the lowest AVE value of 0.685,

which still surpassed the threshold.

Table 3. AVE

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We employed several methods for discriminant

validity testing, including Heterotrait-monotrait

(HTMT), Fornell Larcker, and cross-loading.

However, we present only the HTMT results (Table

4), considered the most accurate test. Table 4

displays the HTMT ratios for all variables, and it is

crucial to note that these ratios should be below 0.9

to establish reliable results.

Table 4. Heterotrait-Monotrait Ratio (HTMT) –

Matrix

Construct

OCB

0.670

OCB

0.692

0.670

0.863

0.701

0.675

Based on our findings, all the HTMT ratios for

the variables examined were below 0.9, indicating

that our results are reliable. These validity

assessments strengthen the overall robustness of our

analysis and provide confidence in the relationships.

4.4 Reliability Test

A reliability test assesses the overall reliability of

the indicator block that measures the constructs.

Table 5 provides evidence that the scores are above

0.7, which implies they are reliable.

Table 5. Composite Reliability

Construct

Composite

reliability

(rho_a)

Composite

reliability (rho_c)

0.959

0.965

0.935

0.946

OCB

0.906

0.930

0.952

0.959

In addition to composite reliability, we can use

Table 6, and the value is considered acceptable if

exceeds 0.708, [46]. Meanwhile the the lowest OCB

value at 0.906.

Table 6. Cronbach’s Alpha

Construct

Cronbach’s Alpha

0.958

0.934

OCB

0.906

0.944

4.5 Effect of F-Square and R-Square

As stated by [46], the F-Square value is categorized

into minor, moderate, and significant effects,

represented by values of 0.02, 0.15, and 0.35.

Therefore, refer to Table 7, where the F-Square

values exceed 0.35, which means all variables are

significant.

Table 7. The F-Square

Construct

OCB

0.670

OCB

0.692

0.670

0.863

0.701

0.675

Table 8. The R-Square

Table 8 shows the results of the R-Square

simulation. It is typically expected to range between

0 and 1, as mentioned by [46]. We can see that JS

and OC variables collectively account for 48.6% of

the influence on OCB. Additionally, the combined

influence of JS, OC, and OCB variables on QQ is

estimated to be 71.2%.

4.6 Model Fit

To evaluate the compatibility of the study model

with the data, a model fit test was conducted. As

shown in Table 9, the SRMR value is 0.047, lower

than the threshold of 0.10, [47], which means good.

Additionally, the value of NFI of 0.832 exceeds the

threshold of 0.8, indicating that the model meets the

criteria for fit. The SRMR and NFI values indicate

that the model is well-fitted to the data.

Table 9. Model Fit

4.7 Hypothesis Testing

SmartPLS 4.0 was employed to test the hypotheses

using bootstrapping as a statistical method. This

approach allowed for assessing the direction and

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significance of the relationships of latent variables.

Details are presented in Table 10.

Table 10. Path Analysis (Direct Effects) and Hypothesis Testing

Table 11. Specific Indirect Effects and Hypothesis Testing

In our study, we scrutinized five distinct

hypotheses concerning the intricate relationships

among JS, OC, OCB, and QQ. The inaugural

hypothesis (H1) postulated a positive correlation

between JS and OCB. A detailed data evaluation

demonstrated a significant positive link, with JS

exerting a direct influence on OCB, quantified as

0.424. The critical p-value registered as 0.000, and

the T-statistic was 5.229, strongly supporting H1.

Our second hypothesis (H2) envisioned an inverse

relationship between JS and QQ. The statistical

analysis unveiled a negative correlation, with JS

directly influencing QQ, registered as -0.639,

thereby substantiating the validity of H2. In our

fourth proposition (H4), we postulated a positive

interconnection between OC and OCB. Upon

analysis, OC was found to impact OCB directly,

measured as 0.349, underscoring a significant

positive correlation, thereby validating H4. The fifth

hypothesis (H5) predicted a negative association

between OC and QQ. Our dataset affirmed this,

exhibiting a direct influence of OC on QQ,

quantified as -0.198. A significant inverse

correlation was revealed, resulting in the validation

of H5. Finally, the seventh hypothesis (H7)

anticipated a negative correlation between OCB and

QQ. Our data analysis revealed a direct influence of

OCB on QQ, and this indicated a significant

negative relationship, hence, validating H7.

Collectively, these findings significantly enhance

our understanding of the intricate interrelationships

among JS, OC, OCB, and QQ.

In this research, two hypotheses featuring

mediating variables were also examined. The results

of these tests and indirect effects are represented in

Table 11 for hypotheses H3 and H6. The initial row

of Table 11 demonstrates the specific indirect

impact of JS on QQ via OCB. The value of the

original sample stands at -0.039, suggesting a

negative but statistically insignificant indirect

influence of JS on QQ through OCB. The standard

deviation is 0.024, and the T-statistic of 1.635 is not

significant at p < 0.05. Thus, H3 is invalid,

indicating that OCB does not effectively mediate the

link between JS and QQ. The second row in Table

11 exhibits the specific indirect influence of OC on

QQ via OCB.

