Journal of Leadership in Organizations.
ISSN 2656-8829 (Prin.
ISSN 2656-8810 (Onlin.
Vol.
No.
JOURNAL OF LEADERSHIP
IN ORGANIZATIONS
Journal homepage: https://jurnal.
id/leadership Understanding Employee Acceptance of Algorithmic Leadership in Indonesia: Insights from Institutional Logic and Industry Type Yusmedi Nurfaizal 1*.
Agapito Barros 2 1 Universitas Amikom Purwokerto.
Indonesia 2 Universidade da Paz (UNPAZ).
Dili.
Timor Leste Timor-Leste ARTICLE INFO Keywords:
Ethical leadership, higher education institutions, academic freedom, academic integrity.
Zimbabwe.
Article History:
Received 2024-01-12
Received in revised form
2024-05-22
Received in revised form
2024-06-22
Accepted 2024-07-15 DOI: 10.
22146/jlo.
* Corresponding Author at Universitas Amikom Purwokerto E-mail address:
faizal@amikompurwokerto.
ABSTRACT
Introduction/Main Objectives: This study examines the impact of ethical decision-making by university leadership on academic freedom and institutional integrity in two Zimbabwean universities.
Background/Problems: Empirical evidence on how ethical leadership in Zimbabwean higher education institutions manages the tension between academic freedom and institutional reputation remains limited.
These challenges are intensified by limited resources, fragile governance frameworks, and significant government interference, which constrain institutional autonomy and complicate leadership decision-making.
Novelty: This study introduces ethical leadership as a key analytical perspective for understanding the persistent yet understudied tension between academic freedom and institutional reputation in higher education Research Methods: A mixed-methods approach was used to explore ethical leadership in balancing academic freedom and institutional reputation.
Qualitative data were collected through online interviews conducted via Zoom and Microsoft Teams with eight university leaders and twenty academics .
our leaders and ten academics from each institutio.
In addition, a semi-structured questionnaire distributed through Google Forms gathered responses from 167 academics across the two universities.
Finding/Results: The findings reveal substantial government interference affecting institutional decision-making, particularly in state universities.
Foreign researchers working in Zimbabwean universities face greater restrictions than citizens and permanent University leadership often prioritizes institutional reputation over academic freedom, limiting open communication and critical inquiry.
The private university involved in the study also lacks formal policies protecting academic freedom.
Conclusion:
The study highlights the importance of ethical leadership and recommends clear ethical frameworks and leadership development programs to safeguard academic freedom while maintaining institutional integrity.
This is an open access article under the CC BY license .
ttp://creativecommons.
org/licenses/by/4.
0/).
Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
Introduction employee skepticism, particularly in regions with strong relational values such as Central Java, where trust, moral reasoning, and human centric leadership remain central to organizational life (Silitonga & Isbah, 2023.
Synchez et al.
, 2.
This disconnects between technological advancement and human readiness presents a critical leadership dilemma in emerging economies.
In the current era of digital transformation, organizations are increasingly adopting Artificial Intelligence (AI) systems to streamline decision making processes.
Among the most significant developments is algorithmic leadership, where decisions traditionally made by human leaders are either delegated to or assisted by algorithmic In Indonesia, the use of AI driven decisionmaking tools has become increasingly visible in both public and private sectors.
For example, ride hailing platforms such as Gojek and Grab have introduced algorithmic management systems that automatically allocate tasks, monitor performance, and evaluate drivers.
While these systems improve efficiency, they have also triggered skepticism and concern among workers regarding fairness, transparency, and loss of human judgment in decision making.
Similar dynamics are observable in SMEs experimenting with AI based monitoring tools, where employees express unease about being evaluated by systems they do not fully These examples illustrate the pressing need to investigate how algorithmic leadership is perceived within IndonesiaAos socio cultural and industrial context (World Economic Forum, 2023.
McKinsey 2.
Although such systems promise efficiency, legitimacy of non-human authority persist especially in developing countries where cultural norms, interpersonal values, and institutional arrangements differ markedly from those in Western economies (Mustafa et , 2023.
Silitonga and Isbah, 2023.
Martin & Waldman, 2023.
Ossa-Cardona, 2.
In this study, the term algorithmic leadership is used as the primary conceptual construct to describe leadership authority exercised through algorithmic or AI assisted decision making systems.
