Nexus between organizational learning capability and organization age on corporate environmental citizenship

Abstract

BACKGROUND:

Many organizations around the world have prudently adopted corporate environmental citizenship. However, the corporate environmental citizenship implementation may vary from reality. Thus, this study examines corporate environmental citizenship to identify ultimate practices to create a strong premise of CEC.

OBJECTIVE:

The study examines the influence of organizational learning capability, organization age on corporate environmental citizenship.

METHODS:

The data were collected from 50 Malaysian construction firms using the survey questionnaire and analyzed by using Partial Least Square Structural Equation Modeling (PLS-SEM).

RESULTS:

The finding revealed that organizational learning capability positively related to corporate environmental citizenship. Organization age was not found to moderate such relationships.

CONCLUSION:

This study establishes that organizational learning capability encourages construction firms to take risks and explore new opportunities are essential for corporate environmental citizenship implementation. This study highlights the role of organizational learning capability to achieve corporate environmental citizenship irrespective of their organization age for construction firms. This study confirms the logic of Natural Resource Based View (NRBV) theory for predicting organizational learning capability as a critical foundation to build corporate environmental citizenship.

Keywords

Organizational learning capability corporate environmental citizenship organization age socioemotional selectivity theory natural resource-based view theory Sustainable Development Goal (SDG) 12

1 Introduction

Organizations are the biggest culprit for irregular weather patterns or ecological problems due to the large quantity of waste generated and consumption of resources [1]. In response to environmental demands from the community [2], many organizations around the world have prudently adopted Corporate Environmental Citizenship (CEC) i.e., organizations acknowledge the importance of the environment in their organizational strategy and the strategic planning processes to preserve the natural environment [3].

Indeed, organizations have taken significant initiatives to develop climate change policies, clean energy processes and sustainable practices in their business [4]. However, despite all these initiatives, ecological problems escalate, leaving the key unanswered question of whether CEC changes organizations to solve ecological problems. Organizations often used CEC as a symbol without serious changes and adaptation of their actions [5]. This is possible since it is usually assumed that an organization’s motivation for CEC efforts is to obtain legitimacy from stakeholders such as suppliers, customers and the community [1, 2]. If so, the CEC implementation may vary from reality, causing inconsistency between the expected and actual actions. On this basis, this study examines CEC to identify ultimate practices to create a strong premise of CEC, offering crucial information on key practices to ensure successful implementation of CEC.

Besides, the implementation of CEC is complicated and deals with various difficulties [6, 7]. In this case, Organizational Learning Capability (OLC) i.e., organizational characteristics that facilitate the organizational learning process [8, 9] are needed to learn environmental behavior and interpret the situation in an environmentally friendly approach to pursue CEC [10]. OLC also happens when it embeds in organizations memory such as values, norms and cultures to guide the organizations including environmental responsibility into the business operation process [11]. Nevertheless, little research to date has explored how OLC contributes to CEC. Prior studies have mainly been explored conceptually and empirically which are confined to case studies [6, 7, 10]. It causes poor understanding of the relevance of OLC in achieving CEC. Agyabeng-Mensah [12] claims how organizational learning (e.g., OLC) fosters corporate social responsibility (e.g., CEC) remains partially understood. Therefore, there is a need for more clarity about this connection. To overcome this gap, this study investigates the influence of OLC on CEC.

Additionally, the role of organization age i.e., the operation years of an organization considered from the start time to date [13] as a moderator between OLC and CEC remains unexplained because organization age is widely found in the firm performance and innovation studies [14, 15]. This study proposes that organization age is a useful moderator to understanding when and how OLC might influence CEC. Organization age shows the ability, experience and capability to implement CEC. Cincalova [16] argued that mature organizations realize more than young organizations about the value of CEC to attract investors and create a good image. Conversely, when organizations have fewer years of operations, organizations have limited experience with the external environment and lack of know-how to integrate environmental practices into daily business i.e., CEC [17]. Therefore, this part needs further empirical investigation in this study, particularly in relation to the moderating effect of organization age between OLC and CEC. In summary, this paper makes several contributions. First, this study provides a better explanation of the impact of OLC on CEC which brings useful insight for future practice and theory. Second, this study contributes to the literature by analyzing the relationship between OLC, CEC and the moderating role of organization age, which is new evidence in the literature. Third, both mature and young organizations enable us to develop relevant and appropriate CEC strategies and directions based on the findings. This paper is organized as follows: first, introduce the conceptual background of OLC, CEC and the hypothesis development discussions. Next, the paper presents the research methodology followed by findings. Finally, this paper highlights the finding discussion, future research agenda and study limitations in the conclusion.

