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Article

Enhancing Organizational Agility in Sustaining Indonesia’s Upstream Oil and Gas Sector: An Integrating Human-Technology-Organization Framework Perspective

by
Octaviandy Giri Putra
1,*,
Amalia Suzianti
1 and
Yassierli
2
1
Department of Industrial Engineering, Faculty of Engineering, University of Indonesia, Depok 16424, Indonesia
2
Department of Industrial Engineering and Management, Faculty of Industrial Technology, Bandung Institute of Technology, Bandung 40132, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(24), 11346; https://doi.org/10.3390/su172411346
Submission received: 11 November 2025 / Revised: 6 December 2025 / Accepted: 10 December 2025 / Published: 18 December 2025
(This article belongs to the Special Issue Innovation Management, Organizational Resilience, and Performance: Pathways to a Sustainable Future)
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Abstract

The upstream oil and gas (O&G) industry faces persistent challenges, including volatile oil prices, declining reserves, and the increasing prominence of renewable energy sources. In response, the Indonesian government has set an ambitious target to increase national O&G production by 70% by 2030. This goal requires upstream O&G producers to adopt innovative approaches that enhance performance and resilience. This study emphasizes organizational agility as a critical capability for organizations in VUCA environments to remain resilient and competitive. This study examines the influence of relevant agility enablers on Indonesian upstream O&G, ensuring that no critical factors are overlooked in the implementation of agility. The human–technology–organization (HTO) framework was used to conceptualize and examine its role in supporting organizational agility. Data were collected from 103 managerial-level respondents representing 27 producer companies representing more than 75% of Indonesia’s overall O&G production. PLS-SEM was employed to examine whether relationships existed among predictor variables and organizational agility. The results highlight HTO, leadership, and innovation capacity as significant enablers of organizational agility. This study contributes theoretically and practically by integrating the HTO framework into the agility discourse and offering a comprehensive view of agility enablers that foster transformation, resilience, and sustainability of Indonesia’s upstream O&G sector.

1. Introduction

Over the past five years, Indonesian upstream oil and gas (O&G) producers have faced significant challenges with volatile, uncertain, complex, and ambiguous (VUCA) conditions [1]. The COVID-19 pandemic, fluctuating oil prices, depletion of O&G reserves, and the growing influence of renewable energy sources have fundamentally transformed the efficient and sustainable management of the upstream O&G sector in Indonesia. In response, the Indonesian government developed a blueprint through the Ministry of Energy and Mineral Resources to increase O&G production by 70% by 2030 [2]. Achieving this ambitious target requires a substantial shift in the operational mindset of upstream O&G producers to align with the government’s strategic objectives [3].
According to several studies, adopting an agile mindset is critical to navigate the complexities of VUCA environments. Agility is widely considered the most effective approach to addressing such challenges. An organization can rapidly develop and apply flexibility, adaptability, and dynamic capabilities [4,5]. As continuous change is inevitable, agility would help organizations cope with VUCA conditions and turn them into growth opportunities [6]. Having an agile mindset would be a key factor for an organization in dealing with dynamic changes, retaining sustainability, building resilience, and maintaining competitiveness [5].
Previous research has demonstrated limitations in highlighting the importance of agility. Most studies focus on isolated variables affecting organizational agility, while others evaluate and estimate an organization’s agility maturity level. Some previous studies on organizational agility maturity developed organizational maturity frameworks [7,8,9,10] and applied organizational maturity agility across industries [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. However, there is no previous study to explore all relevant agility enablers and also relationships among agility enablers to drive organizational agility in upstream O&G. Integrating all relevant agility enablers will avoid any wrong decisions and the company’s strategies due to only focusing on a particular enabler.
This study addresses the gaps by thoroughly identifying all relevant agility enablers of the upstream O&G producers in Indonesia. Accordingly, this study is guided by following research question: “What are the relevant agility enablers for enhancing organizational agility of the upstream O&G in Indonesia?” This study contributes to advancing the perspective of agility enablers in organizational agility research. Examining the connections among all enablers reveals which enablers are crucial for attaining organizational agility. The results of the study will offer new and valuable insights for decision-makers and the Ministry of Energy and Mineral Resources to define practical strategies and policies that foster transformation, resilience, and sustainability of Indonesia’s upstream O&G sector to overcome the challenging conditions faced by Indonesian O&G producers and meet the nation’s production targets.

