This is a self-archived – parallel published version of this article in the 

publication archive of the University of Vaasa. It might differ from the original. 

Big Data Analytics and Green Core Competencies: Important 

Role of Data-Driven Decision-Making Culture and Leader 

Conscientiousness 

 

Author(s): Usman, Muhammad; Zahoor, Nadia; Boğan, Erhan; Akhtar, 

Muhammad Waheed; Dedeoğlu, Bekir 

Title: Big Data Analytics and Green Core Competencies: Important 

Role of Data-Driven Decision-Making Culture and Leader 

Conscientiousness 

Year: 2026 

Version: Accepted manuscript 

Copyright ©2026 IEEE. Personal use of this material is permitted. 

Permission from IEEE must be obtained for all other uses, in 

any current or future media, including reprinting/republishing 

this material for advertising or promotional purposes, creating 

new collective works, for resale or redistribution to servers or 

lists, or reuse of any copyrighted component of this work in 

other works. 

Please cite the original version: 

 Usman, M., Zahoor, N., Boğan, E., Akhtar, M. W. & Dedeoğlu, 

B. (2026). Big Data Analytics and Green Core Competencies: 

Important Role of Data-Driven Decision-Making Culture and 

Leader Conscientiousness. IEEE transactions on engineering 

management 73, 1363-1375. 

https://doi.org/10.1109/TEM.2025.3648578 

 



  

Big data analytics and green core competencies: Important role of data-driven decision-

making culture and leader conscientiousness 

Abstract 

 

Given the growing emphasis on business sustainability and environmental management aimed at 

protecting the natural environment, this study, drawing insights from organizational information 

processing theory, investigates the direct association of artificial intelligence-supported big data analytics 

(AI-BDA) with organizations' green core competencies as well as the indirect association through green 

data-driven decision-making culture (GDDC). Additionally, the role of leader conscientiousness as an 

important boundary condition is examined. Data collected from 339 managers is analyzed using structural 

equation modeling in Mplus (8.8). Our findings indicate that AI-BDA has both direct and indirect positive 

relationships with green core competencies. Moreover, leader conscientiousness moderated the direct 

impact of AI-BDA on GDDC. Supplementary semi-structured interviews with 12 senior managers provide 

contextual validation and illustrate how AI-BDA, green data-driven culture, and leader conscientiousness 

jointly shape green core competencies in practice. Our research provides actionable insights that empower 

organizations to develop green core competencies, thereby enhancing their impact on initiatives aimed at 

preserving the natural environment. By integrating technological capabilities with leadership traits, this 

study highlights a pathway for organizations to align digital transformation with environmental 

sustainability goals, thereby advancing their corporate responses to environmental challenges. 

Keywords. AI-supported big data analytics, green data-driven decision-making culture, green core 

competencies, leader conscientiousness 

  



 

2 

 

 

 

Managerial Relevance Statement 

This paper offers valuable insights for engineering managers seeking to enhance their 

organization's green core competencies through digital advancements and effective leadership. Our 

findings showed that AI-BDA has a positive association with green core competencies both directly and 

indirectly through GDDC. AI-BDA enables organizations to achieve real-time environmental 

monitoring and data-driven decision-making, supporting positive environmental performance and green 

innovations. Positive impacts of AI-BDA are best realized when there is a GDDC, a culture where 

gathering, analyzing, and using green data are critical components of decision-making processes and 

operations. Moreover, our findings indicate that the positive impact of AI-BDA on GDDC is more 

pronounced when leaders are conscientious. Leaders who are more responsible, organized, detail-

oriented, and diligent are more likely to integrate sustainability into their decision-making and motivate 

people to innovate through data-driven approaches. Therefore, organizations should develop training 

sessions that focus on conscientiousness, such as paying attention to detail and promoting greater ethical, 

self-discipline, and accountability. 

1. Introduction 

 

There is a growing emphasis on business sustainability and environmental management, as well as 

the role of emerging technologies in shaping sustainable business practices, reflecting a global shift toward 

practices aimed at protecting the natural environment [10], [25], [60]. Big data encompasses heterogeneous 

formats and is distinguished by velocity, volume, veracity, and variety [55]. This combination empowers 

organizations to leverage advanced algorithms and machine learning models for real-time analysis, 

uncovering hidden patterns and making data-driven decisions [13], [52], [38]. In this context, artificial 

intelligence-based big data analytics (AI-BDA) may play a pivotal role. AI-BDA reflects a transformative 

approach, integrating artificial intelligence techniques with big data analytics processes to derive valuable 

insights, patterns, and trends from large and complex datasets [21], [27], [42], [52]. Previous studies (see 



  

Appendix A) suggest that AI-BDA improves organizations’ market performance [48], fosters green supply 

chain performance [10], [25], increases organizations’ environmental performance [39], and leads to green 

supply chain integration and green innovation [5], [16], [60]. 

Despite growing interest in AI-BDA and its potential benefits, existing research has paid little 

attention to how it contributes to the development of green core competencies. While studies have 

acknowledged the importance of green core competencies for outcomes such as green innovation, green 

absorptive capacity, green image, and green innovation performance [2], [15], [47], limited empirical 

work has examined their technological antecedents. Recent literature reviews (e.g., [2], [47]) specifically 

call for further research on what enables such competencies to emerge. Therefore, this study responds to 

that call by investigating AI-BDA as a structural capability that may directly and indirectly improve green 

core competencies, thereby addressing a critical and underexplored link in the sustainability literature. To 

support this argument, we draw on organizational information processing theory (OIPT) [29], [56] to 

propose AI-BDA as an organizational structural capability that leads to green core competencies. OIPT 

emphasizes the structures and mechanisms within organizations that enable the flow of information and 

effective decision-making processes [10]. The theory posits that organizations with strong capabilities in 

processing and utilizing data are better equipped to address external uncertainties and demands. In this 

context, adopting new-generation technologies such as AI-BDA is seen as a means to bolster information 

processing capabilities [37], [10]. These technologies enable organizations to utilize advanced tools for 

handling vast amounts of diverse data and processing it quickly, thereby enhancing their analytical 

capabilities [41]. As such, we contend that by facilitating the effective processing and utilization of big 

data, AI-BDA enables organizations to integrate environmental data and insights into their strategies and 

operations, helping them develop green core competencies. 

Furthermore, to illustrate how AI-BDA enhances green core competencies, we propose that a green 

data-driven decision-making culture (GDDC) serves as a mediator in the established relationship. Drawing 

on previous studies (e.g., [6], [63].), we define GDDC as an organizational culture where collecting, 

analyzing, and utilizing environmentally relevant data are integral to decision-making processes and 



 

4 

 

 

operations. We consider GDDC because it emphasizes leveraging data-driven insights to guide 

environmental management practices and sustainability initiatives, thereby reducing ecological footprints 

and mitigating environmental issues. This culture fosters a proactive approach to sustainability by 

promoting the use of data to inform and drive green initiatives across all levels of the organization [31], 

[40]. Thus, we contend that by cultivating a strong GDDC, organizations can leverage data-driven 

decision-making to drive environmental sustainability initiatives within the organization and develop 

green core competencies. Davenport and Bean [18] report that 99% of their study respondents indicated 

their businesses were striving to transition to a data-driven decision-making culture. However, only one-

third of the organizations had successfully achieved this goal. Despite this gap being consistently 

identified in annual surveys, there has been little improvement in companies' success rates in achieving a 

data-driven decision-making culture. Furthermore, previous studies suggest that there is a scarcity of 

research on how and when AI-BDA contributes to environmentally sustainable initiatives [39]. To address 

these gaps, we argue that studying the role of GDDC can bring to the fore its value for developing core 

competencies that may encourage organizations to adopt such a culture. 

The current study further argues that the influence of AI-BDA on GDDC may differ across 

organizations. We propose that leader conscientiousness can explain these differentiated effects. One of 

the five personality traits in the Five-Factor Model of Personality, conscientiousness, is a concept that 

describes an “individual who is generally ambitious, responsible, and abides by ethical principles, and 

considers the consequences of his/her behavior before acting” [12, p. 92]. Leaders’ personality plays a 

vital role in interpreting the effect of organizations’ initiatives related to big data analytics on 

organizational outcomes [9]. Leaders high on conscientiousness are expected to pay more attention to AI-

BDA because, compared to others, they show higher degrees of obligation toward their organizations. 

Leaders who are high on conscientiousness are more mindful and cognizant of environmental challenges, 

fulfill environmental obligations, and alleviate environmental issues [10]. As such, we contend that 

leaders’ conscientiousness can substantially affect the role of AI-BDA in cultivating GDDC, which, in 

turn, may augment organizational green core competencies. The proposed model is depicted in Figure 1. 