2 Literature review

2.1 OLC and CEC

Natural Resource-Based View (NRBV) theory is well-suited to explaining the relationship between OLC and CEC. Referring to Hart [18], environmental practices minimize carbon footprints, waste and improve efficiency. Acknowledging the importance of environmental practices to achieve a sustainable competitive advantage, Hart [18] suggests NRBV theory by incorporating three strategic capabilities namely pollution prevention (reduce waste and emissions), product stewardship (life cycle cost of products) and clean development (reduce environmental impacts results from firm growth).

Under natural resource-based view theory, OLC is consistent with the assumption of NRBV theory. By using OLC, organizations can obtain strategic capabilities, which in turn lead to a sustainable competitive advantage. It involves organizations learning from experience and inquiry for environmental issues such as looking into products’ life cycle process from raw material extraction until the final disposal to minimize or prevent the environmental footprint. OLC also entails organizations to take risk in investing future technologies in pursuit of environmental practices, which deliver positive environmental impacts over the long term. As such, it may highly influence organization adoption of CEC. Organizations have significant cost savings, productivity and efficiency compared to competitors [19].

While earlier research has attempted to identify the relationship between OLC and CEC [6, 9], little has been done to determine if organizations’ CEC implementation is associated with higher organizational learning. Experimentation (i.e., sub-dimension that forms OLC) involves trying new ideas and suggestions [8]. Experimentation is closely associated with organizations’ willingness to evaluate existing knowledge, beliefs, routines and assume the need to try new environmental knowledge [19]. That is, progressive experimentation eases critical reviews of self and others questioning practices and contributes to an enhanced CEC. Further, organizations with learning capability characteristic is an open system where organizations regularly interact and exchange resources or feedback with the external environment i.e., customer, suppliers, government agencies [9]. Employees benefit from these interactions and exchange, adapting their work practice or style based on the resources or feedback obtained [7]. This facilitates organizations to address changes in the external environment, concern the stakeholder needs, integrate stakeholders’ suggestions in practice, warranting the practices always aligned to their needs [7]. All these are essential for CEC implementation. For instance, organizations launch environmental friendly innovation in production, packaging and products when organizations learn that the public is environmentally conscious using customer surveys. Such interactions build networks with the public, gain insights on environmentally friendly innovation, enhancing CEC [7].

Additionally, Babiak [20] highlights that organizations engaged in stakeholder dialogue can obtain an understanding of various stakeholders’ environmental demands, perceptions and values. Every stakeholder perceives an organization has an obligation to protect the natural environment and expect organizations to invest in CEC [20]. In turn, organizations can identify the challenges and opportunities in the development of CEC [20]. In a similar vein, stakeholder dialogue helps identify organizations’ limits and their place on CEC issues. Nowadays, stakeholders demand more on organizations’ transparency not only on products but also on environmental values and commitment [22]. Therefore, organizations are expected to justify their business decisions and environmental footprints [22]. At the end, organizations monitor stakeholders and scrutinize the business environment in which strategic CEC decisions are made [23]. Thus, it enhances the quality and effectiveness of CEC decisions.

Along similar lines, CEC is reinforced by employees’ participative decision making. While employees realize themselves as part of determining environmental programs and activities, differences about what to do and how to do concerning environmental processes will be reduced [23]. Thus, a high level of employees’ dedication, passion and interest in environmental programs and activities are more likely to be demonstrated [23]. Subsequently, employees recognize environmental goals, accept environmental innovation, intervention and requirements while solving various environmental problems [25] leading CEC. Organizations can find ways to create two-way communication regularly that focus on the need for advancing CEC will improve employees’ awareness of their significant environmental contributions [26]. Thus, it is hypothesized that:

H₁: OLC positively influences CEC.

2.2 The moderating effect of organization age

Drawing upon socioemotional selectivity theory (SEST), this study assumes that the relationship between OLC and CEC relies on the organizational characteristic i.e., organization age. Specifically, older organizations prefer to spend their time with familiar organizations to share positive relationships [27, 28]. In this context, CEC arises from older organizations’ desire to strengthen relationships due to positive returns from the relationships. Therefore, older organizations practice CEC to gain reputation, better financial performance benefits in the long term when external stakeholders view organizations socially responsible [29], causing customers to feel proud of purchasing products and services engaged by CEC, investors willing to invest by purchasing stock organizations [30].

In a similar vein, organizations’ resources, capacities and abilities change and develop over time [19]. Older firms have more established environmental routines and relevant approaches for engaging in environmental innovations [18]. From this viewpoint, older organizations have extensive experience and expertise in pursuit of CEC. In contrast, younger organizations have limited exposure to environmental innovations [18] and lead to fewer experiences involving CEC. Further, from the financial aspect, stable profitability and predictable cash flows allow older firms to invest in CEC activities [17, 31]. However, younger firms have uncertain cash flows, hence they prefer to focus on survival and lack of resources to invest in CEC activities [17, 31]. Additionally, external stakeholders may be unwilling to associate with younger organizations, making them harder to involve in environmental innovation projects [31]. This may not achieve CEC, given that they have limited stable links and legitimacy in the eyes of external stakeholders whom they associate with [31]. In contrast, as an older organization, its CEC history and reputation enhances external stakeholders’ confidence and expectations about environmental project sponsorship [31]. It causes older organizations very hard to withdraw from CEC activities.