While prior literature often refers to related concepts such as algorithmic management or AI based leadership, these terms are treated here as overlapping manifestations of algorithmic leadership, differing mainly in their operational scope rather than their underlying logic (Gal et al.
Martin & Waldman 2.
To ensure consistently employs the term algorithmic leadership to denote the delegation of leadership influence to algorithmic systems.
While digital leadership and algorithmic governance have gained traction in the literature, current studies tend to emphasize technical system design or efficiency metrics, rather than employee perceptions of fairness, legitimacy, and acceptability (Meijerink et al.
Gal et al.
, 2.
Furthermore, most evidence comes from developed markets, leaving theoretical and empirical gaps in communicative, and industrial factors shape algorithmic leadership acceptance in Global South contexts (Okolo et al.
, 2022.
Upreti & Gangwar, 2025.
Lena & Manuel, 2.
In Indonesia, algorithmic decision making is beginning to shape both private firms and public services.
Small and medium enterprises (SME.
, as well as local government units, are experimenting with AI enabled tools for performance evaluation, task allocation, and service optimization.
However, these innovations often generate Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
To address this gap, the current study investigates how dominant institutional logic moral vs.
market affects employeesAo perceptions of algorithmic leadership Institutional logic refers to the belief systems that guide individualsAo interpretations of authority, fairness, and appropriateness in organizational settings (Pureza & Lee, 2.
We posit that employees exposed to moral oriented logic will be less accepting of algorithmic authority than those exposed to marketoriented logic, particularly in sectors where interpersonal and ethical considerations are more salient.
applied in an emerging economy using scenario based experimental methods.
This approach allows researchers to simulate realistic organizational situations and capture causality more rigorously than survey only studies (Yeomans et al.
, 2.
The empirical setting is Kudus Regency.
Central Java, a region known for its religious cultural conservatism and industrial Data were gathered from 100 employees across the manufacturing and manipulating institutional logic and industry perceptions using validated instruments (Tost, 2011.
Pati et al.
, 2.
, the study contributes to both theory building and practical implementation of AI led leadership strategies in culturally complex Furthermore, communication transparency as a mediating variable that may explain how institutional Communication transparency the openness and clarity with which decisions and processes are shared has been shown to shape perceptions of trust, fairness, and organizational justice (Rai, 2020.
Aderibigbe et al.
, 2.
Yet its role in the context of AI led decision-making remains underexplored, particularly in collectivist societies where relational trust is central (Heaton et al.
, 2.
In sum, this study aims to examine how transparency, and industry type influence By focusing on an emerging economy, this research addresses the gap left by Western centric studies and highlights the cultural and institutional dimensions of AI The main research problem centers on how employees in Indonesia perceive the legitimacy of algorithmic leadership when communication practices, and industrial The unique contribution of this study lies in extending institutional logic theory by integrating communication transparency as a mediating mechanism and industry type as a moderating factor within a moderated mediation framework, offering both theoretical advancement and practical Adding to the conceptual contribution, industry type .
anufacturing vs.
is modeled as a moderator.
Prior studies suggest that expectations of leadership vary across industries.
manufacturing sectors tend to emphasize performance and efficiency, while service sectors prioritize empathy and ethics (Tost, 2011.
Pureza & Lee, 2.
exploring industry specific perceptions, this study offers a more nuanced understanding of how algorithmic leadership is socially constructed and evaluated.
The novelty of this research lies in its integration of institutional theory, leadership legitimacy, and communication scholarship into a cohesive moderated mediation model.
Literature Review 1 Algorithmic Leadership and Legitimacy Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
The concept of algorithmic leadership has emerged in response to increasing reliance on artificial intelligence (AI) and machine learning technologies in managerial decision Unlike traditional human centric leadership, algorithmic leadership refers to the delegation of decision authority to automated systems or algorithms that influence or even control organizational outcomes (Martin & Waldman, 2023.
Synchez et al.
, 2.
While these systems promise objectivity, speed, and data driven precision, they often raise concerns among accountability particularly when the rationale behind decisions is opaque (Gal et , 2020.
Kychling et al.
, 2.
Organizations guided by market logic may frame algorithmic leadership as a tool for performance optimization, thus employees in results-oriented industries.
Conversely, in contexts dominated by moral logic, such systems may be viewed skeptically, especially if perceived as disregarding human judgment or relational values (Liu & Moore, 2.