2. Theoretical Background

2.1. Organizational Agility and Agility Enablers

Organizational agility refers to an organization’s ability to be flexible, adaptable, and dynamic [28]. Walter [29] defined organizational agility by developing a framework that provides a structural conceptualization of organizational agility based on previous organizational agility studies. Walter [29] identified four critical organizational agility categories. Agility within an organization is initiated by factors known as agility drivers, and its development is facilitated by factors known as agility enablers; both categories collectively contribute to the creation of agility capabilities, which are quantified by agility dimensions [29]. These categories are interconnected and collectively affect an organization’s agility. Agility drivers and enablers directly influence agility capabilities and shape agility dimensions. Ultimately, all these elements contribute to the organization’s overall agility.
Agility drivers play an important role in influencing agility capabilities. Walter [29] argues that agility drivers make organizations vulnerable and require them to seek a competitive advantage. However, the drivers cannot always be controlled, especially when they come from an external organization. Agility drivers from an internal organization are usually the result of management decisions that set the organization’s direction [29]. Every change in agility drivers necessitates agility capability adjustment [29]. Agility drivers define an organization’s agility capabilities needs. Agility capabilities are essential abilities that must be addressed to enable organizations to take advantage of positive changes [29]. Agility enablers also build capabilities [10,29]. An agility enabler is an organization’s noticeable response that leads it to become agile [29]. The main point is that an organization can internally manage and control an agility enabler. If the organization wants to be agile, it must respond by preparing agility enablers.
Previous studies have identified that agility enablers encompass various factors that affect organizational agility. This research area is highly prominent in organizational agility studies. The concept of organizational agility can be associated with several factors believed to enable it. Previous research has identified a diverse array of enablers, including information systems [16,17,18,19,20], knowledge management [21], human factors [22,23], technology [18], digitalization [24], knowledge transfer [25], business processes [26], and marketing sensitivity [27]. Most studies do not specifically categorize particular factors as agility enablers. According to Walter’s definition of agility enabler [29], each study’s results can be categorized as agility enablers.
Various industries have identified several agility enablers that positively influence organizational agility. These enablers include leadership [24,30], human–technology–organization relationship (HTO) [31,32,33], knowledge management [21], innovation capacity [18], and IT competence [18]. These factors play crucial roles in fostering and supporting organizational agility. Leadership, HTO, knowledge management, innovation capacity, and IT competence are enablers known to enhance organizational agility across industries. However, each industry has its own unique characteristics, so the study in the upstream industry may yield different results. The interaction between relevant agility enablers must also be considered to obtain a more robust and representative model. Previous studies identified relationships between leadership and HTO [33,34], knowledge management and HTO [35,36], knowledge management and innovation capacity [37,38], and innovation capacity and IT competence [39,40]. By identifying these factors, organizations can better navigate and thrive in VUCA conditions, enabling them to quickly adapt, seize opportunities, and remain competitive. Therefore, this study’s objectives are (1) to examine the relevant agility enablers that affect organizational agility, and (2) to examine the relationship among particular agility enablers that affect organizational agility in Indonesia’s upstream O&G producers.

2.2. Proposed Framework

This section elaborates on the detailed studies built for the proposed framework. This study develops an exploratory framework based on previous studies. This study used Ravichandran’s framework [18] as the base framework. Ravichandran [18] defined IT competence and innovation capacity as agility enablers that accelerate organizational agility. The framework is selected because it is built on data from across industries, so the model is generally relevant to any industry. Moreover, IT is one of the agility enablers often identified in organizational agility research [19]. While innovation capacity is a related enabler with a strong relationship to the development of IT-related products [39,40].
Previous studies have identified many agility enablers that influence organizational agility across industries. This study specifically explores the human–technology–organization (HTO) relationship, drawing on Neuman et al. [23] and Karltun et al. [32,33]. Considering HTO as an agility enabler, it emerges from several challenges and obstacles in implementing the agility concept in industry. Rahim et al. [41] noted that agility can create problems in several forms, including bureaucratic processes, resistance to change, constraints on work culture, ineffective communication, and limited resources. Kalaignanam et al. [42] identified challenges and obstacles that need to be mitigated in the implementation of agility, such as the need for leadership commitment to implementing and supervising it, and for sufficient employee capabilities. This study will then examine the challenges and barriers related to human, technological, and organizational factors. The main advantage of the HTO concept is the equal positioning of the three components: humans, technology, and organizations [32].
To confirm all the relevant agility enablers for organizational agility in this study, a focus group discussion (FGD) was conducted with Indonesian upstream O&G industry professionals from various Indonesian O&G producers with relevant competencies in operations, business, and organizational management. The FGD was conducted in a forum that outlined literature on supporting factors for organizational agility. The FGD results focused on agility enablers that influence upstream O&G organizational agility, which included HTO, leadership, knowledge management, innovation capacity, and IT competence. Ravichandran’s framework then integrates with other frameworks from Neuman et al. [23] and Karltun et al. [32,33] discussing HTO, Gagel discussing leadership [30], and Navarro et al. discussing knowledge management [21].

2.3. Hypothesis Development

Figure 1 shows the proposed framework and nine hypotheses, while Table 1 lists the indicators of each factor and each construct.

2.3.1. Human–Technology–Organization (HTO)

Human–technology–organization (HTO) describes the relationship between the human, technology, and organization subsystems. HTO is a framework developed to elaborate on work activities by explaining the interactions between subsystem [31,32,33]. This study identified HTO as a supporting factor for agility owing to the emergence of several challenges and obstacles in implementing of the agility concept [41]. Implementing agility is challenging due to various barriers, including bureaucratic processes, ineffective communication, work culture, resistance to change, and limited resources [41].
Moreover, Kalaignanam et al. [42] elaborated on the challenges and obstacles that agility implementation must address. The agility concept cannot be implemented without leadership commitment and the development of employee competencies. Several other studies have confirmed similar challenges and obstacles in the organizational agility implementation. Nerur et al. [43] elaborate on organizational agility challenges across four categories: management and organization, people, process, and technology. Hutter et al. [44] explain that an organization should enhance its capabilities, manage resources effectively, and prepare thoroughly before implementing the agility concept. Moreover, Ahlback et al. defined difficulties applying the agility concept as not having clear objectives, applying agility partially, incompetent workers, implementing only in a short period, lack of productivity while starting agile, lack of leaders’ commitment, high investment cost of people’s development and technology, and disruption of current organizational stability [45].
The similarities between these challenges and obstacles related to human, technological, and organizational factors that were not identified or mitigated during the initial stage of technology implementation. This study addresses this problem by proposing HTO variables as a different approach. The main advantage of the HTO approach is the equal positioning of the three subsystems: humans, technology, and organizations [32]. The human factor is often not placed on the same level as other factors and is treated as a complementary factor in system analysis. These circumstances can lead to the inappropriate analysis of problems and alternative solutions [32,33]. Thus, this study suggests the following hypothesis:
H1: 
There is a positive relationship between HTO and organizational agility.