Besides, older organizations have long survived to develop a sense of belonging to the community where they operate, expecting to be socially responsible citizens [29, 32] by engaging CEC. Socially responsible creates older organizations’ reputation among customers and potential customers, who in turn prefer to buy their products and services, strengthening their financial performance in the future [29, 32]. This is different from younger organizations who are not close to the business environment and community where they operate, leading younger organizations are not always interested in acting as good citizens by practicing CEC [29, 32]. Along similar lines, younger organizations never realize positive returns (e.g., goodwill, higher financial performance) of CEC [29]. For instance, younger organizations have little emphasis on communicating environmental activities (e.g., setting up a department of environment, preparing environmental reports) to external stakeholders as it is quite costly. Thus, it is hypothesized that:

H₂: Organization age moderates OLC and CEC

3 Methodology

3.1 Sample and procedure

Data were collected via questionnaire. The population is 244 construction firms in Malaysia which was retrieved from the website of Construction Industry Development Board (CIDB) [31]. Following Krecjie [34] sample size table, the adequate sample size is 169. The systematic sampling technique was employed to identify the study sample because elements in the population have a chance to be selected systematically [35]. All construction firms on the list would be chosen until 169 Grade 7 construction firms were obtained, since 244/169 = 1. Thenceforth, an invitation email describing the purpose of research and data confidentiality was sent to organization directors (i.e., target respondents). 95 organization directors expressed their willingness to participate and were given two weeks to complete the questionnaire. Among 169 questionnaires, 86 were returned 6 were incomplete due to similar responses across all items. Only 80 completes were used for subsequent analysis. The majority of respondents were Malaysian ownership construction firms (96%). Construction firms’ business activities were handled by the professional management groups (58%) and business owners (42%).

3.2 Instruments

To measure OLC, this study included 14 items from Chiva et al. (2007). The measurement has gained strong evidence of reliability and validity in prior studies [36, 37]. Further, CEC was accessed using 16 items from Banerjee [3]. The measurement has been empirically validated by various researchers [38 –40]. Respondents identify their perception levels assessing OLC and CEC on a five-points Likert scale (1 = strongly disagree to 5 = strongly agree).

Organization age was measured using the formation years [16, 41]. Following prior studies [41 –43], this study categorized the organization age into older and younger construction firms. Construction firms with less than 10 years were considered younger, while those with more than 10 years were ranked as older [17, 44]. This study employed a dichotomous measure where younger construction firms were coded “1” and older firms were coded “0”.

Next, a pilot test was performed to determine whether questionnaire instructions and item clarity made sense. The pilot test results reported OLC and CEC measurements have higher reliability values, greater than 0.70, meeting Nunally [45] minimum value. In short, the measurement was reliable.

Figure 1.

Research Model.

4 Findings

Collected data were screened against four requirements namely 1) missing data, 2) suspicious response pattern, 3) outliers, 4) common method bias [44]. This study has no suspicious response pattern and missing values. 30 responses identified outliers and removed them, leading 50 responses were used for further analysis. After that, PLS-SEM is preferred when testing a research model from a prediction angle, theory development and exploration of new phenomena, especially when the research area is emerging and dynamic. Hence, referring to the study’s objectives PLS-SEM is chosen and executed using SmartPLS 4.0 software. Additionally, both CEC and OLC are multidimensional constructs, where items reflectively measured Lower-Order Constructs (LOCs) while relevant variables are formatively measured Higher-Order Constructs (HOCs). PLS-SEM supports multidimensional constructs because it captures the complexity of multidimensional constructs in the research model.

4.1 Measurement model

4.1.1 Reflective measurement model

From Table 1, item loadings exceeding the threshold value i.e., 0.7 except EEO1, EEO4, DG4 were removed as the outer loadings were below 0.40 [46]. Removing these items does not change the meaning of external environmental orientation (EEO) and dialogue constructs because items are the reflection of the constructs [47]. From Table 1, the CR of the constructs are close to the acceptable threshold value of 0.7 [48, 49], thus providing higher levels of reliability. The results exhibit Average Variance Extracted (AVE) values above 0.50, explaining 50% or more item variance that form the construct [48]. As shown in Table 2, the values below HTMT _0.90 criterion. Overall, the measurement model was satisfactory.

Table 1.
Measurement Model Result.