Understanding how dominant institutional logics shape employee perceptions is crucial for predicting and managing reactions to algorithmic leadership.
3 Dominant Institutional Logic significantly influences perceived leadership legitimacy Leadership legitimacy refers to the extent to which followers perceive the leaderAos authority as appropriate, justified, and acceptable (Tost, 2.
In the context of algorithmic leadership, legitimacy becomes a contested domain, as traditional cues of leadership such as empathy, communication, and moral judgment may be perceived as This issue is further amplified in settings with strong cultural expectations for participatory and value-based leadership, such as in Southeast Asia.
Dominant institutional logic serves as a foundational framework that guides organizational behaviors, norms, and decision-making processes.
Organizations governed by market logic prioritize efficiency, performance, and profitability, while those rooted in moral logic emphasize fairness, social responsibility, and ethical conduct (Okolo et al.
, 2.
Employees often interpret the legitimacy of leadership particularly algorithmic leadership through the lens of these institutional norms.
When algorithmic decisions are aligned with the organization's dominant logic, they are more likely to be accepted as legitimate and trustworthy (Pureza & Lee, 2020.
OssaCardona, 2.
2 Dominant Institutional Logic Institutional logic theory provides a valuable lens to examine how organizational values and practices are shaped by broader cultural and societal expectations (Okolo et al.
, 2.
Two dominant forms of institutional logic are often contrasted: market logic, which prioritizes efficiency, profitability, and competitiveness, and moral logic, which emphasizes ethical considerations, social values, and collective welfare (Pureza & Lee.
These logics influence how employees interpret organizational practices including the adoption of algorithmic systems.
The role of institutional logic becomes especially crucial in algorithmic contexts, where traditional markers of leadership, such as empathy or human discretion, are If the perceived rationale behind algorithmic decisions resonates with the institutional expectations of employees, the system is more likely to be judged as appropriate and valid.
Prior empirical research supports that congruence between Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
decision making logic and institutional culture enhances perceptions of authority legitimacy (Tost, 2011.
Martin & Waldman.
Therefore, it is proposed that dominant institutional logic has a significant effect on how employees perceive leadership H2: Dominant Institutional Logic positively influences communication transparency.
H1: Dominant Institutional Logic significantly influences perceived leadership legitimacy.
Effective recognized as a foundational pillar of leadership legitimacy, especially in contexts involving complex, technology-mediated decision-making.
organizational environments where artificial intelligence (AI) and algorithmic systems increasingly influence leadership processes, the clarity and openness of communication become even more crucial.
Communication transparency, defined as the degree to which organizational decisions, procedures, and rationale are openly disclosed, enables employees to form coherent perceptions of fairness, accountability, and credibility (Rai.
Murire, 2.
In algorithmic leadership settings where decision authority is partially or wholly delegated to nonhuman agents employees often experience uncertainty or skepticism regarding the source and ethics of decisions (Ateeq et al.
Transparent communication mitigates this ambiguity by clarifying how algorithms function and how decisions are made, which in turn enhances perceived legitimacy of leadership structures (Patnaik & Bakkar, 5 Dominant Communication Transparency significantly influences perceived leadership 4 Dominant Institutional Logic positively influences communication Institutional logic not only shapes organizational strategies but also affects how information is communicated within the Market based logic, for instance, tends to prioritize transparency performance indicators, while moral logic often focuses on ethical clarity, inclusion, and justifications behind decisions (Kornberger & Leixnering, 2.
These differing logics are reflected in how organizations communicate algorithmic decisions to their members.
Transparency, in this regard, becomes a tool to uphold institutional identity and legitimize processes, especially in technology driven contexts (Meijerink et al.
, 2.
In algorithmic leadership, the clarity with which decision-making mechanisms are explained becomes a reflection of the organizationAos Transparent employees understand the rationale, purpose, and fairness of algorithmic processes, thereby reinforcing the values embedded in either market or moral logic (Rai, 2020.
Kychling et al.
, 2.
Research indicates that when communication practices are congruent with the underlying institutional logic, organizational credibility is enhanced (Etikan, 2023.
Cheong, 2.
Therefore, we hypothesize the following:
Recent empirical research supports the notion that communication transparency acts as a psychological mechanism through which organizations can build or restore trust, particularly under conditions of automation and digital transformation (Martin and Waldman 2023.
Yang.
Jiang, and Yang 2.