  

 

 

Insert Figure 1 about here 

 

 

The current research adds to the following important streams of literature. First, our study 

addresses the calls for further research on business sustainability and environmental management, which 

aim to protect the natural environment [2], [15], [47], as well as the role of digital technologies in 

environmental management [8], [12]. Existing studies have found a positive impact of big data analytics 

on various outcomes, including firm performance [48], user satisfaction [34], sustainable performance 

[35], and green supply chain performance [51]. This research contributes to the existing literature by 

highlighting the significant yet underappreciated role of AI-BDA in enabling organizations to develop 

green core competencies. Second, by illustrating AI-based BDA and GDDC as the antecedents of green 

core competencies, the present research contributes to the limited research on the antecedents of green core 

competencies [2], [15], [47]. Third, existing studies on data-driven decision-making culture demonstrate 

that it enhances firms’ competitive advantage [57], sustainability performance [14], and environmental 

performance [58]. By suggesting the crucial role of GDDC as a mediator in the connection between AI-

BDA and green core competencies, this study contributes to the existing literature on the determinants and 

outcomes of a green data-driven culture. Finally, the present study adds to the literature on leader 

conscientiousness (e.g., [8], [12]). By analyzing the hypothesized links, the current research answers the 

recent calls for further research on how organizations can reduce their carbon emissions through various 

environmentally sustainable initiatives and competencies [2], [47] and the role of AI-BDA in developing 

organizations’ pro-environmental capabilities [25], [39]. 

2. Theoretical background 

2.1. Organizational information processing theory 

Organizations are operating in a dynamic, complex, and turbulent environment that creates 

uncertainty for managers while identifying the opportunities and threats in the marketplace, trying to adapt 



 

6 

 

 

to the relevant changes within organizations, and deciding to make organization-wide decisions, such as 

changing the core strategies [26], [33]. Managers must consider many factors that require allocation with 

different weights in the decision-making process [24], [44]. Organizations face a range of uncertainties 

(e.g., shifts in customer preferences, rapid technological advancements, and changes in regulations and 

laws) that can significantly impact their decision-making effectiveness. In situations of high uncertainty, 

the capabilities of organizations to process, analyze, and utilize information about the events in their 

environment become crucial for an effective decision-making process [28], [45].  

Previous studies mostly use resource-based view theory [65], [66], organizational learning theory 

[67], and complexity theory [68] to examine the relationship between big data analytics and firm 

performance. Unlike these studies, we build on OIPT to hypothesize the relationships examined in the 

research model. Organizational learning theory and resource-based view focus on an organization’s 

internal resources or knowledge acquisition. Specifically, the resource-based view emphasizes firm-

specific tangible and intangible resources that are valuable, rare, inimitable, and non-substitutable [79]. In 

contrast, organizational learning theory highlights how firms systematically pursue, acquire, and apply 

information to improve their capacity to adapt and innovate in processes, products, and services [80]. 

Complexity theory, on the other hand, views organizations as complex adaptive systems that evolve 

through self-organization and multiple interacting elements [81]. Rather than assuming a single linear path 

to performance outcomes, complexity theory emphasizes that different combinations of conditions may 

lead to similar results, acknowledges the existence of contrarian cases, and highlights causal asymmetry 

in organizational phenomena [82]. Unlike these perspectives, OIPT provides a direct explanation of how 

organizations respond to environmental uncertainty by enhancing their information processing capacity. 

Since our study investigates AI-BDA and GDDC as mechanisms through which firms gather, analyze, 

and utilize environmental data to build green core competencies, OIPT offers a more suitable theoretical 

lens than RBV, organizational learning theory, or complexity theory. 

OIPT addresses the need for organizations to manage uncertainties and process information 

efficiently in dynamic contexts. OIPT posits that organizations need more information while making 



  

decisions in uncertain external conditions to make effective decisions and sustain the desired level of 

performance [28], [53]. In other words, information is considered a key organizational resource that is 

pivotal in determining an organization’s competitiveness in the dynamic business environment. 

Accordingly, in this study, which is theoretically grounded in OIPT, we investigate how organizations can 

enhance their green core competencies by adopting AI-based BDA directly and through GDDC. 

3. Hypotheses Development 

 

3.1. AI-BDA and Green Core Competencies 

 

Core competencies enable organizations to adapt to a changing environment and are considered a 

pivotal source of competitive advantage [15]. Core competencies enable organizations to explore and 

capitalize on new opportunities, thereby differentiating themselves from competitors [46]. Considering 

the current environmental regulations and stakeholders’ increasing awareness of environmental issues, 

organizations feel severe pressure on themselves to comply with environmental sustainability practices 

[47]. Building on the concept of core competence by [46] and existing literature on green core competence 

[2], [15], [47], we define green core competencies as the collective learning and capabilities concerning 

environmental management and technologies that are rare, less imitable by competitors, and less likely 

to be substituted. Developing green core competencies serves as a strong indicator of an organization’s 

commitment to environmental responsibility [15]. 

Organizations can gain valuable insights from AI-BDA, including forecasting energy demand, 

formulating more effective plans to minimize waste, and optimizing energy usage during manufacturing 

processes [4], [27]. AI-BDA may help organizations improve the processes to mitigate resource 

consumption and reduce environmental impact. For instance, a recent study [36] highlighted the significant 

impact of AI-BDA on forecasting water availability, optimizing resource allocation, and enhancing 

infrastructure maintenance in a multifaceted manner. By leveraging AI-BDA, organizations can identify 

patterns and trends in environmental data that may not be readily apparent through traditional methods. 

Consequently, organizations may develop a deeper understanding of environmental challenges and 



 

8 

 

 

opportunities for improvement that can form the foundation for green core competencies. Furthermore, as 

organizations continually gather and analyze environmental data, they accumulate knowledge and 

capabilities and adopt technological advancements that are rare and less imitable by competitors. The 

sophistication and complexity of AI algorithms and data processing techniques are likely to contribute to 

the uniqueness of these competencies, making them difficult for competitors to replicate or substitute. 

OIPT suggests that organizations with strong capabilities in processing and utilizing data are better 

equipped to deal with uncertain external environmental conditions, making effective decisions and 

developing capabilities that help them gain a foothold in the market [28]. OIPT also suggests that 

“employing computers” and “various machine mechanisms” are critical factors in effective decision-

making, as they enhance organizational information processing capabilities [15, p. 17]. Therefore, 

investment in information technology is considered a crucial aspect of enhancing the flow of information, 

improving effective decision-making capability, and reducing uncertainties [15]. Researchers highlight 

the role of employing new-generation technologies, such as artificial intelligence and big data analytics, 

for enhancing an organization's information processing capabilities [37], [10]. These technologies have 

democratized access to tools capable of handling extensive data volumes, which are diverse in nature, and 

processing them at high velocities, empowering organizations with unprecedented analytical capabilities 

[41]. As such, drawing on OIPT, we contend that by leveraging the distinct advantages of AI-BDA in 

acquiring, processing, and utilizing vast amounts of environmental data, organizations can develop green 

competencies that are rare and less likely to be imitable. Based on the above arguments, we propose the 

following hypothesis. 

H1: AI-BDA is positively associated with organizational green core competencies. 

 

3.2. AI-BDA and GDDC 

 

AI-BDA represents a cutting-edge set of technologies and infrastructures tailored to efficiently 

extract value from vast volumes of diverse data [16]. AI-BDA empowers organizations to promptly 

capture, discover, and analyze data, facilitating the generation of valuable insights and effective decision 



  

making [30]. Big data analyzed using AI methods can help organizations gain insights from a larger 

dataset by identifying patterns and trends, thereby steering them toward more effective and 

environmentally conscious operational strategies [27]. We argue that AI-BDA may serve as a catalyst for 

cultivating GDDC within organizations. OIPT suggests that organizations with robust IT systems (e.g., 

AI-BDA) can more effectively gather, interpret, and act upon environmental data [28]. Studies suggest that 

the availability of big data tools fosters data literacy across the organization as employees become more 

proficient in interpreting and leveraging data to inform their actions [16], [60]. Decision-making using 

AI-BDA by top management encourages managers at different levels and employees to engage with data 

in their decision-making processes [9]. Furthermore, AI-BDA facilitates evidence-based decision-making 

by enabling organizations to derive insights from empirical evidence, rather than relying solely on 

intuition or anecdotal evidence [40]. Consistent use of data-driven insights enhances the accuracy and 

effectiveness of decisions, encouraging a culture of using data insights to inform decisions [9]. Overall, 

we contend that AI-BDA plays a pivotal role in instilling a culture where data is valued as a strategic asset 

and utilized to drive organizational success. 

Additionally, a culture of using accurate and comprehensive information related to trends and 

projections, encompassing sustainability, customer expectations, and the availability of raw materials, is crucial for 

organizations to identify and implement eco-friendly practices within their production and operational processes 

[40]. In GDDC, managers prioritize using data over intuition and integrating environmental considerations 

into their decision-making processes [40]. GDDC is a fundamental requirement for continuously 

improving manufacturing processes to achieve low energy consumption, utilize recycled and reused 

materials, and employ cleaner technology to prevent pollution [61], [7]. GDDC fosters a proactive 

approach toward leveraging product development opportunities [20] and supports firms in achieving 

ambidextrous innovation and enhancing business sustainability [35], [1]. Furthermore, GDDC helps 

organizations navigate uncertainty by enhancing their data scanning capabilities and enabling them to 

respond promptly to market dynamics and changing customer needs [6], [63]. GDDC enhances the 

awareness and attitudes of the organization’s members toward sustainable practices, leading them to 



 

10 

 

 

reconsider their manufacturing processes in order to reduce waste and the use of raw materials [32], [43]. 