Constructs Items Loadings CR AVE

Corporate strategic focus CSF1 0.771 0.927 0.717

CSF2 0.852

CSF3 0.900

CSF4 0.880

CSF5 0.826

Dialogue DG1 0.895 0.893 0.736

DG2 0.880

DG3 0.795

DG4 †

Experimentation EM1 0.959 0.957 0.917

EM2 0.957

External environmental orientation EEO1 † 0.951 0.906

EEO2 0.952

EEO3 0.952

EEO4 †

Functional strategic focus FSF1 0.876 0.932 0.821

FSF2 0.942

FSF3 0.900

Interaction with external environment IEE1 0.724 0.877 0.706

IEE2 0.930

IEE3 0.855

Internal environmental orientation IEO1 0.954 0.964 0.872

IEO2 0.918

IEO3 0.949

IEO4 0.913

Participative decision making PDM1 0.900 0.946 0.853

PDM2 0.926

PDM3 0.944

Risk taking RS1 0.920 0.886 0.796

RS2 0.863

Constructs	Items	Loadings	CR	AVE
Corporate strategic focus	CSF1	0.771	0.927	0.717
	CSF2	0.852
	CSF3	0.900
	CSF4	0.880
	CSF5	0.826
Dialogue	DG1	0.895	0.893	0.736
	DG2	0.880
	DG3	0.795
	DG4	†
Experimentation	EM1	0.959	0.957	0.917
	EM2	0.957
External environmental orientation	EEO1	†	0.951	0.906
	EEO2	0.952
	EEO3	0.952
	EEO4	†
Functional strategic focus	FSF1	0.876	0.932	0.821
	FSF2	0.942
	FSF3	0.900
Interaction with external environment	IEE1	0.724	0.877	0.706
	IEE2	0.930
	IEE3	0.855
Internal environmental orientation	IEO1	0.954	0.964	0.872
	IEO2	0.918
	IEO3	0.949
	IEO4	0.913
Participative decision making	PDM1	0.900	0.946	0.853
	PDM2	0.926
	PDM3	0.944
Risk taking	RS1	0.920	0.886	0.796
	RS2	0.863

Notes: CR = composite reliability, AVE = Average Variance Extracted, †= Items dropped as the loadings below 0.4.

Table 2.

Heterotrait-Monotrait Ratio (HTMT) Results.

	CSF	DG	EM	EEO	FSF	IEE	IEO	PDM
CSF
DG	0.325
EM	0.552	0.787
EEO	0.870	0.412	0.705
FSF	0.875	0.325	0.548	0.765
IEE	0.702	0.753	0.716	0.832	0.723
IEO	0.876	0.399	0.658	0.596	0.735	0.718
PDM	0.315	0.682	0.592	0.439	0.423	0.728	0.432
RS	0.649	0.765	0.676	0.668	0.726	0.861	0.652	0.541

Note: HTMT _0.90 criterion.

Table 3.

VIF, Formative Items’ Outer Weights, Outer Loadings.

Constructs	Items	VIF	t values	Outer loadings
Corporate environmental citizenship	Internal environmental orientation	1.979	3.951*	0.988
	External environmental orientation	3.479	0.030	0.978
	Corporate strategic focus	3.683	1.293	0.891
	Functional strategic focus	2.908	0.760	0.782
Organizational learning capability	Experimentation	2.661	0.273	0.764
	Risk taking	1.989	0.837	0.727
	Interaction external environment	2.236	0.943	0.879
	Dialogue	2.554	0.668	0.724
	Participative decision making	1.667	0.071	0.815

Note: *t value > 1.96.

4.1.2 Formative measurement model

The first step is evaluating OLC convergent validity [44] via redundancy analysis, providing a path coefficient of 0.81, above the threshold value 0.70, therefore supporting OLC convergent validity. Besides, the second step is examining Variance Inflation Factor (VIF) to assess formative items’ collinearity [44]. VIF values were less than 5, which means no collinearity issues in the formative items (see Table 3). Meanwhile, the third step is to assess the formative item outer weights’ statistical significance [48]. Referring to Table 3, one formative item was found to be statistically significant at p < 0.00 except external environmental orientation, corporate strategic focus, functional strategic focus, experimentation, risk taking, interaction with external environment, dialogue, participative decision making. These formative items are not necessarily illuminated as proof of poor measurement model quality although their outer weights are insignificant [46]. Alternatively, outer loading formative items were considered [46]. As exhibited in Table 3, formative item outer loadings were greater than 0.50, suggesting that these formative items remain for subsequent analysis [46].

4.2 Structural model

Firstly, the SRMR value is 0.084, less than the cut-off point of 0.10 [51] indicating the satisfactory data fitting the research model. Secondly, all VIF values were clearly below 0.50, the collinearity was eliminated. Thirdly, R² of 0.826, meaning that 82.6% of CEC variance was explained by OLC. 0.826 R² values can be considered as substantial based on Hair [46] recommendation. Fourthly, OLC has a large f² effect size of 3.66 in CEC. Fifthly, Q² value is above zero i.e., 0.574, providing the research model has predictive relevance. Sixthly, Table 4 revealed that OLC has a significant effect on CEC, thus, H₁ is supported. Alternatively, an organization’s age has no moderating effect between OLC and CEC because the confidence interval contains zero. H₂ is not supported.