Employees who perceive their leaders or algorithmic systems to be transparent are more likely to attribute moral authority and fairness to those systems.
Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
increasing their willingness to accept and comply with decisions.
This is especially salient in collectivist or high context cultures, where relational dynamics and the perceived sincerity of communication carry significant weight in legitimacy judgments (Scheuer & Thaler 2024.
Yeomans et al.
, 2.
Therefore, this study posits the following hypothesis:
(Kellogg et al.
, 2.
Accordingly, the following hypothesis is proposed:
H4: Communication Transparency mediates the relationship between Dominant Institutional Logic and Perceived Leadership Legitimacy 7 Industry Type moderates the relationship between Dominant Institutional Logic and Perceived Leadership Legitimacy H3: Dominant Communication Transparency significantly influences perceived leadership The acceptance of algorithmic leadership may vary across industries, particularly due to differing expectations about how leadership should function.
Manufacturing sectors, for example, are more accustomed to performance metrics and automated decision making, aligning well with market logic.
contrast, service-oriented sectors often emphasize human interaction and empathy, values that are typically grounded in moral logic (Pureza & Lee, 2020.
Yeomans et al.
These contextual differences can moderate how institutional logic influences the perceived legitimacy of algorithmic 6 Communication Transparency mediates the relationship between Dominant Institutional Logic and Perceived Leadership Legitimacy Communication transparency has been consistently identified as a key determinant of employee trust and perceived fairness within organizations.
In the context of algorithmic leadership, where the decisionmaking process may appear opaque or becomes even more critical.
Employees are more likely to accept and support algorithmic decisions when they are clearly informed about how and why such decisions are made (Rai, 2020.
Murire, 2.
This transparency provides cognitive and ethical clarity, bridging the gap between impersonal systems and human expectations.
When the prevailing logic of an organization aligns with the dominant values of its industry, employees are more likely to accept and support leadership decisions, including those made by AI or algorithms.
This alignment creates what is referred to as "institutional fit," enhancing legitimacy perceptions (Martin and Waldman 2023.
Patnaik & Bakkar 2.
Conversely, a mismatch between industry expectations and institutional logic may generate skepticism or resistance.
Based on this reasoning, the following hypothesis is Furthermore, the mediating effect of transparency is especially pronounced when institutional logic is considered.
When organization's underlying logic whether market or moral-driven they reinforce perceived alignment and coherence.
This, in turn, enhances the legitimacy attributed to leadership practices, including algorithmic (Gal et al.
, 2020.
Scheuer & Thaler 2.
Prior findings suggest that without adequate well-intentioned systems may be met with skepticism H5: Industry Type moderates the relationship between Dominant Institutional Logic and Perceived Leadership Legitimacy.
Building on the reviewed literature, this study addresses the research problem of how Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
transparency, and industry type jointly shape employeesAo perceptions of algorithmic leadership legitimacy in Indonesia.
While prior studies on algorithmic management have primarily centered on technical efficiency in Western contexts, limited acceptance and legitimacy in emerging To fill this gap, the present study contributes by extending institutional logic communication transparency as a mediating mechanism and industry type as a moderating factor.
This approach advances theoretical understanding while also providing practical insights for organizations in culturally diverse and institutionally complex settings.
either market oriented .
, performance based, target drive.
or moral oriented .
fairness focused, ethically guide.
The industry type was varied to simulate manufacturing or service sector conditions, using language and examples familiar to the local context of Kudus Regency.
Central Java.
2 Sample and Data Collection The population for this study consisted of employees working in manufacturing and service industries in Kudus Regency.
Indonesia a region characterized by a dual economic base that includes both large scale manufacturing .
, tobacco and food processin.
and a growing service sector .
, healthcare, education, and public A purposive sampling technique was used to ensure representation from both sectors.
The minimum sample size was determined using the rule of 10 times the maximum number of arrows pointing at a construct in the model, resulting in a target sample of at least 40 respondents (Hair et al.
Method.
Data, and Analysis 1 Research Design This study employed a scenario based experimental design to examine how dominant institutional logic .
arket vs.
influences employeesAo perceptions of algorithmic leadership legitimacy, with communication transparency as a mediator and industry type .
anufacturing vs.
as a moderator.
Scenario based experiments are particularly well suited for leadership research involving hypothetical constructs such as AI based decision making because they allow controlled manipulation of contextual variables (Yeomans et al.
, 2021.
Aguinis et al.