2.3.2. Leadership

Leadership is a critical factor in the implementation of agility. Gagel [30] distinguishes three types of leadership: transformational, transactional, and passive-avoidance. The three leadership types are conceptualized as constructs that impact an organization’s agility. Several previous studies have defined these three types of leadership. Gagel’s research [30] was conducted across 126 business units from 47 corporate organizations from various industries in America with the three most significant respondents coming from construction companies (25%), energy companies (15%), and government agencies (11%). The rest are spread across various sectors, including universities, health, manufacturing, mining, insurance, and others. The research was conducted using confirmatory factor analysis (CFA). Only three leadership styles were significantly influenced by organizational agility. However, the study’s results sufficiently confirmed that leadership influences organizational agility and will be used as a factor explored in this study. Taqi & Talib [46] also confirmed that leadership is a crucial factor in the O&G industry. In line with these previous studies, this study hypothesizes leadership as a factor that impacts organizational agility with the following hypothesis:
H2: 
There is a positive relationship between leadership and organizational agility.

2.3.3. Knowledge Management

Navarro et al. [21] identified the influence of knowledge management on organizational agility. Knowledge management is divided into three sequential process series: knowledge acquisition, knowledge conversion, and knowledge application. Three types of knowledge have an impact on organizational agility and organizational performance [21]. This study involved 112 companies in Spain from various industries. The model analysis using PLS-SEM indicated that knowledge management generally impacts organizational agility. Navarro et al. [21] found that knowledge processes mediate the creation of organizational agility. The detailed factors, constructs, and indicators in this study related to knowledge management will be developed based on several additional literature sources, as defined in Table 1. Thus, this study suggests the following hypothesis:
H3: 
There is a positive relationship between knowledge management and organizational agility.

2.3.4. Innovation Capacity and IT Competence

Ravichandran [18] investigated IT competence and innovation capacity as two key factors that support agility. The two factors supporting agility were identified through literature reviews of several previous studies. IT competence is an organization’s ability to create a digital platform, which is defined by two constructs: information system capabilities and IT investment. Innovation capacity is defined as the flexibility of the IT infrastructure and the platform applications adopted by the company [18]. The study was conducted with study objects in 129 large companies in America across various industries, with 65% of manufacturing companies, 17.8% of financial services, banking, and insurance companies, 6.2% of retail companies, 7.8% of transportation companies, and 3.2% of other companies. The analysis in this study was performed using PLS-SEM.
Previous studies examining IT competence and innovation capacity individually have also validated the effect of each factor on organizational agility. Ridwandono and Subriadi [19] conducted a literature review on IT aspects and organizational agility. The research provides a comprehensive overview of IT aspects and related environmental factors impacting organizational agility. Ridwandono and Subriadi [19] found that IT on organizational agility can be divided into IT competence (similar to this study), IT resources, and IT capability. Ridwandono and Subriadi [19] defined IT competence as an organization’s ability to utilize IT resources and processes, including digital platform creation.
In this study, innovation capacity as an agility enabler is also developed, as referred to Arsawan et al.’s research [11]. Innovation capacity elaborates an organization’s ability to introduce new ideas that improve its capacity to identify new market environments and products for business growth [11]. Innovation is reflected in the opportunity to develop new technology to improve an organization’s performance [47]. Research indicates that IT competence and innovation capacity have a positive impact on an organization’s speed of response. Therefore, the following hypotheses are proposed:
H4: 
There is a positive relationship between innovation capacity and organizational agility.
and
H5: 
There is a positive relationship between IT competence and organizational agility.