In green culture, environmental considerations are embedded in organizations’ strategy, operations, and 

decision-making processes [32]. Therefore, a green culture based on data insights can cultivate a 

heightened awareness of sustainability issues and opportunities. This heightened awareness may prompt 

the organization to invest in developing capabilities related to environmental management, technologies, 

and innovation that can nurture green core competencies. For example, an electronics manufacturer may use 

AI-BDA to analyze product lifecycle data. When these insights are regularly used to guide design and 

supply chain decisions, it reflects a GDDC. Over time, this leads to the development of green capabilities, 

including green innovation and a green image. This example illustrates how GDDC mediates the 

relationship between AI-BDA and green core competencies. Consistent with OIPT, AI-BDA acts as a 

structural mechanism that enhances the organization’s capacity to collect, interpret, and use environmental 

information. This technological capability precedes and shapes the development of GDDC, which reflects 

the cultural manifestation of these enhanced information-processing capacities. Thus, GDDC serves as the 

pathway through which AI-BDA’s technological potential is transformed into organizational green 

competencies. Together, based on the above arguments, we hypothesize the following. 

H2: GDDC mediates the relationship between AI-BDA and green core competencies. 

 

3.3. Leader conscientiousness as a moderator 

Organizations today face considerable complexity and uncertainty in the context of environmental 

sustainability due to external factors largely beyond their control, including new regulations, shifting 

customer expectations, and natural disasters. In these conditions, OIPT suggests that organizations with 

enhanced abilities to gather, process, and utilize information are better equipped to adapt to their 

environments, make informed decisions, and ultimately achieve higher levels of effectiveness and success. 

A key premise of OIPT is that enhanced information processing capability leads to improved 

organizational performance [28]. Building on this premise of OIPT, we propose that leader 

conscientiousness strengthens the direct link between AI-BDA and GDDC and the indirect link between 



  

AI-BDA and green core competencies. Conscientiousness is a concept based on the Big Five personality 

model. The concept of responsibility is a very important factor in shaping the attitudes and behaviors of 

leaders. Conscientiousness represents the tendency to be goal-oriented, responsible, and organized [17]. 

Leader conscientiousness is a fundamental attribute affecting various organizational dynamics [11], [49]. 

The effectiveness of an organization's environmental sensitivity and the formulation of environmentally 

conscious policies and strategies largely hinges on the moral integrity of its leaders. Conscientious leaders 

are characterized by their awareness, perseverance, determination, and ability to complete tasks [8]. 

Conscientiousness involves ambition and a conscientious attitude toward others in the immediate 

environment [3], [8]. Individuals with a high level of conscientiousness are relatively better positioned to 

focus on ongoing events [23]. 

Leaders high in conscientiousness proactively motivate employees to engage in eco-friendly 

behaviors and prioritize sustainability within the work environment [3]. Therefore, they are likely to 

encourage and direct organizational members to use environmental data in their decision-making. Through 

their systematic and disciplined approach, conscientious leaders may ensure that the organization 

effectively utilizes big data insights to drive environmentally conscious behaviors and practices across all 

levels. By setting clear objectives and establishing robust processes, they may create an environment where 

integrating big data capability with green initiatives becomes feasible and ingrained in the organizational 

ethos. As a result, leader conscientiousness acts as a catalyst, reinforcing the link between big data 

capability and the cultivation of GDDC, ultimately facilitating sustainable green core competencies. 

Within the framework of these arguments, our hypotheses are formulated as follows: 

H3: Leader conscientiousness moderates the association of AI-BDA with GDDC, such that the 

association is stronger when leader conscientiousness is high (vs. low). 

4. Methodology 

4.1. Sample and procedure 

Data were collected using a time-lagged design, with three phases separated by two-week intervals. 



 

12 

 

 

The target population comprised senior managers working in manufacturing firms across China, 

specifically in industries such as leather, textiles, and ceramics. We focused on these industries due to 

their significant environmental impact and ongoing adoption of AI-BDA and green practices. Participants 

were recruited from a pool of 700 alumni of a large public university in China. These alumni held senior 

managerial positions and were actively involved in strategic or operational decision-making processes 

within their organizations. Each potential participant received an invitation via email, which explained the 

purpose of the study, the voluntary nature of participation, the assurance of confidentiality, and the 

anonymity of responses. Out of 700, a total of 482 managers agreed to participate in the study.  

In phase 1, we distributed the first wave of the questionnaire to these 482 consenting participants, 

focusing on demographic and organizational variables (e.g., firm age and size), AI-BDA, and leader 

conscientiousness. A total of 445 valid responses were returned within the specified time frame. Two 

weeks later, in phase 2, the same participants were contacted again to provide data on GDDC. We received 

376 complete responses in this round. In phase 3, conducted two weeks after Phase 2, the third and final 

survey was distributed to collect data on green core competencies. This round yielded 346 responses. To 

ensure accuracy and consistency, each participant was assigned a unique code, which allowed us to match 

responses across all three phases. After eliminating responses with missing or unmatched data, the final 

sample consisted of 339 complete and usable responses. The data were analyzed using structural equation 

modeling (SEM) in Mplus (8.8). Firms’ average age was 34.15 years. 

4.2. Common method bias and response bias 

A time-lagged design was employed to address the issues related to common method bias. We also 

tested the data for sampling bias. We also employed Harman's single-factor method. To do so, all the items 

were constrained to load on a single factor. This single factor explained 31.39% of the variance, which is 

well below the 50% cut-off. 

We also tested the data for response bias. No demographic differences were found between the 

final sample (n = 339) at Time 3 and the initial sample (n = 445) at Time 1. Furthermore, to assess whether 



  

subject attrition from Time 1 to Time 3 led to non-random sampling, we tested whether the probability of 

remaining in the final sample (n = 339) could be predicted by demographics and the variables measured 

at Time 1 (AI-BDA). We employed the criteria suggested by [78]. We used logistic regression analysis to 

assess the presence of non-random sampling. Overall, results indicated that respondents’ attrition in the 

sample was mainly random 

4.3. Measures and variables 

 

The details regarding the measurement of the constructs used in the current research are as follows. 

We assessed AI-BDA by adopting a four-item scale (α = .88) [4]. “We use advanced analytical techniques 

(e.g., simulation, optimization, regression) to improve decision-making” was a sample item. GDDC was 

assessed by adopting a five-item scale (α = .88) [63]. “We continuously coach our employees to make 

decisions based on data” was a sample item. Green core competencies were measured by adapting a five-

item scale (α = .87) from [15]. “The environmental capabilities, technologies, or know-how of the firm are 

rare in the marketplace” was a sample item. Conscientiousness was measured by adopting a four-item 

scale (α = .83) [19]. The items were assessed on a 5-point scale (1 = strongly disagree and 5 = strongly 

agree), with greater scores showing greater conscientiousness. Sample item: “I get chores done right 

away.” 

4.4. Control variables 

 

Extant studies show that firm age and size may confound the results [10], [63] and were 

consequently considered control variables. Furthermore, green knowledge-sharing culture (GKSC) can 

confound the results. GKSC creates a foundation for informed decision-making and can enhance green 

competencies [27], [40]. Thus, we controlled for GKSC. To measure GKSC, we adapted a 5-item scale (α 

= .85) from [76]. Sample items include: “In our organization, employees share environmentally-friendly 

knowledge with other organizational members.” 

4.5. Supplementary Qualitative Phase 



 

14 

 

 

To enhance methodological rigor and provide deeper contextual insights, we supplemented the 

quantitative survey with a sequential explanatory qualitative phase [83]. After completing the survey 

analysis, we conducted 12 semi-structured interviews with senior managers from Chinese manufacturing 

firms across various sectors, including leather, textiles, and ceramics. Participants were purposively 

selected from the same alumni network used for the survey to ensure comparability. Interviews lasted 45-

50 minutes and were conducted via WeChat, audio-recorded, and transcribed verbatim. 

The initial set of questions (Appendix A) consisted of eight open-ended questions. The questions 

probed actual practices corresponding to the scale items used in the survey. Data were analyzed using 

thematic analysis [84]. Themes are presented in (Appendix B). Coding was performed by the first author 

and cross-checked by the second author; disagreements were resolved through discussion. Theoretical 

saturation was reached after the 12th interview. This qualitative phase triangulates and illustrates the 

quantitative findings, offering concrete examples of how AI-BDA, GDDC, leader conscientiousness, and 

green core competencies interact in practice. 

5. Results 

5.1. Means and correlations 

Means and correlations are presented in Table 1. All the correlations among the main variables 

were significant and largely in the expected direction. 

 

 
 

 

Insert Table 1 about here 

5.2. Measurement model 

 

 

Confirmatory factor analysis was employed to assess the measurement model, which comprised 

AI-BDA, GDDC, green core competencies, leader conscientiousness, and GKSC. All items demonstrated 

statistically significant loadings (p < .01). The results of the confirmatory factor analysis indicated a 



  

satisfactory model fit: χ²(220) = 522.325, χ²/df = 2.38, IFI = .93, TLI = .92, SRMR = .07, and RMSEA = 

.06. These fit indices confirm that the measurement model aligns well with the observed data. Additionally, 

the average variance extracted (AVE) for each construct exceeded the recommended threshold of 0.50 (see 

Table 2), indicating support for convergent validity. Discriminant validity was also established, as the 

square roots of AVEs for all constructs were higher than their respective inter-construct correlations. 

Moreover, both the average shared variance (ASV) and the maximum shared variance (MSV) for each 

construct were lower than their AVE values. Collectively, these results confirm that the measurement 

model demonstrates strong validity in terms of both convergent and discriminant validity. 