, 2.
Participants were randomly assigned to read one of four experimental scenarios, constructed using a 2y2 factorial design combining two types of institutional logic and two types of industry Data were collected through offline distribution of printed questionnaires and online via Google Forms to reach broader participant groups.
Participants were screened to ensure they had worked in their respective industries for a minimum of one year and had experience with digital or algorithmic decision support systems.
Ethical clearance was obtained from the institutional ethics committee, and informed consent was secured from all respondents prior to participation.
Kudus Regency was deliberately selected as the research site because it reflects IndonesiaAos industrial diversity, combining traditional manufacturing with a growing service economy, and because of its strong religious cultural values that make it an appropriate setting to explore how institutional logics shape perceptions of Each scenario described a fictional organizational environment in which decisions were made algorithmically.
The texts were carefully constructed to reflect Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
algorithmic leadership.
While the sample size of 100 respondents fulfills the requirements for PLS-SEM, the focus on a single region may limit external validity and generalizability to other contexts.
To address heterogeneity by including employees from multiple industries and organizational Furthermore, the use of both offline and online surveys, while broadening respondent access, may introduce potential biases such as response style differences or social desirability effects.
These risks were emphasizing that there were no right or wrong answers, and clarifying that responses would remain confidential.
chosen for its ability to handle complex moderation, and for its robustness to nonnormal data distribution (Hair et al.
, 2.
The analysis proceeded in two stages: first, the measurement model was evaluated for reliability and validity (CronbachAos alpha, composite reliability.
AVE, discriminant second, the structural model was tested using bootstrapping .
,000 resample.
to assess path significance, indirect effects, and interaction terms.
To test the moderated hypothesis, the two-stage approach was employed as recommended in the PLS-SEM literature (Hair et al.
, 2.
Dummy variables were created for industry type and interaction terms were included to assess conditional indirect effects.
3 Measures Result and Discussion Dominant Institutional Logic manipulation .
arket vs.
, adapted from (Pureza & Lee, 2.
Participants were randomly exposed to one of the two logic Communication Transparency was measured using a 5 item Likert scale adapted from Rawlins and Rawlins, .
, with items such as AuThe organization clearly explains how algorithmic decisions are Ay CronbachAos alpha in pretesting Leadership Legitimacy was assessed using 5 items adapted from (Tost, 2.
, capturing employeesAo belief in the appropriateness and fairness of algorithmic Industry Type .
was also manipulated via scenario context .
anufacturing vs.
All Likert scale items used a 5-point scale ranging from 1 (AuStrongly DisagreeA.
to 5 (AuStrongly AgreeA.
1 Respondent Description A total of 167 responses were obtained from 212 academics and eight universities leadership yielding a 79% response rate, which is deemed satisfactory for online The high response rate underscores the importance of the study's topic.
The high response rate and diverse participation enhanced the credibility and significance of Eight senior management and 20 academics drawn from the two universities were interviewed via Zoom and Teams.
This study involved 100 full time employees from various sectors within Kudus Regency.
Central Java.
Indonesia.
Participants were selected using purposive sampling, targeting individuals who had at least one year of organizational experience and possessed sufficient familiarity with technology driven work environments.
The sample included respondents from both manufacturing and service industries, ensuring balanced manipulation of industry context.
4 Data Analysis Data were analyzed using Partial Least Squares Structural Equation Modeling (PLSSEM) with SmartPLS 4.
This method was Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
Of the total participants, 52% were male and 48% were female, with ages ranging from 22 to 48 years (M = 32.
SD = 6.
In terms of educational background, 68% held a bachelor's degree, 25% held a diploma, and 7% had completed postgraduate education.
Job roles varied across functional domains such as production, administration, finance, customer service, and human resources.
CommunicationT Participants were randomly assigned to one of four experimental conditions in a 2y2 factorial design: .
market logic in manufacturing context, .
market logic in service context, .
moral logic in manufacturing context, and .
moral logic in service context.
This design ensured an even distribution of respondents across scenarios, with approximately 25 individuals in each Such stratification enabled the systematic testing of interaction effects between institutional logic and industry type on perceived leadership legitimacy.
CommunicationT CommunicationT CommunicationT LeadershipLegiti LeadershipLegiti LeadershipLegiti Source: Data processed .
The evaluation of the measurement model was conducted to ensure the reliability and validity of the constructs prior to testing the structural relationships.