2.3.5. Interaction Between Agility Enablers

Five key agility enablers were identified to be explored in this study: human–technology–organization (HTO) [32,33], leadership [30,46], knowledge management [21], innovation capacity [11,18,47], and IT competence [18,19]. Moreover, the literature also showed relationships among these agility enablers, which could affect organizational agility. However, this study may not discuss all the relationships between these agility enablers. Examining the interactions among agility enablers is necessary to gain a broader perspective and identify a priority enabler to affect the organization’s agility. Some relationships between the agility enablers that were identified are as follows.
  • Relationship between leadership and human–technology–organization (HTO).
  • Relationship between knowledge management and HTO.
  • Relationship between knowledge management and innovation capacity.
  • Relationship between innovation capacity and IT competence.
The relationship between leadership and human–technology–organization (HTO) was identified based on previous studies [33,34]. Hwang et al. [34] investigated the effect of Leader–Member Exchange Theory (LMX) on the relationship between leadership and the technology adoption process. Hwang et al. [34] demonstrated that leadership is critical to ensuring that all technologies designed to ease work processes are appropriately implemented. Moreover, leadership significantly influences organizational culture, especially when adopting new technologies. Leadership plays a significant role in driving organizational change and encouraging employees to embrace new circumstances [48]. Kartlun et al. [33] elaborated that implementation issues arise when no comprehensive analysis is conducted, especially regarding human factors. Karltun et al. [33] also reported that leadership’s commitment to implementing technological changes constrained technology implementation. These previous studies then became the basis for determining the relationship between leadership and HTO. Consequently, this study suggests the following hypothesis:
H6: 
There is a positive relationship between leadership and HTO, which impacts organizational agility.
The initial relationship between knowledge management and HTO in this study was identified based on Ibrahim and Cln’s study [36]. Ibrahim and Cln [36] focused on implementing a knowledge management system that encompasses knowledge acquisition, conversion, and documentation. The study also found that several factors lead to the failure of a knowledge management system, such as inadequate technical support, an excessive reliance on technology to resolve issues, a lack of understanding of the organization’s needs, and a lack of comprehension of the system’s purpose. This study addresses these kinds of problems by discussing the importance of human readiness and organizational structure in implementing a knowledge system. Without the right competence, a built system cannot be implemented. Darmawan et al. [35] stated that knowledge management requires greater attention because it is a key factor influencing human factors in the execution of work processes. The results of several studies then became the basis for determining the relationship between knowledge management and HTO. Therefore, this study proposes the following hypothesis:
H7: 
There is a positive relationship between knowledge management and HTO that impacts organizational agility.
Mardani’s study [37] captured the relationship between knowledge management and innovation capacity. This study examined the relationship between knowledge management, innovation speed, and innovation quality and quantity. The study involving 120 companies in Iran found that knowledge management significantly influences innovation speed, quality, and quantity. Mardani et al. [37] addressed that knowledge management is an instrument for a company to boost the innovation process and achieve performance. Simanaviciene et al. [38] also conducted a similar study to examine the impact of knowledge management on the innovation process. This study included more than 300 respondents from various professions in Lithuania. The results showed that the ability to manage and adapt information in an organization positively impacts the innovation process. Several studies then yielded results that formed the basis for determining the relationship between knowledge management and innovation capacity. Consequently, this study proposes the following hypothesis:
H8: 
There is a positive relationship between knowledge management and innovation capacity that impacts organizational agility.
Meanwhile, the relationship between innovation capacity and IT competence was identified based on research by Cuevas-Vargas et al. [39] and Neziraj & Shaqiri [40]. Cuevas-Vargas et al. [39] examined the acquisition process of information and communication technology towards innovation. This study was conducted on manufacturing MSMEs in Mexico and involved more than 230 business owners. The study found that the implementation of information and communication technology affected the innovation process of the organization and the performance of MSMEs. Simultaneously, Neziraj & Shaqiri’s study [40] conducted a similar research to see the relationship between innovation and information and communication technology. The study involves the software and non-software industries in Kosovo, resulting that innovation capacity and information and communication technology can influence each other. Neziraj & Shaqiri [40] found that the innovation process toward technology positively impacted business performance in Kosovo. These results demonstrate the role of the innovation process in triggering technological development. Several of these studies then became the basis for determining the relationship between innovation capacity and IT competence in this study. Consequently, this study suggests the following hypothesis:
H9: 
There is a positive relationship between innovation capacity and IT competence that impacts organizational agility.
Table 1. Factors, constructs and indicators of the conceptual framework.
Table 1. Factors, constructs and indicators of the conceptual framework.
NoFactorsReferencesConstructsIndicator Codes (No of Question)References
1IT Competence[18]Digital platform capability (DPC)DPC1-4 (4)[18]
IT Infrastructure (ITI)ITI1-4 (4)[49,50]
Data Analytics Capabilities (DAC)DAC1-4 (4)[51]
2Innovation capacity[18]Innovation opportunity (IO)IO1-4 (4)[47]
Knowledge creation (KC)KC1-3 (3)[11]
Resources allocation (RA)RA1-4 (4)[52]
3Human–Technology–Organization (HTO) Relationship[31,32,33]Human skills & capabilities (HSC)HSC1-5 (5)[23]
Technology adoption (TA)TA1-3 (3)[33,53]
Organizational structure and design (OSD)OSD1-3 (3)[21,23,54]
Change management (CM)CM1-4 (4)[55]
Performance management (PM)PM1-4 (4)[56]
4Leadership[24,30]Entrepreneurial orientation (ENT)ENT1-2 (2)[57]
Adaptability (ADA)ADA1-3 (3)[58]
Communication (COM)COM1-4 (4)[30,59]
Empowerment (EMP)EMP1-3 (3)[60]
5Knowledge Management[21]Learning culture (LC)LC1-2 (2)[61]
Knowledge accessibility (KNO)KNO1-2 (2)[62,63]
Knowledge transfer (KT)KT1-3 (3)[21,64]
6Organizational Agility[10,15,27,65]Responsiveness (RES)RES1-3 (3)[10,17]
Flexibility (FLE)FLE1-2 (2)[10,17]
Total6 Factors 20 Constructs 66 Indicators

3. Methodology

The methodology adopted in this study was designed to systematically address the study’s objectives and ensure the credibility of the findings. Figure 2 describes each step of the study’s methodology.