 

 

 

Insert Table 2 about here 

 

5.3. Hypotheses testing 

 

The results with controls are presented in Table 3. Overall, the results supported our hypotheses 

despite the presence of controls. The results (Table 3) confirmed a significant positive association between 

AI-BDA and green core competencies (B = .23, SE .05, p < .01). Thus, hypothesis 1 was supported. We 

also found a significant indirect positive relationship between AI-BDA and green core competencies via 

GDDC (B = .09, SE = .03, p < .01). Thus, hypothesis 2 was supported. 

Finally, to test the moderation effects, we included the interaction terms of AI-BDA and leader 

conscientiousness in the mediation model. The moderation analysis showed that the interaction between 

AI-BDA and leader conscientiousness was positively related to GDDC (B = .09, SE = .04, p < .05). The 

interactions plotted at +1/-1 SD (Figure 2) from the mean of leader conscientiousness shows that the 

positive influence of AI-BDA on GDDC was stronger when leader conscientiousness was high (B = .35, 

SE = .07, p < .01) compared to when leader conscientiousness was low (B = .13, SE = .06, p < .05). Thus, 

hypothesis 3 was supported. 



 

16 

 

 

 

 

Insert Table 3 and Figure 2 about here 

 

 

5.4. Qualitative Findings 

Thematic analysis of the 12 semi-structured interviews provided rich, convergent evidence to support the 

quantitative model, while adding contextual depth to the mechanisms. The interviewees confirmed that AI-BDA 

is actively used for environmental decision-making. Typical applications included real-time monitoring of energy, 

water, and emissions. One textile plant manager explained:  

“Our AI system analyses dyeing machine data every minute and automatically adjusts temperature and 

chemical dosage. Last year, we were able to reduce water consumption by 22% and dye chemicals by 

18% without sacrificing quality.” 

GDDC emerged as the critical bridge between technology and green outcomes. Ten respondents 

emphasized that identical AI tools produced dramatically different results depending on cultural acceptance. A 

leather company operations director stated:  

“Only the plants where the factory head starts every morning meeting with ‘Show me the data’ actually 

improved. In the others, people still decide by experience.” 

Leader conscientiousness was the most frequently mentioned boundary condition (11 of 12 interviews). 

Respondents consistently contrasted high- and low-conscientious leaders. The sustainability director of a large 

leather group stated:  

“Our group president is detail-oriented and disciplined. Every month, he would personally review the 

ESG dashboard with each factory head and ask for evidence-based explanations. He is like that; the entire 

organization has become data-driven on green issues.” 

Interviewees repeatedly linked the rarity and inimitability of green core competencies to long-term, 

closed-loop integration of AI-BDA with proprietary historical data, and highlighted some barriers, such as 

middle-management resistance and talent shortages. Taken together, the interviews provide vivid, real-world 



  

validation of the entire mediated moderation model. 

6. Discussion 

This study aimed to explore the mechanisms and boundary conditions through which AI-BDA 

influences firms’ green core competencies. Drawing on OIPT, we proposed and tested a conceptual 

framework outlining both the direct and indirect effects of AI-BDA on green core competencies, with 

GDDC serving as the mediating variable. Additionally, we examined the moderating influence of leader 

conscientiousness on the relationship between AI-BDA and GDDC. Using a survey methodology and 

collecting data employing a time-lagged design, we empirically validated the model. The findings 

confirmed a significant positive direct relationship between AI-BDA and green core competencies. 

Moreover, the analysis demonstrated that GDDC plays a mediating role in this relationship. The results 

also showed that leader conscientiousness moderates the impact of AI-BDA on GDDC, indicating that the 

effect of AI-BDA is stronger when leaders exhibit higher levels of conscientiousness. 

According to the findings of the study, GDDC mediates the relationship between AI-BDA and the 

ability to develop green core competencies. This finding is consistent with earlier studies that demonstrate 

the transformation of organizational culture and technology capabilities into benefits [75]. It is through a 

green, data-driven decision-making culture that businesses can exploit the opportunities provided by AI-

BDA to create competitive advantages through competence development. This culture helps ensure that 

environmentally friendly decisions, based on data, are made to the maximum possible extent, thereby 

enhancing the organization's ability to meet its sustainability objectives [69]. Consequently, the mediating 

role of GDDC confirms that technology alone is insufficient to develop green competencies—an enabling 

organizational culture is imperative. While a prior study [50] on engineering management literature 

highlighted the benefits of big data analytics capability on firm performance via dynamic capabilities, our 

study extends that without a strong internal culture that institutionalizes data-informed green decision-

making, AI-BDA alone may not be sufficient to develop green core competencies. 

Furthermore, the analysis suggests that leader conscientiousness is a crucial factor in enhancing 

the influence of AI-BDA on becoming a green, data-driven decision-making organization. Such findings 



 

18 

 

 

generally support earlier literature, which highlights that leadership characteristics influence the pattern 

of organization management and processes [74]. Such leaders emphasize the importance of detail, 

obligation, and endurance, which form a foundation for establishing practices that inform decisions based 

on data, ultimately promoting the sustainability of organizations. It is likely that leaders who score higher 

on these levels of conscientiousness are better positioned to facilitate and integrate green data-oriented 

mechanisms within organizations, ensuring the effective support of AI-BDA initiatives with the firm's 

green agenda. It lends weight to the idea that leadership traits are essential in guiding the use of available 

technology toward practical implementation within an organization [70]. 

Furthermore, leadership conscientiousness was also found to serve as a moderator to the extent of 

this indirect relationship between AI-BDA and firms’ green core competencies through GDDC. Compared 

to prior studies on AI-enabled business model innovation and the role of leaders [73], we shift the 

companies’ focus from business model change to green core competencies. We also extend their results 

by providing empirical support for the role of conscientious leadership, an overlooked personality trait in 

the AI-environmental management nexus. Our findings indicate that the ability of AI-BDA to transform 

core competencies into greener ones relies not only on the existence of a favorable decision-making 

culture but also on the level of responsibility demonstrated by the leaders in charge of such initiatives. 

They evaluate practices such as AI-BDA, which would further enable GDDC to achieve a higher level of 

organizational sustainability commitment by supporting green strategic goals guided by a conscientious 

effort [71]. This evidence highlights the importance of aligning leadership, culture, and technology in 

achieving sustainable business outcomes. Therefore, companies seeking to adopt AI-BDA to achieve 

sustainability targets should focus not only on instilling a green data culture but also on ensuring leadership 

with prudent traits that support such a culture. While a recent study [22] explored how the interaction of 

green digital learning orientation and big data analytics fosters the link between green innovation and 

sustainable performance, our study differs in that it provides a broader capability-building perspective, 

offering a more systemic framework for engineering managers to understand performance-based models. 

The qualitative post-analysis strongly corroborates the quantitative findings and adds explanatory 



  

depth. The 12 interviews vividly illustrated how AI-BDA delivers real-time environmental insights (e.g., 

predictive water and emission management) and confirmed that a green, data-driven decision-making 

culture is the pivotal mechanism that translates technological capability into green core competencies. 

Most importantly, respondents repeatedly emphasized that highly conscientious leaders, described as 

detail-oriented, persistent, and accountability-focused, were the key differentiator in whether AI-BDA 

initiatives succeeded or failed. 

These insights extend organizational information processing theory by showing that, in high-

uncertainty sustainability contexts, information processing capacity (AI-BDA) is necessary but 

insufficient without a supporting data culture and conscientious leadership to enforce disciplined, 

evidence-based environmental decision-making. The interviews also revealed that the rarity and 

inimitability of green core competencies arise from long-term accumulation of proprietary data loops, 

reinforcing the resource-based logic underlying our definition of green core competencies. 

7. Contributions 

 

There is a growing emphasis on business sustainability and environmental management, reflecting 

a global shift toward practices that aim to protect the natural environment. Therefore, there are several 

calls for further research on the role of digital technologies in business sustainability and environmental 

management [2], [15], [47]. Our study responds to such calls for further research on business sustainability 

and environmental management aimed at protecting the natural environment [2], [15], [47] and the role of 

digital technologies in environmental management [8], [12]. By integrating AI-BDA, organizations can 

leverage data-driven insights not only to track and improve their environmental footprint but also to 

innovate their business processes and product offerings. This integration provides a more comprehensive 

approach to sustainability, encompassing economic, social, and environmental dimensions, thereby 

promoting a more resilient and adaptable business model. In doing so, our study extends the following 

literature streams. 

By providing valuable insights into the intersection of AI-BDA and the development of green core 



 

20 

 

 

competencies within organizations, we contribute to the literature on the outcomes of AI-BDA. Previous 

research has demonstrated the positive impact of AI-BDA on various organizational outcomes, including 

firm performance [48], user satisfaction [34], sustainable performance [35], and green supply chain 

performance [51]. In addition, the formation of green core competencies relies on enterprises responding 

to environmental changes and achieving sustainable competitive advantages. Big data tools and AI 

processes for strategic decision-making can be seen as dynamic competencies [72]. In this regard, we 

demonstrate that AI-BDA can enhance organizations’ environmental sustainability-oriented capabilities. 

Therefore, this study extends the body of literature by highlighting the overlooked yet crucial role of AI-

BDA in facilitating the development of green core competencies. Moreover, by emphasizing the 

significant relationship between AI-BDA and GDDC as antecedents of green core competencies, this 

research contributes to the limited understanding of the factors influencing the development of such 

competencies. While previous studies have explored various antecedents of green core competencies [2], 

[15], [47], the specific role of AI-BDA and GDDC in this context remains underexplored. This study fills 

this gap by elucidating how organizations can harness AI-BDA capabilities and foster a GDDC to enhance 

their green core competencies, thus advancing our understanding of the mechanisms driving sustainable 

organizational performance and innovation. 