This involved assessing indicator reliability, internal consistency reliability, convergent validity, discriminant validity, following established criteria from recent PLS-SEM literature (Sarstedt et al.
, 2021.
Hair et al.
First, all indicator loadings for both Communication Transparency Leadership Legitimacy exceeded the minimum threshold of 0.
757, confirming that the individual indicators were strongly reflective of their respective latent constructs.
This result supports the assumption of indicator reliability and suggests minimal measurement error.
Furthermore, internal consistency reliability was confirmed through Composite Reliability (CR) values.
Table 1.
Outer Loadings of Measurement Items LeadershipLegiti 2 Measurement Model Evaluation CommunicationT LeadershipLegiti The sample distribution also aligns with best practices in scenario based experimental research, where internal validity is enhanced through random assignment and controlled (Clark-Carter.
The relatively high level of education and digital familiarity among participants also supports the reliability of their judgments concerning AI driven leadership Indicator Communication Leadership Transparency Legitimacy Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
which reached 0.
914 for Communication Transparency and 0.
964 for Leadership Legitimacy.
These scores are well above the recommended threshold of 0.
70, indicating strong internal consistency across items within each construct (Fornell & Larcker, structural model testing.
This provides a robust foundation for interpreting the relationships among institutional logic, communication transparency, industry type, and perceived leadership legitimacy in the context of algorithmic leadership.
Table 2.
Convergent Validity and Reliability To assess convergent validity, the Average Variance Extracted (AVE) was computed for each latent variable.
The AVE for Communication Transparency was 0.
while that for Leadership Legitimacy was 841 both exceeding the benchmark of 0.
ee Table .
, thus confirming that a substantial portion of variance in the indicators is explained by the latent construct (Henseler et al.
, 2.
Discriminant validity was also verified using the FornellAeLarcker criterion, where the square root of AVE for each construct was higher than the correlation with any other construct in the Additionally, the Heterotrait Monotrait Ratio (HTMT) of correlations was further validating distinctiveness of each latent variable (Henseler et al.
, 2.
Metrics Construct
Communication Transparency Leadership
Legitimacy Composite Reliability (CR) AVE
0,914
0,681
0,964
0,841
Source: Data processed .
3 Structural Model Evaluation The structural model was assessed to examine the strength and significance of the This evaluation involved analyzing the coefficient of determination (RA), predictive relevance (QA), and the significance of path coefficients using bootstrapping with 5,000 resamples, as recommended in PLS-SEM literature (Hair et , 2.
Overall, these findings confirm that the measurement model exhibits adequate psychometric properties, ensuring the constructs are both reliable and valid for Figure 2.
SmartPLS Data Processing Results Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
The model demonstrated strong explanatory The coefficient of determination (RA) for Communication Transparency was 0.
suggesting that Institutional Logic explained more than half of the variance in the mediator variable.
Similarly, the RA value for Leadership Legitimacy was 0.
952, indicating that Institutional Logic.
Communication Transparency.
Industry Type, and their interaction accounted for over 95,2% of the variance in employees' perception of algorithmic leadership legitimacy.
These values exceed the minimum thresholds for moderate to substantial explanatory power in social science research (Correll et al.
, 2.
and hypothesis testing outcomes.
All five proposed hypotheses were statistically supported, with p values below the conventional threshold of 0.
05, indicating that the relationships specified in the model are empirically significant.
The direct effect of Institutional Logic on Leadership Legitimacy (H.
is strong ( = p < 0.
, as is the effect of Institutional Logic on Communication Transparency (H.
( = 1.
p < 0.
These results indicate that institutional environments substantially shape both communicative practices and legitimacy The effect of Communication Transparency on Leadership Legitimacy (H.
is also significant ( = 0.
p < 0.
confirming that transparent communication enhances perceived legitimacy of algorithmic Furthermore, the mediation effect (H.
is supported ( = 0.
p < 0.
showing that communication transparency serves as a critical pathway through which institutional logic influences leadership Finally, the interaction effect between Industry Type and Institutional Predictive relevance (QA) was assessed using the blindfolding procedure.
The QA value for Communication Transparency was 0.
038 and for Leadership Legitimacy was 0.
As both values are greater than zero, the model has acceptable predictive relevance, suggesting it can effectively predict out-of-sample data (Hair et al.
, 2.
Table 3 presents the results of the structural model evaluation, including path coefficients .
riginal sample value.