Table 4.
Hypothesis Testing Result.

Hypothesis Relationship Standard Beta Standard Error t Values Confidence Interval Decisions

H₁ OLC->CEC 0.932 0.948 22.32 [0.759, 0.983] Supported

H₂ OLC Organization* –0.031 –0.054 0.322 [–0.207, 0.153] Not supported

age->CEC

Hypothesis	Relationship	Standard Beta	Standard Error	t Values	Confidence Interval	Decisions
H₁	OLC->CEC	0.932	0.948	22.32	[0.759, 0.983]	Supported
H₂	OLC Organization*	–0.031	–0.054	0.322	[–0.207, 0.153]	Not supported
	age->CEC

Note: OLC = organizational learning capability, CEC = corporate environmental citizenship, *t value > 1.96, Italic terms represent the results of moderation effect.

5 Conclusion

The finding revealed that OLC significantly related to CEC, suggesting that the construction firms under this study perceive that OLC promotes the generation of organizational competencies and abilities to deal with environmental problems and thus achieve CEC. This finding is similar to Zeimers [52] and Zhang [53]. A possible explanation is that the construction firms have embedded environmental norms, routines, codes, structure and systems to educate employees learning environmental orientation throughout the construction firms [54]. This explains that the majority of Grade 7 construction firms have environmental officers to assess construction projects’ environmental performance and review construction projects that comply with environmental regulations and client requirements [33]. As such, environmental officers educate employees via toolbox briefings and training sessions, communicating pollution control approaches and environmental goals [33]. It is more likely to enhance all workers’ competencies, abilities and CEC.

Another explanation is Grade 7 construction firms have varying numbers of experienced employees with higher level competency and the education level positively impacts OLC. It is closely related to their willingness to be involved in the process of learning new environmentally friendly knowledge and technologies. They learn via external stakeholder (e.g., competitors, customers, universities) interactions, modify work practices in line with the environmental information obtained and contribute to CEC. Additionally, the finding showed that organization age does not moderate OLC and CEC, providing the age of construction firms under this study has no impact on CEC action. This means that young construction firms did not engage less in CEC. This conforms to Cincalova [16] and Badulescu [17] who never associate corporate social responsibility (e.g., CEC) action with firm age. A possible justification is given the construction industry, CEC enhancement not only depends on organization age, but on other critical factors related to CEC such as government support [55, 56], suppliers’ collaboration [57, 58].

5.1 Research implications

This finding contributes to the OLC and CEC literature in several ways. Specifically, this study confirms that the logic of NRBV theory for predicting OLC creates a critical foundation to build CEC. This is where organizations take the risk of investing in environmental innovation, making sure to minimize the use of natural resources and toxic release [20, 59]. Thus, organizations’ interest in CEC increased, adding value to reputation and bigger market demand [60, 61]. This finding further advances the understanding of OLC and CEC relationships. Thus, it can be used as a future reference for exploring the relationship between organizational learning and environmental related studies. Besides, this study adds new proof on the relationship between OLC and CEC by investigating the moderating role of organization age, which warrants fresh perspectives in the literature. Moreover, this study does not confirm that organization age moderates the association between OLC and CEC, contradicting SEST theory [27, 28]. As such, future studies explore whether OLC and CEC are moderated by organization age. This finding reminds the construction firms of the importance of OLC in pursuing CEC. Construction firms could enhance the environmental learning efforts by developing shared knowledge among the employees.

The employees may use the shared knowledge to access environmental opportunities and search for environmental initiatives for the construction firms. The results of this study offer insights for government agencies, especially CIDB to support CEC in the construction industry. CIDB can exhibit commitment to CEC by offering resources necessary for CEC success. For example, CIDB determines the strategic decisions to ensure registered construction firms have tools, budget, recognition and system to enhance CEC. CIDB must model itself as well as be involved in leading this long-term practice i.e., CEC because it is not a once-and-done practice. Instead, CEC is a complex practice that construction firms initiate, sustain and evolve over a long time. Besides, the findings indicate that older and younger construction firms can achieve CEC when implementing OLC. An explanation of this can be that older and younger construction firms with OLC enable them to explore new opportunities, exploit existing opportunities, interact and exchange feedback with stakeholders, review existing goals and practices can achieve CEC as theorized by Leonidou [20].