Additionally, GDDC is critical in enhancing information processing capabilities by prioritizing 

data-driven decision-making [40]. This approach aligns with the principles of green core competencies, 

focusing on developing rare environmental management capabilities to enhance energy savings, pollution 

prevention, and waste recycling [62]. Through AI-BDA, organizations gain access to vast datasets related 

to sustainability trends and resource availability [27]. When integrated into a GDDC framework, these 

insights enable organizations to optimize resource allocation and implement eco-friendly practices [32], 

[59]. Ultimately, this cultural shift towards sustainability, supported by data-driven evidence, drives 

continuous improvement in manufacturing processes and fosters green core competencies. 

Furthermore, leader conscientiousness moderates the indirect effect of GDDC on the relationship 

between AI-BDA and green core competencies. Conscientious leaders foster a culture of data-driven 



  

decision-making, focusing on environmental sustainability, which aligns with the green core 

competencies' goals [15]. The responsible and goal-oriented approach ensures that data-driven strategies 

prioritize green initiatives, enhancing innovation and environmental performance [54], [64]. Conscientious 

leaders' advocacy for green initiatives within GDDC promotes sustainable practices and mitigates 

environmental issues [6]. This moderation by leader conscientiousness strengthens the influence of AI-

BDA on GDDC and green core competencies within an OIPT framework. 

Finally, we extended the scope of OIPT by demonstrating its applicability to modern, AI-driven 

environmental contexts. OIPT emphasizes the importance of organizations' ability to collect, analyze, and 

effectively utilize information [28]. Our study demonstrates how organizations enhance their 

environmental information processing capacity by effectively acquiring sustainability-related data through 

AI-BDA technologies, and how GDDC serves as a key underlying mechanism that transforms this 

capacity into green actions. Furthermore, we extend OIPT by examining leader conscientiousness as a 

moderator influencing how effectively organizations process and act upon environmental information. By 

jointly considering AI-BDA, GDDC, and leader conscientiousness, our study broadens the theoretical 

scope of OIPT and highlights its applicability in guiding organizations to proactively respond to and align 

with environmental demands from stakeholder groups. 

7.1. Implications 

 

Our research findings hold significant practical implications across various domains. First, AI-

BDA offers significant practical implications for organizations seeking to develop green core 

competencies and enhance overall sustainability. By harnessing advanced algorithms and computational 

techniques, AI analytics enables organizations to process and analyze vast volumes of data swiftly, 

extracting valuable insights and patterns crucial for optimizing resource allocation and enhancing 

operational efficiency. This capability enables organizations to identify inefficiencies, reduce energy 

consumption, minimize waste production, and streamline production processes, ultimately leading to cost 

savings and enhanced sustainability performance. Additionally, AI-BDA empowers data-driven decision-



 

22 

 

 

making by analyzing consumer preferences, market trends, and environmental regulations, guiding 

strategic initiatives toward core competencies that can enable eco-friendly products, services, and 

processes. Furthermore, AI algorithms facilitate predictive modeling and scenario analysis, enabling 

organizations to anticipate and mitigate environmental risks, adapt to changing conditions, and ensure 

business continuity while contributing to broader sustainability goals. Overall, AI-BDA serves as a 

catalyst for developing green core competencies that can reduce organizations’ carbon footprint.  

To practically implement AI-BDA, organizations should start by investing in robust AI-BDA 

infrastructure, including advanced data storage and analytical tools that can handle large-scale 

environmental data. AI-BDA infrastructure must be integrated into core decision-making systems across 

the organization, ensuring that environmental data informs strategies related to operations, supply chain, 

and product development. Additionally, managers, particularly decision-makers, should be trained to 

utilize and interpret AI-BDA insights in alignment with sustainability goals. Creating cross-functional 

green data teams can enhance collaboration between IT, sustainability experts, and operations leaders, 

ensuring that AI-driven insights are consistently applied across departments. Organizations should also 

establish data-driven key performance indicators (KPIs) tied to sustainability objectives, regularly 

monitoring progress using AI-BDA tools. 

Second, fostering GDDC within organizations enables informed and strategic decision-making by 

leveraging data insights to identify environmental trends, assess risks, and implement targeted initiatives 

for sustainability. A data-driven culture promotes transparency, accountability, and stakeholder 

collaboration, thereby enhancing employee engagement and fostering a shared responsibility towards 

sustainability goals. GDDC facilitates continuous improvement and innovation in sustainable practices by 

analyzing environmental data, identifying areas for improvement, and innovating new solutions. 

Moreover, it may build trust and credibility among stakeholders, attracting environmentally conscious 

customers and investors while complying with regulatory requirements. Our findings indicate that AI-

BDA enables firms to gather and analyze complex environmental data. However, it is the GDDC that 

ensures these data insights are consistently integrated into decision-making processes. A strong GDDC 



  

can foster an organizational environment where data-driven insights are not only valued but also regularly 

applied to enhance sustainability practices. Consequently, it leads to the development of green core 

competencies. Without GDDC, the potential of AI-BDA may not be fully realized, as data insights may be 

underutilized or inconsistently applied across the organization.  

Moreover, leaders with high levels of conscientiousness tend to exhibit strong organizational 

commitment, diligence, and responsibility toward environmental issues. They are more likely to prioritize 

sustainability initiatives, drive environmentally friendly practices, and advocate for green strategies within 

the organization. As a result, it strengthens the impact of AI-BDA on a culture that values environmental 

responsibility, promotes eco-friendly behaviors among employees, and integrates sustainability into core 

business practices. Conscientious leaders effectively foster a data-driven culture where decisions are 

grounded in evidence and insights from big data analytics. By leveraging data-driven decision-making, 

leaders can identify opportunities for green process innovation, optimize resource allocation, and reduce 

environmental impact across various operational areas. Conscientious leaders are crucial in promoting 

collaboration, communication, and knowledge sharing related to sustainability initiatives. They encourage 

cross-functional teams to work together towards environmental goals, facilitate learning and skill 

development on sustainability practices, and promote innovation through continuous improvement. To 

enhance leaders' conscientiousness, organizations can implement tailored training programs focusing on 

conscientious behaviors, such as attention to detail, accountability, and ethical decision-making. These 

programs should emphasize the importance of integrating sustainability into decision-making and 

highlight how conscientious leadership can enhance the effectiveness of AI-BDA in achieving green goals. 

Organizations can also introduce performance evaluation metrics that assess leaders based on their 

conscientiousness in decision-making, particularly regarding adopting data-driven, eco-friendly practices. 

Linking these behaviors to incentives, such as rewards for sustainability leadership or performance 

bonuses, encourages leaders to consider how they can best utilize AI-BDA in shaping GDDC to enhance 

organizations’ green core competencies ultimately. 

Our findings suggest that organizations can focus on three main factors to enhance their green core 



 

24 

 

 

competencies: AI-BDA, GDDC, and leader conscientiousness. AI-BDA enables organizations to process 

vast amounts of data precisely, facilitating informed decisions supporting green initiatives. As such, 

organizations should invest in AI-BDA to collect, analyze, and interpret large-scale environmental data. By 

enhancing their AI-BDA capabilities, firms can gain deep insights into sustainability challenges, track 

environmental performance in real-time, and identify opportunities for eco-innovation. Further, we 

suggest that AI-BDA alone is insufficient without a strong organizational culture that prioritizes data-

driven decisions. To enhance green core competencies, organizations must foster a GDDC where 

environmental data and analytics are consistently used to guide strategic choices. GDDC would ensure 

that data insights from AI-BDA are applied systematically across the organization to drive sustainable 

practices in areas like resource efficiency, waste reduction, and environmental innovation. Additionally, 

leadership plays a crucial role in ensuring that AI-BDA and GDDC work effectively together to enhance 

organizations’ core competencies. Organizations can enhance leader conscientiousness by promoting 

leaders who are diligent, responsible, and detail-oriented, particularly when it comes to integrating AI-

BDA into green decision-making. Conscientious leaders are more likely to ensure that environmental data 

is used rigorously and consistently, holding their teams accountable for implementing sustainable 

practices based on data insights. 

7.2. Limitations and future studies 

 

The limitations of this study are important to acknowledge. First, while the use of a three-wave 

data collection method is a key strength that reduces the risk of common method bias, the study’s cross-

sectional design limits the ability to make causal inferences. Furthermore, while the supplementary 

qualitative phase of 12 in-depth interviews considerably strengthens interpretive depth and contextual 

confidence beyond what single-method survey studies typically offer, this qualitative component is 

modest in scale and confined to Chinese manufacturing firms in pollution-intensive industries. To address 

this, future research should consider employing longitudinal designs to establish more reliable causal 

relationships. Second, the data were collected from organizations in China, a country that is rapidly 



  

adopting emerging technologies. As a result, generalizing the findings to developing countries, which may 

lag behind in technology adoption, should be done with caution. This opens up an avenue for future 

research to explore these dynamics in different geographical and technological contexts. Further, we relied 

on managers’ data to reach our conclusions. Although collecting data from 339 managers across various 

organizations provided valuable insights, collecting data from other key stakeholders, such as employees, 

customers, or environmental experts, could provide a more holistic understanding of how AI-BDA 

influences green core competencies, as these groups may engage with sustainability initiatives in different 

ways. 