, t statistics, p values.
Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
Logic (H.
is significant and negative ( = Ae p = 0.
, suggesting that the impact of institutional logic varies depending on the sectoral context.
Lee, 2020.
Gal et al.
, 2.
In environments where moral logic dominates, employees interpersonal trust, whereas market logic may legitimize leadership based on performance and efficiency.
Table 3.
Path Coefficients and Significance Path Original Sample Result Institutional Logic -> Leadership Legitimacy 1,465 0,000 Supported Institutional Logic -> Communicatio n Transparency 1,881 0,000 Supported Communicatio n Transparency -> Leadership Legitimacy 0,255 0,000 Supported Institutional Logic -> Communicatio n Transparency -> Leadership Legitimacy 0,479 0,000 Supported Industry Type x Institutional Logic -> Leadership Legitimacy -0,280 This finding aligns with institutional theory.
The theory suggests that employees evaluate authority not only through structural attributes but also through cognitive and cultural frames (Okolo et al.
, 2022.
Lena & Manuel, 2.
In the context of algorithmic leadership, where the human element is partially abstracted, the dominant logic
serves as a lens through which legitimacy is Thus, when institutional expectations are met either through ethical justifications or outcome-based validation employees are more likely to confer
0,002
Supported Institutional Logic Communication Transparency The results provide strong evidence that dominant institutional logic significantly influences how communication transparency is perceived in algorithmic leadership The results indicate a robust and highly significant effect ( = 1.
t = 84.
p < 0.
This suggests that the dominant institutional logic within an organization substantially shapes the way communication is structured and perceived.
For instance, moral logic may prioritize values such as honesty and relational clarity, while market logic may frame transparency in terms of measurable performance outcomes (Heaton et al.
, 2023.
Martin and Waldman 2.
These variations influence how openly and ethically communication is practiced, particularly when AI driven decisions are Source: Data processed .
4 Discussion 1 Institutional Logic on Leadership Legitimacy The findings demonstrate that dominant institutional logic plays a central role in employeesAo algorithmic leadership legitimacy.
The results show a very strong and statistically significant effect ( = 1.
t = 10.
, indicating that the type of institutional logic whether market oriented or moral substantially influences how employees evaluate algorithmic leadership authority.
This supports previous findings that institutional frames shape the way individuals assess leadership norms and legitimacy within organizations (Pureza & This result reinforces prior literature that associates institutional environments with Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
behavior (Ossa-Cardona, 2024.
Yang et al.
It also emphasizes that transparency is not just a technical variable, but a reflection of broader institutional expectations.
Organizations embedded in moral logic are more likely to emphasize sincerity and dialogue, while market logic may prioritize accountability and disclosure through logic to perceived leadership legitimacy.
The mediation effect was found to be statistically significant ( = 0.
t = 4.
p < 0.
indicating that communication transparency functions as an explanatory pathway through which institutional logic affects perceptions of leadership legitimacy.
The mediation effect indicates that aligning employeesAo perceptions of leadership credibility in algorithmic settings.
3 Communication Transparency on Leadership Legitimacy The results indicate that communication employeesAo perceptions of algorithmic leadership legitimacy.
The result reveals a significant and positive influence ( = 0.
t = 4.
p < 0.
, confirming that transparent communication improves how employees perceive the legitimacy of algorithmic leadership.
When organizational decisions are clearly articulated and justified especially when made by AI systems employees are more likely to interpret them as fair, competent, and trustworthy (Rawlins & Rawlins, 2008.
Rai, 2020.
Murire, 2.
These results extend prior frameworks by environment indirectly shapes leadership legitimacy by influencing how openly decisions are communicated (Yang et al.
Fu & Wang, 2.
In other words, even if institutional values are appropriate, employees may still perceive AI based leadership as illegitimate or opaque.
Thus, communication serves as both a cognitive and relational bridge.
Beyond statistical confirmation, the findings invite deeper reflection on the psychological reactions to algorithmic leadership.
One central mechanism is trust: employees are more likely to perceive algorithmic authority as legitimate when decision making processes are transparent and align with institutional norms, reducing uncertainty and fostering fairness perceptions (Scheuer & Thaler 2024.
Patnaik & Bakkar 2.
Conversely, limited transparency may exacerbate perceptions of bias or loss of control, leading to resistance.