5.2 Research limitations and future research suggestions

This study has limitations. First, this study is restricted to construction firms, which may constraint the generalizability of the results to the non-construction industries. Future studies should extend this research framework to the non-construction industries. Second, this study uses two factors i.e., OLC and organization age to examine CEC. For this reason, future studies may be extended to include other factors (e.g., innovation, technological knowledge) influence CEC. Innovative technologies enable organizations to reduce their environmental footprints by improving resource efficiency, minimizing waste generation and mitigating pollution [62]. For example, adopting 3D models of buildings and structures can identify clashes, conflicts and inefficiencies in the construction planning stage. Third, this study suffers from a causal design. As such, longitudinal studies may be relevant in future studies to examine the evolution of OLC and CEC influence over the years. Fourth, a single informant technique (each firm has one respondent) was applied, there is a fair chance of bias issue (i.e., the respondents prefer to give desirable responses). Multi-informant and multi-method designs could be used in future studies to determine organizations’ learning capability, CEC and age. For example, document analysis could be integrated with a self-administered questionnaire to measure OLC, CEC and age. Another example is that more than one respondent from each firm could be considered, especially at different position levels because OLC and CEC implementation may vary across employees although they work in the same firms.

Footnotes

Ethical approval

Not applicable.

Informed consent

Informed consent was obtained from all participants.

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgments

The authors have no acknowledgments.

Funding

Not applicable.

References

Tay

Tan

Leow

. Ability, motivation, opportunity-enhancing HRM practices and corporate environmental citizenship: Revisiting the moderating role of organizational learning capability in Malaysian construction companies. International Journal of Business and Society. 2023a;24(1):100–8.

Tay

Tan

Leow

. Ability, motivation and opportunity (AMO)-enhancing HRM practices and corporate environmental citizenship: The mediation effect of organizational ethical climate. Jurnal Pengurusan. 2023b;67, 1–14.

Banerjee

. Corporate environmentalism. The construct and its measurement. Journal of Business Review. 2002;55(3):177–91.

Tashman

Flankova

Van-Essen

Marano

. Why do firms participate in voluntary environmental programs? A meta-analysis of the role of institutions, resources and program stringency. Organization & Environment. 2021;35(1):3–29.

Ting

. Do large firms just talk about corporate social responsibility? - The evidence from CSR report disclosure. Finance Research Letters. 2021;38, e101476.

Bianchi

Testa

Boiral

Iraldo

. Organizational Learning for Environmental Sustainability: Internalizing Lifecycle Management. Organization and Environment. 2021;35(1):103–29.

Osagie

Wesselink

Blok

Mulder

. Learning organization for corporate social responsibility implementation, unraveling the relationship between organizational and operational learning organization characteristics. Organization and Environment. 2020;35(1):130–53.

Chiva

Alegre

Lapiedra

. Measuring organizational learning capability among the workforce. International Journal of Manpower. 2007;28(3/4):224–42.

Valdez-Juarez

Gallardo-Vázquez

Ramos-Escobar

. Organizational learning and corporate social responsibility drivers of performance in SMEs in Northwestern Mexico. Sustainability. 2019;11(20):1–23.

10.

Battistella

Cicero

Preghenella

. Sustainable organizational learning in sustainable companies. The Learning Organization. 2021;28(1):15–31.

11.

Banerjee

. Corporate environmentalism perspective from organizational learning. Management Learning. 1998;29(2):147–64.

12.

Agyabeng-Mensah

Tang

Afum

Baah

Dacosta

. Organizational identity and circular economy: Are inter and intra organizational learning, lean management and zero waste practices worth pursuing? Sustainable Production and Consumption. 2021;28(1):662–48.

13.

Coad

Holm

Krafft

Quatraro

. Firm age and performance. Journal of Evolutionary Economics. 2018;28(1):1–11.

14.

Semenov

Randrianasolo

. Advertising intensity and firm performance. The influence of firm age and cultural communication studies. International Marketing Review. 2023;40(2):265–89.

15.

Blank

. Developing incremental innovation in the high-tech industry: The effect of age and tenure in research and development teams. Cross-Cultural and Strategic Management. Advance online printing. https://doiorg.tarc.idm.oclc.org/10.1108/CCSM-04-2023-0054.

16.

Cinčalová

Hedija

. Firm Characteristics and Corporate Social Responsibility: The Case of Czech Transportation and Storage Industry. Sustainability. 2020;12(5):e12051992.

17.

Badulescu

Saveanu

Hatos

. The relationship between firm size and age, and its social responsibility actions-focus on developing countries (Romania). Sustainability. 2018;10(3):805.

18.

Hart

. A natural-resource-based view of the firm. The Academy of Management Review. 1995;20(4):986–1014.

19.

Borchardt

Wendt

Pereira

Sellitto

. Redesign of a component based on eco-design practices: environmental impact and cost reduction achievements. Journal of Cleaner Production. 2011;19(1):49–57.

20.