Third, the study highlights the importance of leader conscientiousness in enhancing the impact of 

AI-BDA on GDDC and green core competencies. However, future research should explore the role of 

other leadership traits and compare their influence on AI-BDA’s impact on green core competencies. 

Moreover, future studies could also examine GDDC as a potential moderating variable, rather than only a 

mediating one, to understand whether and how a strong data-driven culture may amplify or attenuate the 

impact of AI-BDA on green core competencies. Finally, while the study demonstrated strong relationships 

between AI-BDA, GDDC, and green core competencies, alternative mechanisms or explanations could 

exist. For example, AI-BDA may also influence green competencies through other factors, such as 

innovation capacity, organizational support, and green knowledge-sharing culture. In turn, these 

constructs may positively affect green core competencies. Including these variables in future research 

would offer a more nuanced understanding of how AI-BDA fosters green competencies. 

References 

[1] M. S. Akram, A. S. Goraya, A. Malik, and A. M. Aljarallah, "Organizational performance and sustainability: 

Exploring the roles of IT capabilities and knowledge management capabilities," Sustainability, vol. 10, no. 

10, pp. 3816, Oct. 2018. 

[2] H. Al Halbusi, J. E. Klobas, and T. Ramayah, "Green core competence and firm performance in a post-

conflict country, Iraq," Bus. Strategy Environ., vol. 32, no. 6, pp. 2702-2714, Jun. 2023. 

[3] M. Ali, M. Usman, M. A. S. Khan, I. Shafique, and F. Mughal, "Articulating cognizance about what to hide 



 

26 

 

 

what not: Insights into why and when ethical leadership regulates employee knowledge-hiding behaviors," 

J. Bus. Ethics, pp. 1-11, Sep. 2023. 

[4] A. W. Al-Khatib, "Can big data analytics capabilities promote a competitive advantage? Green radical 

innovation, green incremental innovation and data-driven culture in a moderated mediation model," Bus. Process 

Manag. J., vol. 28, no. 4, pp. 1025-1046, Aug. 2022. 

[5] A. W. Alkhatib, "Fostering green innovation: The roles of big data analytics capabilities and green supply 

chain integration," Eur. J. Innov. Manag., vol. 23, no. 1, pp. 1-20, Jan. 2023. 

[6] U. Awan, P. Braathen, and L. Hannola, "When and how the implementation of green human resource 

management and data-driven culture improve firm sustainable environmental development?" Sustainable 

Develop., vol. 31, no. 4, pp. 2726-2740, Jul. 2023. 

[7] G. Azzone and G. Noci, "Identifying effective PMSs for the deployment of 'green' manufacturing strategies," 

Int. J. Oper. Prod. Manag., vol. 18, no. 4, pp. 308-335, Apr. 1998. 

[8] M. T. Babalola, M. C. Bligh, B. Ogunfowora, L. Guo, and O. A. Garba, "The mind is willing, but the situation 

constrains: Why and when leader conscientiousness relates to ethical leadership," J. Bus. Ethics, vol. 155, 

no. 1, pp. 75–89, Jan. 2019. [Online]. Available: https://doi.org/10.1007/S10551-017-3524-4/TABLES/4 

[9] S. Bag, M. S. Rahman, G. Srivastava, A. Shore, and P. Ram, "Examining the role of virtue ethics and big 

data in enhancing viable, sustainable, and digital supply chain performance," Technol. Forecast. Soc. Chang., 

vol. 186, pp. 122154, Apr. 2023. 

[10] S. Benzidia, N. Makaoui, and O. Bentahar, "The impact of big data analytics and artificial intelligence on 

green supply chain process integration and hospital environmental performance," Technol. Forecast. Soc. 

Chang., vol. 165, pp. 120557, Feb. 2021. 

[11] G. Blickle, J. A. Meurs, A. Wihler, C. Ewen, and A. K. Peiseler, "Leader inquisitiveness, political skill, and 

follower attributions of leader charisma and effectiveness: Test of a moderated mediation model," Int. J. Sel. 

Assess., vol. 22, no. 3, pp. 272–285, Sep. 2014. [Online]. Available: https://doi.org/10.1111/IJSA.12076 

[12] N. A. Bowling and K. J. Eschleman, "Employee personality as a moderator of the relationships between work 

stressors and counterproductive work behavior," J. Occup. Health Psychol., vol. 15, no. 1, pp. 91-103, Jan. 

2010. 

[13] S. Chatterjee, R. Chaudhuri, S. Gupta, U. Sivarajah, and S. Bag, "Assessing the impact of big data analytics 

on decision-making processes, forecasting, and performance of a firm," Technol. Forecast. Soc. Chang., vol. 

196, pp. 122824, Oct. 2023. 

[14] R. Chaudhuri, S. Chatterjee, M. M. Mariani, and S. F. Wamba, "Assessing the influence of emerging 

technologies on organizational data driven culture and innovation capabilities: A sustainability performance 

perspective," Technol. Forecast. Soc. Chang., vol. 200, pp. 123165, Jan. 2024. 

[15] Y. S. Chen, "The driver of green innovation and green image–green core competence," J. Bus. Ethics, vol. 

https://doi.org/10.1007/S10551-017-3524-4/TABLES/4
https://doi.org/10.1111/IJSA.12076


  

81, pp. 531-543, Apr. 2008. 

[16] Z. Chen and M. Liang, "How do external and internal factors drive green innovation practices under the 

influence of big data analytics capability: Evidence from China," J. Cleaner Prod., vol. 404, Jan. 2023. 

[Online]. Available: https://doi.org/10.1016/j.jclepro.2023.136862 

[17] P. T. Costa and R. R. McCrae, "Normal personality assessment in clinical practice: The NEO personality 

inventory," Psychol. Assess., vol. 4, no. 1, pp. 5–13, Mar. 1992. [Online].Available: 

https://doi.org/10.1037/1040-3590.4.1.5 

[18] T. H. Davenport and R. Bean, "Big companies are embracing analytics, but most still don’t have a data-driven 

culture," Harv. Bus. Rev., vol. 6, pp. 1-4, Jan. 2018. 

[19] M. B. Donnellan, F. L. Oswald, B. M. Baird, and R. E. Lucas, "The mini-IPIP scales: Tiny-yet-effective 

measures of the big five factors of personality," Psychol. Assess., vol. 18, no. 2, pp. 192, Jun. 2006. 

[20] Y. Duan, G. Cao, and J. S. Edwards, "Understanding the impact of business analytics on innovation," Eur. J. 

Oper. Res., vol. 281, no. 3, pp. 673-686, Jun. 2020. 

[21] R. Dubey, D. J. Bryde, Y. K. Dwivedi, G. Graham, and C. Foropon, "Impact of artificial intelligence-driven 

big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view," Int. 

J. Prod. Econ., vol. 250, pp. 108618, Dec. 2022. 

[22] H. Al Halbusi, P. Soto-Acosta, S. Popa, and A. Hassani, “The role of green digital learning orientation and 

big data analytics in the green innovation–sustainable performance relationship,” IEEE Trans. Eng. Manage., 

vol. 71, pp. 12886-12896, Aug. 2024. 

[23] A. J. DuBrin, Fundamental of Organizational Behavior, 6th ed., Academic Media Solutions, USA, 2019. 

[24] R. B. Duncan, "Characteristics of organizational environments and perceived environmental uncertainty," 

Admin. Sci. Q., vol. 17, no. 3, pp. 313-327, Sep. 1972. 

[25] Y. K. Dwivedi et al., "Evolution of artificial intelligence research in Technological Forecasting and Social 

Change: Research topics, trends, and future directions," Technol. Forecast. Soc. Chang., vol. 192, pp. 122579, 

Mar. 2023. 

[26] D. S. Elenkov, "Strategic uncertainty and environmental scanning: The case for institutional influences on 

scanning behavior," Strateg. Manag. J., vol. 18, no. 4, pp. 287-302, Apr. 1997. 

[27] A. N. El-Kassar and S. K. Singh, "Green innovation and organizational performance: The influence of big 

data and the moderating role of management commitment and HR practices," Technol. Forecast. Soc. Chang., 

vol. 144, pp. 483-498, May 2019. 

[28] J. R. Galbraith, Designing Complex Organizations, Addison-Wesley Pub. Co., Reading, MA, 1973. 

[29] J. R. Galbraith, Organization Design, Addison-Wesley Pub. Co., Reading, MA, 1977. 

[30] K. Govindan, "How artificial intelligence drives sustainable frugal innovation: A multitheoretical 

perspective," IEEE Trans. Eng. Manag., vol. 71, pp. 638-655, Oct. 2022. 

https://doi.org/10.1016/j.jclepro.2023.136862
https://doi.org/10.1037/1040-3590.4.1.5


 

28 

 

 

[31] M. Gupta and J. F. George, "Toward the development of a big data analytics capability," Inf.Manag., vol. 53, 

no. 8, pp. 1049-1064, Dec. 2016.  

[32] M. Gurlek and M. Tuna, "Reinforcing competitive advantage through green organizational culture and green 

innovation," Serv. Ind. J., vol. 38, no. 7-8, pp. 467-491, Aug. 2018. 