The original sample value of -0.032 indicates an

adverse and statistically significant indirect effect of

OC on QQ through OCB. The mean value of the

sample, which is -0.031, closely resembles the

original sample value, indicating that the indirect

effect remains consistent across the sample. The

observed relationship is supported by evidence

indicating a standard deviation of 0.017 and a

significant T-statistic of 1.933 at the p < 0.05 level.

Consequently, H6 is validated, suggesting OCB

partially mediates the connection between OC and

QQ.

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5 Discussion

5.1 The Impact of JS on OCB and QQ

Our study suggests that as the levels of JS rise,

employees are more prone to display OCB, which

can ultimately prove advantageous to the

organization. This discovery aligns with past studies

demonstrating a strong positive correlation between

JS and OCB, [24], [29], [48].

The investigation corroborated the second

hypothesis (H2), signifying a notable negative

association between JS and QQ amongst employees.

This mirrors earlier research that defined a negative

connection between these two variables.

Discontented employees are more likely to partake

in QQ behaviors, which encompasses diminished

effort, motivation, and reduced OC, [11], [22], [23].

Contrarily, the study did not validate the third

hypothesis (H3) that proposed OCB as a mediator

between JS and QQ. Even without a mediating

influence, comprehending the interplay between JS,

QQ, and OCB can guide management in

formulating strategies to enhance employee

satisfaction and diminish the probability of

disengagement and attrition. The current study

challenges the previous research conducted by [24],

which suggested that OCB mediates the relationship

between JS and QQ. The disparity in findings could

be attributed to the study's participants, who

primarily belong to generations Y and Z and have

relatively shorter job tenures. These individuals

often prioritize their personal needs over

organizational duties, leading to higher engagement

in OCB despite being dissatisfied with other job

aspects.

In addition, the relationship between JS, OCB,

and QQ may be more intricate than initially

assumed, with OC potentially acting as a mediator

between JS and OCB, altering the flow from JS to

OC to OCB to QQ. Further research is necessary to

clarify the complex interplay between these

variables, especially in the context of younger

generations in the workforce.

5.2 The Impact of OC, OCB, and QQ

The outcomes of this study underscore the pivotal

role of OC in promoting OCB. This result

corresponds with past studies by [15], [31], that also

observed a positive correlation between OC and

OCB. Employees who feel a strong bond with their

organization and its objectives are more inclined to

participate in actions that favor the organization,

ultimately strengthening their sense of belonging.

The fifth hypothesis (H5) exposed a notable inverse

association between OC and QQ, suggesting that

employees with a stronger connection to their

organization are less likely to display QQ behaviors.

This outcome resonates with previous research by

[26], which also noted a negative connection

between OC and QQ.

Furthermore, the sixth hypothesis (H6) confirms

that OCB can mediate OC and QQ. Therefore, OCB

can reinforce OC, which in turn can enhance

employee performance and reduce QQ behavior.

This finding suggests that OCB is a mechanism

through which OC positively influences QQ.

Finally, the seventh hypothesis (H7) exhibited a

significant inverse relation between OCB and QQ.

This result is in sync with past research that

ascertained that a decrease in OCB was linked to

reduced employee JS and a rise in turnover

intentions, indicators of QQ, [41]. These findings

furnish crucial insights for organizations to foster a

positive work culture that encourages employee

commitment and citizenship behavior and lessens

the likelihood of QQ behaviors.

5.3 Implication and Recommendation

The study's implications aim to help digital start-up

companies tackle the issue of QQ by utilizing each

variable's highest loading factor values. Companies

should improve JS by providing regular employee

feedback, especially in superior supervision and

feedback. To enhance OC, companies should foster

a positive workplace culture, provide opportunities

for professional development, acknowledge

employee contributions, and encourage employee

involvement. Similarly, companies should create a

culture of mutual assistance and cooperation to

improve OCB by promoting mutual help and

support, offering opportunities for collaboration,

rewarding helpful behavior, and monitoring

employee involvement regularly. In contrast, QQ

behaviors like leaving work early (physical

dimension) or pretending to be busy (psychological

dimension) relate to QQ. Thus, remote working

trends require companies to monitor their

employees' performance through specific,

measurable goals, progress tracking, and regular

performance reviews instead of hours worked and

conducting regular assessments.

This study offers practical recommendations for

improving JS, OC, and OCB and preventing QQ in

the workplace. The study identified the lowest value

for each factor, with JS being linked to below-

market salaries. Companies should consider

competitive benefits packages, performance-based

bonuses, open communication about compensation,

and regular salary adjustments to tackle this issue.

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For OC, companies should provide clear

expectations, growth opportunities, recognition, and

rewards and foster a sense of belonging. To promote

OCB, companies should encourage employee

involvement, cultivate a positive organizational

culture, offer training, and recognize high OCB

performers. To prevent QQ, companies should

establish clear policies with consequences for

excessive personal activities during work hours or

fraudulent sick leave, provide necessary resources

such as designated areas for personal activities, and

promote work-life balance through flexible

schedules, telecommuting, and paid time off.