3.1. Participants, Procedures, and Samples

Data were collected from O&G producers in Indonesia. The operational areas of these producers are spread onshore and offshore locations throughout the Indonesian archipelago. Data collection was conducted using a self-developed instrument and distributed online to the participants. All participants provided informed consent before participation. Online consent was obtained from respondents. The participants were voluntary, and respondents were informed that they could withdraw at any time without any consequences.
The respondents were selected randomly based on the following criteria: Indonesian O&G producers’ employees with a current job position at or above the manager level. No specific competencies from a particular department were required to be the respondent. The constructs and indicators of this study are non-operational, making it crucial to select a sample of employees at the leadership level who possess business acumen and orientation, with sufficient insight, knowledge, and experience of the business and the challenges facing the Indonesian upstream O&G industry.
Several procedural remedies were implemented in accordance with the guidelines to minimize bias. Every respondent was guided and accompanied by the researcher to avoid misinterpretation while completing the questionnaire. Respondents were assured of anonymity and confidentiality, and informed that there were no right or wrong answers. The questionnaire items were written clearly and unambiguously. Each construct was separated into distinct sections of the survey to reduce pattern recognition. To complete the procedural, the collinearity VIF was also checked while performing PLS SEM using SmartPLS to avoid common method bias.
The questionnaire instrument encompassed two parts: the introduction and organizational agility assessment sections. In the introduction, respondents were informed that this data collection was anonymous and not individual data processing. The concept of the research analysis unit represents an organization, not an individual. Several questions in the introduction are on company profiling, such as production volume, ownership type, company age, type of operation, number of company employees, and respondent employee level. The organizational agility assessment section contained 66 questions as indicators used to measure each construct variable, as presented in Table 1.
Data collection was conducted for approximately 2–3 months, involving 103 employees at the department/equivalent level including strategic, tactical, and operational management-level personnel across 27 O&G producers in Indonesia, with 103 responses between Q3-2023 and Q1-2024. The 27 O&G producers represent more than 75% of Indonesia’s overall O&G production. A total of 49 respondents represented a production-size cluster of 0–100 KBOEPD, while 54 respondents represented a production-size cluster > 100 KBOEPD. Participants were informed of the study objectives, and consent was obtained before data collection. The participants’ privacy was ensured, and the paper guaranteed that all statistics obtained from the participants would be treated confidentially. To maintain the confidentiality and security of the participants, only the authors had access to the data.
The O&G producers comprise 85% national and 15% foreign-owned companies. Based on ownership, 68.9% of the respondents were from Indonesian O&G state-owned companies and subsidiaries in Indonesia, while the remaining were private companies. This circumstance represents the actual condition of O&G producers in Indonesia, where almost 70% of O&G contributors are from state-owned companies and subsidiaries. The samples also considered the operational type of O&G, where 44.7% were onshore companies, 26.2% were offshore companies, and 29.1% were both onshore and offshore companies. Table 2 presents an overview of the profiles of the companies, highlighting the relevant characteristics of the Indonesian O&G producers.

3.2. Measures

This study explored the relationship between agility enablers and organizational agility in Indonesian O&G producers, as presented in Figure 1. The study applied a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree), which was validated and developed to match the context of this study. The range of 1–7 allows respondents to provide a more detailed level of agreement for each statement. The 1–7 scale’s determination is grounded in research by Altuna and Arslan [66], which indicates that the larger the range employed in the Likert scale, the more effectively it captures the distinctions in assessments made by respondents. In addition, a Likert scale with a range of more than four provides data analysis that can be performed using a parametric approach [66]. Before the questionnaire was distributed to respondents, the content validity was determined through an FGD with O&G professionals who had relevant competencies to the research topic. The validators agreed that the prepared questionnaire was sufficient as an instrument for measuring data collection in this study.

3.3. Methodological Analysis

This study employs the partial least squares (PLS) approach to develop SEM using SmartPLS. This methodology was chosen to predict the built model and to examine the relationships among variables that address all hypotheses. The PLS approach yielded more consistent findings than the regression approach, particularly when assessing the moderating influence on the dependent variable. Based on the literature review, five factors were explored as enablers of organizational agility. This study explores multiple aspects of agility enablers and their interactions that impact organizational agility among O&G producers. This study uses PLS-SEM with an exploratory analysis approach and SmartPLS software to assess the validity and reliability of the research model and test the research hypotheses [67]. A rule of thumb for a minimum sample size has been constructed in some studies. The “10-times rule” has been popular due to its simplicity of application [68]. However, Jhantasana [69] defines the appropriate sample size for PLS-SEM based on statistical power and effect size. The smallest sample can generate at least 0.80 statistical power and an effect size of 0.15.

3.4. Homogeneity and Linearity

Because this study uses PLS-SEM to explore the relationships among variables, homogeneity is not a mandatory requirement. The primary focus of PLS-SEM is to maximize the explained variance in latent variables; therefore, the data must exhibit sufficient variability [68]. Hair [68] explained that considering heterogeneity is promising from both practical and theoretical perspectives for understanding differences between groups of respondents; it is also often necessary to obtain valid results. Linearity among variables was also investigated in this context. Hair [67] explained that PLS-SEM assumes linearity among variables. Therefore, linearity is highly recommended to obtain an accurate path coefficient in the model [67]. The scatterplot analysis showed linearity among the variables.

4. Results

4.1. Respondents and Demographic Characteristics

The unit of analysis in this study is the organization, not the individual. The demographic characteristics of the study’s respondents have been clustered into: company production size (KBOEPD), company ownership, company establishment, company number of full-time employees, and company operation type. A nonparametric statistical test was performed for each demographic characteristic to avoid potential confounding effects on the PLS-SEM model. The test results are presented in Table 3. The results confirmed that none of the demographic characteristics affected the data set for the PLS-SEM analysis.

4.2. The Assessment of the Measurement Model

The measurement model involved the assessment of inter-item reliability, convergent validity, and composite reliability. Then, the structural model was used to evaluate the predictive capability of the model and conduct a hypothesis analysis [68]. This approach ensured robust data analysis and provided meaningful insights into variables’ relationships.
Construct reliability was estimated using Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE). The results showed that CA, CR, and AVE values were above the threshold [67] as presented in Table 4. Discriminant validity was established using the Fornell–Larcker approach to verify that the square root of the AVE of each construct met the criteria. Table 4 presents the main parameters obtained from the structural assessment of the model under study. Some iterations of the analysis were conducted using PLS, then generated eight indicators to be taken out in building the model, specifically KT2, ITI3, HSC4, EMP2, COM2, CM3, CM4, and ADA2. The elimination of those indicators due to weak load on their constructs by assessing outer loadings, then considering all measurement model criteria [68].