This dynamic also connects to the literature on employees can experience stress, overload, or diminished autonomy when digital systems reduce human discretion in decision making (Sharif et al.
, 2.
The mediation effect of communication transparency observed in This finding aligns with a growing body of research suggesting that communication transparency is a key mechanism in establishing psychological safety and moral authority in technologically mediated workspaces (Yeomans et al.
, 2021.
Aguinis et , 2.
Particularly in cultures where interpersonal clarity and trust are valued, transparent communication is not just operational, it is symbolic of ethical 4 Mediation The findings confirm that communication transparency serves as a key mediating mechanism linking dominant institutional Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
this study suggests that clear explanation of algorithmic processes can act as a buffer against such technostress, transforming perceptions from threat to opportunity.
confirms that organizational sector norms such as expectations for human interaction in services versus technical optimization in appropriateness of algorithmic leadership.
Cultural implications also merit attention.
collectivist and relationally oriented societies such as Indonesia, legitimacy is not judged solely on efficiency but also on moral (Silitonga and Isbah 2023.
Upreti & Gangwar.
This may explain why moral logic had a strong effect on legitimacy perceptions, and why transparent communication was critical in shaping acceptance.
In contrast, studies in Western autonomy, suggesting that algorithmic leadership may be interpreted differently across cultures.
Future cross-cultural comparisons could therefore deepen understanding of how institutional logics interact with national values to shape the acceptance of AI driven leadership.
Beyond summarizing statistical outcomes, the findings also shed light on why employee perceptions of algorithmic leadership may differ between Western and Indonesian In collectivist cultures such as Indonesia, legitimacy is not merely a function of procedural fairness but is also deeply embedded in relational trust, moral reasoning, and communal values (Silitonga & Isbah, 2023.
Upreti & Gangwar, 2.
This contrasts with Western contexts, where employees often emphasize individual autonomy and contractual fairness as the basis for legitimacy judgments.
These cultural underpinnings help explain why moral logic exerted a strong influence in this study and why transparent communication emerged as a crucial mediator in shaping acceptance of AI driven leadership.
5 Moderation From a practical perspective, the results offer Indonesian organizations adopting AI based systems.
Leaders and managers should recognize that algorithmic tools, while efficient, cannot replace the relational and moral dimensions of leadership that employees value.
enhance acceptance, organizations should embed algorithmic decision making with provide channels for employee feedback, and adapt deployment strategies according to industry specific expectations.
For example, manufacturing firms may emphasize efficiency gains supported by transparent reporting, while service organizations should foreground empathy, fairness, and participatory decision making.
These practices not only strengthen legitimacy but also mitigate resistance, ensuring that digital The results reveal that industry type significantly moderates the relationship between dominant institutional logic and perceived leadership legitimacy.
The result is significant ( = Ae0.
t = 3.
p = 0.
indicating that the strength and direction of logicAelegitimacy relationship vary across industry types.
Specifically, in manufacturing contexts where efficiency and automation are often algorithmic leadership more readily than in service-oriented sectors, which emphasize empathy and relational leadership (Tost Pureza & Lee 2.
This interaction effect suggests that institutional logic alone is insufficient to explain legitimacy evaluations without considering the work context in which it The moderating role of industry Nurfaizal & Barros Journal of Leadership in Organizations Vol.
No.
transformation efforts are sustainable in culturally diverse environments.
research underscores the importance for organizations to align algorithmic decision making with prevailing institutional norms and to embed transparent, ethically grounded communication strategies in order to foster trust and legitimacy among Despite these contributions, the study has certain limitations, including its scenario-based design, single region focus, and modest sample size, which may restrict Future research should therefore expand to other regions, adopt mixed experimental and field approaches, moderators such as organizational culture or religiosity to deepen understanding of algorithmic leadership acceptance across diverse settings.
It is important to note that several path coefficients in this study exceed unity and that the explained variance for leadership legitimacy reaches a high level (RA = 0.
While such values may appear unusually large in conventional survey-based studies, they are not uncommon in scenario based experimental designs, particularly when key independent variables are categorically manipulated rather than perceptually measured (Aguinis et al.
, 2023.
Hair et al.
In experimental PLS-SEM contexts, strong manipulations combined with theoretically aligned mediators may yield high coefficients without violating model assumptions or indicating model overfitting.
Moreover, the use of distinct scenario treatments helps reduce the risk of common method bias typically associated with single source self-reported data.
References