Implementing these recommendations can enhance

employee satisfaction, commitment, and

performance, leading to a more productive and

positive workplace culture.

5.4 Limitations and Future Research

Our investigation was exclusively conducted within

the start-up industry, a factor that could influence

the generalizability and validity of our conclusions

owing to this industry's unique and fluctuating

nature. The recent wave of layoffs sweeping across

the digital start-up landscape is a significant

concern. This context could introduce external

influences, which might alter the dynamics of our

findings. Furthermore, our research methodology

employed only two independent variables and one

mediating variable, which, while shedding light on

the QQ, might offer a partial understanding of this

multifaceted phenomenon.

Despite these limitations, our study paves the

way for intriguing future research possibilities

around QQ. One such direction is extending the QQ

analysis to diverse industries, such as the

historically stable banking sector, to garner valuable

comparative insights. Subsequent studies could also

explore variables that can impact the examined

relationships, such as work-life balance,

employment flexibility, level of employee

engagement, opportunities for career progression,

perceived employee value, and feelings of

empowerment or autonomy. Incorporating these

elements into the research framework could

significantly enrich our understanding of the

intricate dynamics underlying QQ. Moreover, future

research could probe into alternative JS and OCB

dimensions that exhibit stronger QQ associations.

Exploring OC as a possible mediator also promises

valuable insights. Through a comprehensive

examination of these QQ aspects, a more thorough

understanding of the phenomenon can be achieved,

significantly contributing to the knowledge and

practices within the management field, particularly

at the individual and organizational levels.

Ultimately, by further investigating these areas, we

can construct more effective countermeasures

against QQ, contributing to healthier work

environments and driving increased organizational

performance.

6 Conclusions

In an attempt to comprehend the intricate dynamics

of JS and OC in relation to QQ, we focused our

study on the digital start-up industry in Indonesia, a

context rich with the variables mentioned above,

aiming to unlock crucial insights that would deepen

our understanding of these components in such a

specific yet impactful setting. Through our rigorous

research efforts, the findings overwhelmingly

validated the proposed hypotheses, with a single

exception being H3. It was intriguing to observe that

a surge in JS levels was associated with an

amplification of OCB while concurrently exhibiting

a downward trend in QQ; a similar pattern emerged

with increased OC levels. A striking observation

was a substantial inverse relationship between OCB

and QQ, with the former mediating in the

relationship between OC and QQ but absent in the

JS to QQ interrelation.

The ramifications of these observations are

extensive, particularly in guiding human resource

management strategies in the dynamic landscape of

digital start-ups. It underlines the essence of

fostering an enriching work culture, where

employees are provided with myriad development

opportunities, acknowledged for their contributions,

and actively encouraged to engage, bolstering OC

and OCB. Concurrently, these firms must ensure the

competitiveness of their benefits packages, maintain

transparency around compensation, offer

performance-linked bonuses, and align salaries to

market standards to boost JS. Moreover, to mitigate

QQ behaviors, it becomes imperative for these

companies to set forth clear and concise policies,

extend the required resources, and promote a

healthy work-life balance. While our study

significantly enriches the extant literature on JS,

OC, OCB, and QQ, we acknowledge its limitations,

primarily emanating from the restricted and

homogenous sample size. Consequently, we suggest

that subsequent research should endeavor to

incorporate a broader, more diverse sample, thus

allowing for the exploration of these variables

across various contexts and industries, which could

potentially yield universal findings, transcending the

realm of Indonesian digital start-ups and making a

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notable contribution to the domain of organizational

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

- Ade Suhendar was instrumental in forming the

study's conceptual framework. He meticulously

performed the necessary analyses and interpreted

the data, forming the backbone of the research

findings. His expert understanding and rigorous

approach significantly enriched the study, lending

credibility and depth to the findings, and setting a

strong foundation for future explorations in this

research area.

- Ronald Setiadi also significantly contributed to the

study's design and structure, marking his

involvement in its conception. His role extended

beyond the realms of ideation, as he generously

provided crucial resources that were vital in the

actualization of the research, thereby ensuring its

smooth execution.

- Artati Artati played a dual role in this project. On

the one hand, she was responsible for drafting the

manuscript, skillfully transcribing the findings and

observations into a comprehensive document. On

the other hand, she was actively involved in the

actual investigation, collating the data, and

managing the processes that would later form the

basis of our analysis.

- Abdul Rohman, alongside his contribution to the

authoring of the manuscript, was actively engaged

in the investigative aspects of the study. His role in

data collection, observance of processes, and

meticulous investigation added another layer of

accuracy to our work, enhancing its credibility and

validity.

Notably, all authors were involved in the final

review of the manuscript. After comprehensive and

critical readings of the document, each author

expressed their approval for the final manuscript.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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