4.3. Structural Model Testing

The structural model was developed through multicollinearity assessment and path coefficient analysis to determine whether it can describe the relationship among constructs, as hypothesized in this study. The variance inflation factor (VIF) value with a threshold value of 5.0 is used to assess collinearity among predictor constructs and the path coefficients for the model’s relationships [67], as presented in Table 5 and Table 6.
Bootstrapping was conducted to increase estimation accuracy to determine the statistical significance of the path coefficients. Table 6 presents the PLS path coefficients and the attached p-values. PLS-SEM evaluates the structural model using the coefficient of determination (R2) and the path coefficients to assess the importance of the relationships. In this study, the R2 value for organizational agility was 0.837. The value suggests that the constructed model could account for a substantial part of the variance in organizational agility, with 83.7% of organizational agility achievement attributed to the HTO and innovation capacity. This R2 value exceeds the suggested threshold level [69], indicating that the model fits well and provides substantial explanatory power for organizational agility.

4.4. Hypotheses Testing

The result supported three of the four hypotheses regarding the direct relationship with organizational agility; thus, H1, H2, and H4 were supported. H1: HTO on organizational agility (β = 0.403; p < 0.05); H2: leadership on organizational agility (β = 0.265; p < 0.05); and H4: innovation capacity on organizational agility (β = 0.274; p < 0.05). H3: Knowledge management on organizational agility and H5: IT competence with organizational agility is rejected. Meanwhile, none of four hypotheses regarding the direct relationship among the variables were supported. The complete results are presented in Table 5 and described in Figure 3.

5. Discussion

This study aims to examine all relevant agility enablers that affect organizational agility of the upstream O&G producers in Indonesia. Using PLS SEM, this study revealed that HTO, leadership, and innovation capacity significantly impact organizational agility in Indonesian upstream O&G producers. These results validated previous research mentioning leadership [24,30], HTO [31,32], and innovation capacity [18] across industries. These findings implied that basically there are two agility enablers in upstream O&G industry, namely leadership and innovation capacity. These two then need to be completed by HTO, an agility enabler that aimed to ensure organizational agility could be applied properly.
Leadership involved entrepreneurial orientation, adaptability, communication, and empowerment. Organizational agility could be implemented if the leader were a role model for workers and applied consistently [24,30]. In the upstream O&G industry, strong leadership is essential, as explained by Budiman et al. [70], who state that leadership capabilities are among several factors that enhance a project’s success rate in the upstream O&G sectors. The Indonesian Petroleum Association [71] also identified that leadership is a competency needed to achieve the production and net-zero emission targets of upstream O&G in Indonesia.
Innovation capacity involved innovation opportunity, knowledge creation, and resource allocation. Innovation capacity acknowledges the importance of proactivity and openness to new ideas and market trends [72], thereby boosting organizational agility. In the upstream O&G industry, most technology innovations are executed by third-party vendors [73]. However, the initiative is still initiated by the O&G companies. Knowledge creation focuses on an organization’s capability to generate new knowledge through research, development, and learning processes [74]. Effective resource allocation establishes that the organization has financial, human, and technological resources to drive innovation and agility. Innovation capacity contributes to an organization’s ability to respond to change by generating new ideas and knowledge while effectively utilizing resources [72]. By cultivating a supportive environment for innovation, investing in research and development, and strategically allocating resources, Indonesian O&G producers can accelerate their organizational agility and navigate the challenges and opportunities in the industry.
This study explicitly addresses HTO as an agility enabler to complete other enablers, offering a different approach. HTO involved human skills and capabilities, technology adoption, organizational structure and design, change management, and performance management. The finding revealed that implementing organizational agility is not only about focusing on a particular agility enabler, but also about preparing the necessary supporting elements for smooth implementation. A stronger focus on managing the relationships among humans, technology, and organizations could enhance an organization’s competitive advantage [75]. In the O&G industry, the findings strengthened the previous research about the importance and criticality of another factor in applying technology and digital transformation in the O&G industry, especially change management communication, empowering employees, and providing upskilling programs to enhance their competencies [76].
Contrary to what might be expected, knowledge management and IT competence did not significantly affect organizational agility. The results contradicted previous studies [18,19,21], which elaborated on the relationship between relationship knowledge management and IT competence with organizational agility. IT competence encompassed digital platform capabilities, IT infrastructure, and data analytics. Knowledge management involved learning culture, knowledge accessibility, and knowledge transfer. A possible argument is that these two enablers have already been well applied in this industry. In the upstream O&G industry, IT competence is a basic need, as all sophisticated O&G technology is supported by it. The findings were strengthened by previous research showing that O&G digitalization and applications have increased drastically over the last decade [77]. Digitalization is applied across all aspects of the O&G industry, helping the industry maximize returns [78]. Knowledge management is also considered a mandatory item that can effectively address major challenges in the O&G industry [79]. Knowledge management implementation in the O&G industry is strongly supported by embracing IT-based systems and digitalization to increase efficiency and effectiveness [79,80]. Advancements in big data and digitalization have enabled the O&G industry to manage knowledge more efficiently [80].
In relationship among agility enablers, leadership, knowledge management, and innovation capacity were not the influenced other enablers to affect organizational agility. The results were contrary with previous studies. Leadership influenced HTO to affect organizational agility [33,34,48]. Knowledge management influenced HTO to affect organizational agility [35,36]. Knowledge management influenced innovation capacity to affect organizational agility [37,38]. Innovation capacity influenced IT competence to affect organizational agility [39,40]. However, the O&G industry is a unique industry compared to other industries.
Leadership did not influence HTO to affect organizational agility, contrary to the previous studies [33,34,38]. Generally, leadership is part of employee competencies, focusing on the variables needed to be an excellent leader. A possible explanation is that leadership has a significant impact on the project success rate in the upstream O&G industry [81]. Rather than solely influencing HTO as an agility enabler, leadership is a factor that works simultaneously with HTO to create organizational agility.
Knowledge management did not influence HTO to affect organizational agility, contrary to the previous studies [35,36]. Knowledge management is a key factor that impacts human factors in achieving good organizational performance [35]. A possible explanation of the rejection is that the O&G industry has unique characteristics and requires specific knowledge management tools and strategies to be developed [82]. This circumstance generates different responses for the O&G company compared to other industries, as elaborated in previous studies.
Knowledge management did not influence innovation capacity to affect organizational agility, contrary to the previous studies [37,38]. Innovation in Indonesia’s upstream O&G sector primarily focuses on O&G production technology. Operational problems arising in upstream O&G operations mainly drive the need for innovation. A possible explanation is that knowledge management also supports the innovation process, but the O&G producer’s role is limited to confirming the problems currently facing the company and defining the field characteristics. The innovation process is primarily handled by third parties (service providers) who already possess extensive benchmarking data from various upstream O&G companies worldwide [73].
Innovation capacity did not influence IT competence to affect organizational agility, contrary to the previous studies [39,40]. IT competence in the upstream O&G sector is primarily driven by the need for operational IT support for the use of the latest upstream O&G technologies, since the O&G industry continuously innovates to implement advanced technologies [83]. Innovation in the upstream O&G sector is mainly driven by third-party providers offering all-inclusive services, from hardware and software to maintenance, including IT competence [73]. The upstream O&G producers’ role primarily focuses on ensuring that all proposed technologies can be well implemented and meet the company’s needs.