[33] M. Han, H. Lin, D. Sun, J. Wang, and J. Yuan, "The eco-friendly side of analyst coverage: The case of green 

innovation," IEEE Trans. Eng. Manag., vol. 71, pp. 1007-1022, Sep. 2022. 

[34] M. Haverila, E. Li, J. C. Twyford, and C. McLaughlin, "The quality of big data marketing analytics (BDMA), 

user satisfaction, value for money and reinvestment intentions of marketing professionals," J. Syst. Inf. Technol., vol. 

25, no. 1, pp. 30-52, Jan. 2023. 

[35] K. Jin, Z. Z. Zhong, and E. Y. Zhao, "Sustainable digital marketing under big data: An AI random forest 

model approach," IEEE Trans. Eng. Manag., vol. 71, pp. 3566-3579, Apr. 2024. 

[36] H. Kamyab et al., "The latest innovative avenues for the utilization of artificial intelligence and big data 

analytics in water resource management," Results Eng., vol. 101566, Jan. 2023. 

[37] M. Kowalczyk and P. Buxmann, "Big data and information processing in organizational decision processes: 

A multiple case study," Bus. Inf. Syst. Eng., vol. 6, pp. 267-278, Sep. 2014. 

[38] Y. H. Kuo and A. Kusiak, "From data to big data in production research: The past and future trends," Int. J. 

Prod. Res., vol. 57, no. 15-16, pp. 4828-4853, Aug. 2019. 

[39] L. Li, S. Shan, J. Dai, W. Che, and Y. Shou, "The impact of green supply chain management on green 

innovation: A meta-analysis from the inter-organizational learning perspective," Int. J. Prod. Econ., vol. 250, 

pp. 108622, Dec. 2022 

[40] A. McAfee, E. Brynjolfsson, T. H. Davenport, D. J. Patil, and D. Barton, "Big data: The management 

revolution," Harv. Bus. Rev., vol. 90, no. 10, pp. 60-68, Oct. 2012. 

[41] P. Mikalef, I. O. Pappas, J. Krogstie, and M. Giannakos, "Big data analytics capabilities: A systematic 

literature review and research agenda," Inf. Syst. e-Business Manag., vol. 16, pp. 547-578, Sep. 2018. 

[42] T. D. Oesterreich, E. Anton, and F. Teuteberg, "What translates big data into business value? A 

meta-analysis of the impacts of business analytics on firm performance," Inf. Manag., vol. 59, no. 6, pp. 

103685, Nov. 2022.  

[43] W. Omri, J. M. Courrent, and A. Nemeh, "To go or to not go green for SMEs: Toward the twinning 

effect of ecodesign practices and radical innovativeness on SME performance," IEEE Trans. Eng. 

Manag., vol. 71, pp. 6370-6381, Nov. 2023. 

[44] V. M. Papadakis, S. Lioukas, and D. Chambers, "Strategic decision-making processes: The role 

of management and context," Strateg. Manag. J., vol. 19, no. 2, pp. 115-147, Feb. 1998. 

[45] D. X. Peng, G. R. Heim, and D. N. Mallick, "Collaborative product development: The effect of 

project complexity on the use of information technology tools and new product development 



  

practices," Prod. Oper. Manag., vol. 23, no. 8, pp. 1421-1438, Dec. 2014. 

[46] C. K. Prahalad and G. Hamel, "The core competence of the corporation," Harv. Bus. Rev., vol. 

90, no. 10, pp. 79-91, Oct. 1990. 

[47] X. Qu, A. Khan, S. Yahya, A. U. Zafar, and M. Shahzad, "Green core competencies to prompt 

green absorptive capacity and bolster green innovation: The moderating role of organization’s 

green culture," J. Environ. Plan. Manag., vol. 65, no. 3, pp. 536-561, Mar. 2022. 

[48] M. Saeed, Z. Adiguzel, I. Shafique, M. N. Kalyar, and D. B. Abrudan, "Big data analytics-enabled 

dynamic capabilities and firm performance: Examining the roles of marketing ambidexterity and 

environmental dynamism," Bus. Process Manag. J., vol. 29, no. 4, pp. 1204-1226, Jul. 2023. 

[49] T. A. Saleh, A. Sarwar, M. A. Islam, M. Mohiuddin, and Z. Su, "Effects of leader 

conscientiousness and ethical leadership on employee turnover intention: The mediating role of 

individual ethical climate and emotional exhaustion," Int. J. Environ. Res. Public Health, vol. 19, 

no. 15, pp. 8959, Aug. 2022. 

[50] D. Wu, X. Lin, S. Gupta, and A. K. Kar, “Big data analytics capability, dynamic capability, and 

firm performance: the moderating effect of it–business strategic alignment,” IEEE Trans. Eng. 

Manage., vol. 71, pp. 11638-11651, Jul. 2024. 

[51] H. Shi, T. Feng, and Z. Zhu, "The impact of big data analytics capability on green supply chain 

integration: An organizational information processing theory perspective," Bus. Process Manag. 

J., vol. 29, no. 2, pp. 550-577, Mar. 2023. 

[52] N. Shukla, M. K. Tiwari, and G. Beydoun, "Next generation smart manufacturing and service 

systems using big data analytics," Comput. Ind. Eng., vol. 128, pp. 905-910, Nov. 2019. 

[53] R. Srinivasan and M. Swink, "Leveraging supply chain integration through planning 

comprehensiveness: An organizational information processing theory perspective," Decis. Sci., 

vol. 46, no. 5, pp. 823-861, Dec. 2015. 

[54] L. Tan et al., "Conscientiousness and leader emergence: The mediating role of functional 

behaviors," J. Manag. Psychol., vol. 38, no. 5, pp. 319–337, Jun. 2023. 

[55] F. Tao, Q. Qi, A. Liu, and A. Kusiak, "Data-driven smart manufacturing," J. Manuf. Syst., vol. 48, 

pp. 157-168, Apr. 2021. 

[56] M. L. Tushman and D. A. Nadler, "Information processing as an integrating concept in 

organizational design," Acad. Manag. Rev., vol. 3, no. 3, pp. 613-624, Jul. 1978. 

[57] A. Vafaei-Zadeh, J. Madhuri, H. Hanifah, and R. Thurasamy, "The interactive effects of 

capabilities and data-driven culture on sustained competitive advantage," IEEE Trans. Eng. 

Manag., vol. 71, pp. 1204-1222, Jan. 2024. 

[58] S. F. Wamba, M. M. Queiroz, and L. Trinchera, "The role of artificial intelligence-enabled 



 

30 

 

 

dynamic capability on environmental performance: The mediation effect of a data-driven culture 

in France and the USA," Int. J. Prod. Econ., vol. 268, pp. 109131, Apr. 2024. 

[59] C. H. Wang, "How organizational green culture influences green performance and competitive 

advantage: The mediating role of green innovation," J. Manuf. Technol. Manag., vol. 30, no. 4, 

pp. 666-683, Jul. 2019. 

[60] M. Waqas, X. Honggang, N. Ahmad, S. A. R. Khan, and M. Iqbal, "Big data analytics as a 

roadmap towards green innovation, competitive advantage and environmental performance," J. 

Clean. Prod., vol. 323, pp. 124169, Dec. 2021. 

[61] C. Wijethilake, B. Upadhaya, and T. Lama, "The role of organizational culture in organizational 

change towards sustainability: Evidence from the garment manufacturing industry," Prod. Plan. 

Control, vol. 34, no. 3, pp. 275-294, Apr. 2023. 

[62] C. Y. Wong, C. W. Wong, and S. Boon-itt, "Effects of green supply chain integration and green 

innovation on environmental and cost performance," Int. J. Prod. Res., vol. 58, no. 15, pp. 4589-

4609, Aug. 2020. 

[63] W. Yu, C. Y. Wong, R. Chavez, and M. A. Jacobs, "Integrating big data analytics into supply 

chain finance: The roles of information processing and data-driven culture," Int. J. Prod. Econ., 

vol. 236, pp. 108135, Jul. 2021. 

[64] W. Zhang, W. Zhang, and T. U. Daim, "The voluntary green behavior in green technology 

innovation: The dual effects of green human resource management system and leader green traits," 

J. Bus. Res., vol. 165, pp. 114049, Sep. 2023. 

[65] E. Raguseo and C. Vitari, "Investments in big data analytics and firm performance: An empirical 

investigation of direct and mediating effects," Int. J. Prod. Res., vol. 56, no. 15, pp. 5206-5221, 

Aug. 2018. 

[66] R. Dubey, A. Gunasekaran, and S. J. Childe, "Big data analytics capability in supply chain agility: 

The moderating effect of organizational flexibility," Manag. Decis., vol. 57, no. 8, pp. 2092-2112, 

Aug. 2019. 

[67] H. Le and K. C. Vu, "Big data analytics and environmental performance: The moderating role of 

internationalization," Finance Res. Lett., vol. 64, 105484, Jan. 2024. 

[68] P. Mikalef, M. Boura, G. Lekakos, and J. Krogstie, "Big data analytics and firm performance: 

Findings from a mixed-method approach," J. Bus. Res., vol. 98, pp. 261-276, Jan. 2019. 

[69] H. Wang, C. Pan, Q. Wang, and P. Zhou, "Assessing sustainability performance of global supply 

chains: An input-output modeling approach," Eur. J. Oper. Res., vol. 285, no. 1, pp. 393-404, Feb. 