Leonidou

Christodoulides

Kyrgidou

Palihawadana

. Internal drivers and performance consequences of small firm green business strategy: The moderating role of external forces. Journal of Business Ethics. 2017;140(3):585–606.

21.

Babiak

Kihl

. A case study of stakeholder dialogue in professional sport: An example of CSR engagement. Business and Society Review. 2018;123(1):119–49.

22.

Monfort

Villagra

López-Vázquez

. Exploring stakeholders’ dialogue and corporate social responsibility (CSR) on Twitter. El Profesional de la Información. 2019;28(5):1699–2407.

23.

Khuong

NKT

Hang

TTT

. Stakeholders and Corporate Social Responsibility (CSR) programme as key sustainable development strategies to promote corporate reputation—evidence from Vietnam. Cogent Business & Management. 2021;8(1):e1917333.

24.

Roscoe

Subramanian

Jabbour

CJC

Chong

. Green human resource management and the enablers of green organizational culture: Enhancing a firm’s environmental performance for sustainable development. Business Strategy and the Environment. 2019;28(5):713–37.

25.

Ababneh

OMA

. How do green HRM practices affect employees’ green behaviors? The role of employee engagement and personality attributes. Journal of Environmental Planning and Management. 2021;64(7):1204–26.

26.

Hsieh

Weng

Pham

. What drives employees to participate in corporate social responsibility? A personal characteristic - CSR capacity - organizational reinforcing model of employees’ motivation for voluntary CSR activities. The International Journal of Human Resource Management. 2021;33(18):3703–35.

27.

Carstensen

Turk-Charles

. The salience of emotion across the adult life span. Psychology and Aging. 1994)9(2)259–64.

28.

Fredrickson

Carstensen

. Choosing social partners: How old age and anticipated endings make people more selective. Psychology and Aging. 1990;5(3):335–47.

29.

Madden

McMillan

Harris

. Drivers of selectivity in family firms: Understanding the impact of age and ownership on CSR. Journal of Family Business Strategy. 2020;11(2):100335.

30.

Ali

Danish

Asrar-Ul-Haq

. How corporate social responsibility boosts firm financial performance: The mediating role of corporate image and customer satisfaction. Corporate Social Responsibility and Environmental Management. 2020;27(1):166–77.

31.

D’Amato

Falivena

. Corporate social responsibility and firm value: Do firm size and age matter? Empirical evidence from European listed companies. Corporate Social Responsibility Environmental Management. 2020;27(2):909–24.

32.

Withisuphakorn

Jiraporn

. The effect of firm maturity on corporate social responsibility (CSR): Do older firms invest more in CSR? Applied Economics Letters. 2016;23(4):298–301.

33.

Construction Industry Development Board Malaysia. Impact Study On ISO 14001 implementation to G7 contractors with annual turnover Rm200 million and above. 2020. pp. 1-20.

34.

Krejcie

Morgan

. Determining sample size for research activities. Educational and Psychological Measurement. 1970;30(3):607–10.

35.

Sekaran

Bougie

. Research Methods for Business: A Skill Building Approach. New Delhi: Wiley India; 2013.

36.

Lafuente

Solano

Leiva

Mora-Esquivel

. Determinants of innovation performance. Academia RevistaLatinoamericana de Administración. 2019;32(1):40–62.

37.

Lara

Salas-Vallina

. Managerial competencies, innovation and engagement in SMEs: The mediating role of organizational learning. Journal of Business Research. 2017;79(1):152–60.

38.

Tay

Tan

Yahya

Rasli

. Validation of corporate environmental citizenship measurement in Malaysia. European Business Review. 2021;33(2):345–60.

39.

Tay

Tan

Yahya

. Ability, motivation, opportunity enhancing human resource management and corporate environmental citizenship: What’s the connection? Global Business and Management Research. 2017;9(1):299–312.

40.

Tay

Tan

Yahya

. Elucidating the bonds between organizational ethical climate and corporate environmental citizenship. Jurnal Pengurusan. 2018;54(1):61–71.

41.

Leyva-de Hiz

La DI

Bolívar-Ramos

. The inverted U relationship between green innovative activities and firms’ market-based performance: The impact of firm age. Technovation. 2021;110, e102372.

42.

Mabenge

Ngorora-Madzimure

GPK

Makanyeza

. Dimensions of innovation and their effects on the performance of small and medium enterprises: the moderating role of a firm’s age and size. Journal of Small Business & Entrepreneurship. 2020. https://www-tandfonline-com-s.web.bisu.edu.cn/action/showCitFormats?doi=10.1080/08276331.2020.1725727.

43.

Kim

. Innovation failure and firm growth: dependence on firm size and age. Technology Analysis & Strategic Management. 2022;34(2):166–79.

44.

Laforet

. Innovation characteristics of young and old family-owned businesses. Journal of Small Business and Enterprise Development. 2013;20(1):204–24.