5.1. Theoretical Implications

This study contributed to enhancing the literature on organizational agility. First, this study examined an integrated framework that captures the relevant agility enablers affecting organizational agility in upstream O&G producers. It confirmed that HTO, leadership, and innovation capacity were accepted as agility enablers in the upstream O&G producers. Positioning all those enablers would enable an upstream O&G producer to be agile. Second, the results confirmed that HTO is a critical variable for ensuring that other enablers are well applied [32,33,75], indicating that an organization cannot focus solely on operational variables to create organizational agility. HTO supports an organization to control the challenges and barriers related to human, technological, and organizational factors [32]. Third, this study revealed that an agility enabler may elicit different responses across industries. Knowledge management and IT competence were identified as agility enablers in previous studies across various industries, but were not accepted as enablers for the upstream O&G producers. Fourth, this study identified several rejection hypotheses regarding the relationships among specific agility enablers that affect organizational agility in the upstream O&G producers. The characteristics of upstream O&G producer may lead to differences in research results compared with previous studies under different conditions. Therefore, this study highlighted the importance of assessing the industry’s characteristics and the context’s alignment when applying a theoretical finding on agility enablers.

5.2. Managerial Implications

In managerial implications, this study contributed to providing three insights. First, implying the critical role of HTO, leadership, and innovation capacity to be run simultaneously, provides managers with the ability to prepare organizational competencies appropriately to create organizational agility. Second, creating organizational agility is a journey for an organization. There are several aspects to be considered and require firm commitment from top management. Organization needs to invest in enhancement of people competencies, system and infrastructure capabilities, and organizational related capabilities to make it happen. Third, understanding the study results leads to a shift in management’s mindset toward creating organizational agility, as the exploration of some relationships involving well-known variables was rejected in the upstream O&G.

6. Conclusions

This study investigated the impact of relevant agility enablers on organizational agility within Indonesia’s upstream O&G, with special emphasis on the interplay between HTO, leadership, innovation capacity, knowledge management, and IT competence. The results revealed that HTO, leadership, and innovation capacity significantly influence organizational agility, explaining 83.7% of the observed variance. In contrast, there were no relationships among the agility enablers, confirming that each enabler does not influence the others in generating organizational agility. Most previous studies considered agility enablers as a specific variable that will immediately lead to organizational agility. However, the examination of HTO in this study strengthened the idea that focusing on a particular agility enabler is essential, but it still requires supporting elements to address challenges and obstacles in implementing the agility concept.
This study identified several limitations as insights for future research. Collecting a large sample of data representing all upstream O&G fields was difficult, since the upstream O&G operations spread across all of Indonesia. Therefore, the study findings are limited to the upstream O&G company in the production phase. Consequently, the result framework in this study needs to be assessed for implementation in different characteristics. Moreover, this study was conducted only in the upstream O&G; therefore, the results cannot be generalized to other industries. Then, a dynamic circumstance leads to policy changes in managing upstream O&G business, which might result in additional agility enablers to be considered. Future research might expand the study’s scope by integrating other agility enablers, making comparisons across different industries, and utilizing larger datasets, in addition to examining how new digital technologies and industry transformations are altering the energy industry’s agility environment.

Author Contributions

O.G.P. is the lead author of this paper, responsible for conceptualization, methodology formulation, software utilization, data curation, data validation, formal analysis, visualization, project administration, and writing the original draft preparation. A.S. as the supervisor provided conceptual direction, methodology formulation, formal analysis, funding acquisition, reviewing editing of the final manuscript, and validation at all stages of the study. Y. provided input on the concern areas of human–technology–organization and organization agility, including analysis of relevant literature and implementation in industry. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hibah Pubilikasi Terindeks Internasional (PUTI) Pasca Sarjana Doktoral 2022–2023 University of Indonesia, grant number No. NKB-303/UN2.RST/HKP.05.00/2022.

Institutional Review Board Statement

Faculty of Engineering Universitas Indonesia does not have an IRB department, but this study was conducted in accordance with the principles of the Declaration of Helsinki. The host institution is aware of the details of this study and confirms that it complies with ethical standards.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

During the preparation of this manuscript/study, the authors used an AI-based proofreading application for the purposes of English language proofreading, style suggestion, and word consistency checking. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflicts of interest associated with the publication of this research.