2020. 

[70] M. E. Brown and L. K. Treviño, "Ethical leadership: A review and future directions," The 



  

Leadership Quarterly, vol. 17, no. 6, pp. 595-616, Dec. 2006. 

[71] R. Dubey, A. Gunasekaran, S. J. Childe, T. Papadopoulos, Z. Luo, S. F. Wamba, and D. Roubaud, 

"Can big data and predictive analytics improve social and environmental sustainability?" Technol. 

Forecast. Soc. Chang., vol. 144, pp. 534-545, May 2019. 

[72] D. J. Teece, G. Pisano, and A. Shuen, "Dynamic capabilities and strategic management," Strateg. 

Manag. J., vol. 18, no. 7, pp. 509-533, Aug. 1997. 

[73] P. Jorzik, A. Yigit, D. K. Kanbach, S. Kraus, and M. Dabic, “Artificial intelligence-enabled 

business model innovation: Competencies and roles of top management,” IEEE Trans. Eng. 

Manage., vol. 71, pp. 7044-7056, Apr. 2024. 

[74] G. Yukl, "How leaders influence organizational effectiveness," The Leadership Quarterly, vol. 19, 

no. 6, pp. 708-722, Dec. 2008. 

[75] B. J. Hartmann, "Peeking behind the mask of the prosumer: Theorizing the organization of 

consumptive and productive practice moments," Mark. Theory, vol. 16, no. 1, pp. 3-20, Mar. 2016. 

[76] S. K. S. Wong, "Environmental requirements, knowledge sharing and green innovation: Empirical 

evidence from the electronics industry in China," Bus. Strategy Environ., vol. 22, no. 5, pp. 321-

338, Jul. 2013. 

[77] C. T. Chen, S. C. Chen, A. Khan, M. K. Lim, and M. L. Tseng, "Big data analytics-artificial 

intelligence and supply chain ambidexterity impacts on corporate image and green 

communication," Ind. Manag. Data Syst., vol. 124, no. 10, pp. 2899-2918, Aug. 2024. 

[78] S. Chatterjee, R. Chaudhuri, S. Kamble, S. Gupta, and U. Sivarajah, "Adoption of artificial 

intelligence and cutting-edge technologies for production system sustainability: a moderator-

mediation analysis," Inf. Syst. Front., vol. 25, no. 5, pp. 1779-1794, 2023. 

[79] B. E. Kaufman, "The RBV theory foundation of strategic HRM: Critical flaws, problems for 

research and practice, and an alternative economics paradigm," Hum. Resour. Manag. J., vol. 25, 

no. 4, pp. 516-540, 2015. 

[80] G. P. Huber, "Organizational learning: The contributing processes and the literatures," Organ. 

Sci., vol. 2, no. 1, pp. 88-115, 1991. 

[81] D. Cabrera, L. Cabrera, E. Powers, J. Solin, and J. Kushner, "Applying systems thinking models 

of organizational design and change in community operational research," Eur. J. Oper. Res., vol. 

268, no. 3, pp. 932-945, 2018. 

[82] P. L. Wu, S. S. Yeh, and A. G. Woodside, "Applying complexity theory to deepen service 



 

32 

 

 

dominant logic: Configural analysis of customer experience-and-outcome assessments of 

professional services for personal transformations," J. Bus. Res., vol. 67, no. 8, pp. 1647-1670, 

2014. 

[83] Plano Clark, V. L. (2017). Mixed methods research. The Journal of Positive Psychology, 12(3), 305-306. 

[84] V. Braun and V. Clarke, "Using thematic analysis in psychology," Qual. Res. Psychol., vol. 3, no. 2, pp. 

77–101, 2006. 

 

 

 

 

 

 

 



  

Table 1. Means and correlations 
 

 

Construct Mean SD 1 2 3 4 5 6 7 

1. AI-BDA 3.37 1.27        

2. GDDC 3.15 1.17 .26**       

3. Green core competencies 3.19 1.14 .25** .43**      

4. Leader conscientiousness 3.38 1.19 .23** .13* .02     

5 Green knowledge-sharing 2.76 1.07 .14** .22** .33** .04    

6. Firm age 34.15 8.20 .04 .11* -.01 .01 -.06   

7. Firm size   .12* -.05 .08 .00 -.03 -.08  

Notes. N = 339. * p <.05. ** p <.01 level (2-tailed). SD = standard deviation AI-BDA = AI-supported big data analytics capabilities. GDDC = 

Green data-driven decision-making culture. 

 

 

Table 2. Discriminant validity and convergent validity 

 

Construct 1 2 3 4 5 AVE MSV ASV 

1. AI-BDA .80     .64 .09 .08 

2. GDDC .30 .77 
   

.59 .25 .12 

3. Green core competencies .29 .50 .75 
  

.57 .25 .11 

4. Leader conscientiousness .26 .16 .03 .79 
 

.63 .07 .03 

5 Green knowledge sharing culture .15 .20 .37 .01 .73 .54 .14 
 

Notes. N = 339. AVE = average variance extracted. MSV = maximum variance shared. ASV = average variance shared. Bolded values on the 

diagonals of columns 2 to 5 are the square root values of AVE. AI-BDA = AI-supported big data analytics capabilities. GDDC = Green data-

driven decision-making culture. 

 

  



 

34 

 

 

Table 3. Hypotheses results 

 

Variables 
 

GDDC 
  

GCC 
 

 
B (SE) 

p-

value 
CI (95%) B (SE) 

p-

value 
CI (95%) 

AI-BDA .22(.05) .00 .13, .32 .23(.05) .00 .11, .31 

GDDC 
   

.40(.05) .00 .30, .50 

Leader conscientiousness .08(.05) .15 -.03, .18 -.03(.05) .53 -.13, .07 

Green knowledge-sharing culture .20(.06) .00 .09, .32 .25(.05) .00 .15, .35 

Firm age .01(.003) .08 -.001, .01 -.001(.003) .73 -.01, .005 

Firm size -.08(.05) .15 -.18, .03 .04(.05) .42 -.06, .15 

Interaction .09(.04) .01 .02, .17 
   

    
B (SE) 

P- 

value 
CI (95%) 

Indirect effect of AI-BDA on GCC via GDDC 
  

.09(.03) .02 .05, .15 

Conditional effects 
      

Conditional direct effect of AI-BDA on GDDC (on low leader 

conscientiousness) 

 
.13(.06) .04 .01, .25 

Conditional direct effect of AI-BDA on GDDC (on high leader 

conscientiousness) 

 
.35(.07) .00 .21, .49 

Notes: *p <.05. **p <.01. Sample size (N) = 339. AI-BDA = AI-supported big data analytics capabilities. GDDC = Green data-driven decision-

making culture. GCC = Green core competencies. B = Unstandardized coefficient, SE = standard error, CI = Confidence interval. Bootstrapping 

was specified at 5000 with 95% confidence interval 



  

 

 

 

 

 

 
 

 

Figure 1. The proposed model 
 

 

 

Figure 2. Leader conscientiousness as a moderator of the AI-BDA on GDDC 
  



 

 

Appendix A: Semi-Structured Interview Protocol 

1. Can you walk me through a recent example of how your company uses AI or big data analytics tools for 

environmental or sustainability decisions? 

2. In daily operations, how commonly are environmental decisions made based on data analysis rather than 

experience or intuition? 

3. What specific practices does your organization use to encourage employees to base green decisions on 

data (e.g., training, KPIs)? 

4. To what extent do you believe your firm’s environmental technologies, processes, or know-how are 

difficult for competitors to copy? Please give an example. 

5. Thinking of the senior leaders you work with most closely, how would you describe their style regarding 

planning, follow-through, and attention to detail? 

6. Do more conscientious/diligent leaders behave differently when rolling out AI-based big data analytics 

for sustainability initiatives? How? 

7. What have been the biggest enablers and barriers in turning AI-based big data analytics insights into 

actual green competitive advantages? 

8. Is there anything else you would like to add about the role of data analytics and leadership in building 

green capabilities? 

  



  

 

Appendix C: Summary of qualitative themes 

 

Theme Description & Illustrative Quotations Link to Model 

1. AI-BDA as an enabler of 

real-time green insight 

“Our AI system analyses dyeing machine 

data every minute and automatically adjusts 

temperature and chemical dosage. Last year, 

we were able to reduce water by 22% and 

chemicals 18%.” (Plant Manager of a textile 

company) 

Direct effect (H1) 

2. GDDC as the cultural 

bridge 

“Only the plants where the factory head 

starts every morning meeting with ‘Show 

me the data’ actually improved. In the 

others, people still decide by experience.”  

(Operations Director of a leather company) 

Mediation via GDDC (H2) 

3. Conscientious leaders drive 

adoption and accountability 

“Our group president is detail-oriented and 

disciplined. Every month, he would 

personally review the ESG dashboard with 

each factory head and ask for evidence-

based explanations. He is like that; the 

entire organization has become data-driven 

on green issues.” (Sustainability Director of 

a large leather group)  

Moderation (H3) 

4. Rarity stems from 

proprietary data loops 

“Competitors can buy the same software 

tomorrow, but they cannot buy our eight 

years of historical sensor data tied to every 

recipe.” (R&D head of ceramics company) 

Explains GCC 

rarity/inimitability 

5. Barriers 

Middle-management resistance, data silos, 

shortage of dual-competence (Digital 

Operations Manager of a textile company)