45.

Nunnally

. Psychometric Theory, 2nd ed., McGraw-Hill, New York, NY; 1978.

46.

Hair

Hult

GTM

Ringle

Sarstedt

. A Primer on Partial Least Squares Structural Equation Modeling (PLSSEM) 3^rd ed Thousand Oaks, CA: Sage; 2021.

47.

Mackenzie

Podakoff

Jarvis

. The Problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology. 2005;90(4):710–30.

48.

Bagozzi

. On the evaluation of structural equation models. Journal of the Academy of Marketing Science. 1988;16(1):74–94.

49.

Cataldo

Grassia

Lauro

Marino

. Developments in Higher-Order PLS-PM for the building of a system of Composite Indicators. Quality & Quantity. 2016;51(2):657–74.

50.

Hair

Risher

Sarstedt

Ringle

. When to use and how to report the results of PLS-SEM. European Business Review. 2019;31(1):2–24.

51.

Henseler

Dijkstra

Sarstedt

Ringle

Diamantopoulos

Straub

Ketchen

Jr Hair

Hult

GTM

Calantone

. Common beliefs and reality about PLS: comments on Rönkkö Evermann (2013). Organizational Research Methods. 2014;17(2):182–209.

52.

Zeimers

Anagnostopoulos

Zintz

Willem

. Organizational learning for corporate social responsibility in sport organizations. European Sport Management Quarterly. 2019;19(1):80–101.

53.

Zhang

Zhu

. Enhancing corporate sustainable development: Stakeholder pressures, organizational learning, and green innovation. Business Strategy and the Environment. 2019;28(6):1012–26.

54.

Ajibike

Adeleke

Mohamad

Bamgbade

Nawi

MNM

Moshood

. An evaluation of environmental sustainability performance via attitudes, social responsibility, and culture: A mediated analysis. Environmental Challenge. Forthcoming 2021. https://doi.org/10.1016/j.envc.2021.100161.

55.

Bamgbade

Kamaruddeen

Nawi

MNM

. Malaysian construction firms’ social sustainability via organizational innovativeness and government support: The mediating role of market culture. Journal of Cleaner Production. 2017;154(1):114–24.

56.

Kang

Ahmad

Goh

Song

. Comparison of CSR activities between global construction companies and Malaysian construction companies. Open Journal of Social Science. 2015;3(7):92–8.

57.

Khurshid

Muzaffar

Bhutta

. Institutional pressures and supplier involvement: a perspective on sustainability. Operations Management Research. 2021;14(3):123–37.

58.

Shibin

Gunasekaran

Papadopoulos

Dubey

Singh

Wamba

. Enablers and barriers of flexible green supply chain management: A total interpretive structural modeling approach. Global Journal Flexible System Management. 2016;17(2):171–88.

59.

Mishra

Yadav

. Environmental capabilities, proactive environmental strategy and competitive advantage: A natural-resource-based view of firms operating in India. Journal of Cleaner Production. 2021;291:125249.

60.

Delmas

Hoffmann

Kuss

. Under the tip of the iceberg: Absorptive capacity, environmental strategy, and competitive advantage. Business & Society. 2011;50(1):116–54.

61.

Walsh

Dodds

. Measuring the choice of environmental sustainability strategies in creating a competitive advantage. Business Strategy and the Environment. 2017;26(5):672–87.

62.

Bhat

Mir

Allie

Lone

Al-Adwan

Jamali

Riyaz

. Unlocking corporate social responsibility and environmental performance: Mediating role of green strategy, innovation and leadership. Innovation and Green Development. 2024;3(1):100112.

Nexus between organizational learning capability and organization age on corporate environmental citizenship

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Literature review

2.1 OLC and CEC

2.2 The moderating effect of organization age

3 Methodology

3.1 Sample and procedure

3.2 Instruments

4.1 Measurement model

4.1.1 Reflective measurement model

4.2 Structural model

Table 4. Hypothesis Testing Result. Hypothesis Relationship Standard Beta Standard Error t Values Confidence Interval Decisions H1 OLC->CEC 0.932 0.948 22.32 [0.759, 0.983] Supported H2 OLC* Organization –0.031 –0.054 0.322 [–0.207, 0.153] Not supported age->CEC

5.1 Research implications

5.2 Research limitations and future research suggestions

Footnotes

Ethical approval

Informed consent

Conflict of interest

Acknowledgments

Funding

References

Table 4.
Hypothesis Testing Result.

Hypothesis Relationship Standard Beta Standard Error t Values Confidence Interval Decisions

H₁ OLC->CEC 0.932 0.948 22.32 [0.759, 0.983] Supported

H₂ OLC Organization* –0.031 –0.054 0.322 [–0.207, 0.153] Not supported

age->CEC