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Figure 1. Conceptual Framework and Hypotheses.
Figure 1. Conceptual Framework and Hypotheses.
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Figure 2. Flowchart of the study’s methodology [18,21,23,30,32,33].
Figure 2. Flowchart of the study’s methodology [18,21,23,30,32,33].
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Figure 3. Estimated path model result.
Figure 3. Estimated path model result.
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Table 2. Characteristics of responding companies (N = 103).
Table 2. Characteristics of responding companies (N = 103).
Description ClusterFrequencyPercentageCumulative Percentage
Company production size (KBOEPD)0–100 KBOEPD4947.647.6
>100 KBOEPD5452.4100.0
Total103100.0-
OwnershipState-owned company7168.968.9
Private company3231.131.1
Total103100.0-
Company establishmentFewer than 10 years3635.035.0
10–20 years5452.487.4
Over 20 years1312.6100.0
Total103100.0-
Number of Full-time EmployeesFewer than 2502019.419.4
250–5002120.439.8
500–7504341.781.6
Over 7501918.4100.0
Total103100.0-
Operation TypeOnshore4644.744.7
Offshore2726.270.9
Onshore & offshore3029.1100.0
Total103100.0-
Table 3. Demographic non-parametric test.
Table 3. Demographic non-parametric test.
ClusterCharacteristicsp-ValueDecision
Company production size (KBOEPD)0–100 KBOEPD
>100 KBOEPD		0.05No evidence of a difference between group
OwnershipState-owned company
private company 		0.398No evidence of a difference between group
Company establishmentFewer than 10 years
10–20 years
Over 20 years		0.306No evidence of a difference between group
Number of Full-time EmployeesFewer than 250
250–500
500–750
Over 750		0.461No evidence of a difference between group
Operation TypeOnshore
Offshore
Onshore & offshore		0.511No evidence of a difference between group
Table 4. The reliability and validity statistics.
Table 4. The reliability and validity statistics.
ConstructCACRAVE
IT competence0.9470.9530.648
Digital platform capability (DPC)0.9260.9470.817
IT Infrastructure (ITI)0.8440.9040.759
Data analytics capabilities (DAC)0.9060.9320.774
Innovation capacity0.9500.9550.661
Innovation opportunity (IO)0.9280.9480.819
Knowledge creation (KC)0.8600.9140.780
Resources allocation (RA)0.9140.9370.788
Human–Technology–Organization0.9660.9680.655
Human skills & capabilities (HSC)0.8930.9240.754
Technology adoption (TA)0.9010.9380.834
Organizational structure & design (OSD)0.9120.9580.919
Change management (CM)0.8610.9350.877
Performance management (PM)0.9220.9420.803
Leadership0.9580.9630.745
Entrepreneurial orientation (ENT)0.9400.9710.943
Adaptability (ADA)0.8610.9340.876
Communication (COM)0.9210.9500.863
Empowerment (EMP)0.8340.9230.857
Knowledge management0.9160.9350.704
Learning culture (LC)0.9340.9680.938
Knowledge accessibility (KNO)0.8690.9390.884
Knowledge transfer (KT)0.8660.9370.882
Organizational agility0.9610.9690.863
Note. Bold indicates for higher-order construct values.
Table 5. Multicollinearities VIF.
Table 5. Multicollinearities VIF.
HTOIT CompetenceInnovation CapacityKnowledge ManagementLeadershipOrganization Agility
HTO 4.843
IT Competence 3.782
Innovation Capacity 3.413 4.515
Knowledge Management4.932 3.230 4.044
Leadership4.673 4.928
Organization Agility
Table 6. Path coefficients and significance level of the structural model.
Table 6. Path coefficients and significance level of the structural model.
Relationship Between Variables in ModelEstimated ValueStandard Errort-Valuep ValueResult
H1: HTO ➔ organizational agility0.4030.1672.4140.016H1 strongly supported
H2: Leadership ➔ organizational agility0.2650.1142.3140.021H2 strongly supported
H3: Knowledge management ➔ organizational agility−0.0110.1070.1020.919H3 rejected
H4: Innovation capacity ➔ organizational agility0.2740.1032.6670.008H4 strongly supported
H5: IT competence ➔ organizational agility0.0280.0760.3720.710H5 rejected
H6: Leadership ➔ HTO0.0000.0060.0390.969H6 rejected
H7: Knowledge management ➔ HTO0.0000.0050.0670.947H7 rejected
H8: Knowledge management ➔ innovation capacity0.0010.0010.7500.454H8 rejected
H9: Innovation capacity ➔ IT competence0.0000.0000.1210.904H9 rejected
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Putra, O.G.; Suzianti, A.; Yassierli. Enhancing Organizational Agility in Sustaining Indonesia’s Upstream Oil and Gas Sector: An Integrating Human-Technology-Organization Framework Perspective. Sustainability 2025, 17, 11346. https://doi.org/10.3390/su172411346

AMA Style

Putra OG, Suzianti A, Yassierli. Enhancing Organizational Agility in Sustaining Indonesia’s Upstream Oil and Gas Sector: An Integrating Human-Technology-Organization Framework Perspective. Sustainability. 2025; 17(24):11346. https://doi.org/10.3390/su172411346

Chicago/Turabian Style

Putra, Octaviandy Giri, Amalia Suzianti, and Yassierli. 2025. "Enhancing Organizational Agility in Sustaining Indonesia’s Upstream Oil and Gas Sector: An Integrating Human-Technology-Organization Framework Perspective" Sustainability 17, no. 24: 11346. https://doi.org/10.3390/su172411346

APA Style

Putra, O. G., Suzianti, A., & Yassierli. (2025). Enhancing Organizational Agility in Sustaining Indonesia’s Upstream Oil and Gas Sector: An Integrating Human-Technology-Organization Framework Perspective. Sustainability, 17(24), 11346. https://doi.org/10.3390/su172411346

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