# A paradoxical perspective on the time dimension of corporate sustainability: Having both finite and infinite mindsets to play the game

Lin Wu, Nachiappan Subramanian, Kulwant S. Pawar, Miao Wang, Ajay Kumar, Kim Hua Tan

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## To cite this version:

Lin Wu, Nachiappan Subramanian, Kulwant S. Pawar, Miao Wang, Ajay Kumar, et al.. A paradoxical perspective on the time dimension of corporate sustainability: Having both finite and infinite mindsets to play the game. Journal of Cleaner Production, 2024, 480, 10.1016/j.jclepro.2024.144082. hal-05531911

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# A paradoxical perspective on the time dimension of corporate sustainability: Having both finite and infinite mindsets to play the game

Lin Wu<sup>a,*</sup>, Nachiappan Subramanian<sup>b</sup>, Kulwant S. Pawar<sup>a</sup>, Miao Wang<sup>c</sup>, Ajay Kumar<sup>d</sup>, Kim Hua Tan<sup>a</sup>

## ARTICLE INFO

Handling Editor: Jian Zuo

## Keywords:

Corporate sustainability (CS)

Time

Finite and infinite games

Paradox

Organizational mindset

## ABSTRACT

Adopting a paradoxical perspective, we propose that companies need to have both finite and infinite mindsets to play the infinite game of corporate sustainability (CS). The current development of the paradoxical approach to CS focuses primarily on tension management, with limited attention directed to the time dimension and insufficient empirical evidence on how tensions between the present and the future should be dealt with. To bridge this gap, we empirically explore the effect of CS practices, driven by both finite and infinite mindsets, on CS performance. Data collected from a large-scale survey of Chinese automotive manufacturers were analyzed using the Entropy Weight Method (EWM) and Ordinary Least Squares (OLS) regressions. Findings suggest that a time-based paradoxical perspective on CS exists in firms, and they have realized short- and long-term benefits from implementing different types of CS practices. Our findings complement the CS literature on tension management and encourage businesses and policymakers to take a paradoxical approach to CS processes and outcomes.

## Nomenclature list

<table><thead><tr><td>Abbreviation</td><td>Full term</td><td>Definition</td></tr></thead><tbody><tr><td>CS</td><td>Corporate sustainability</td><td>A development approach that satisfies the needs of a company's direct and indirect stakeholders while ensuring that future stakeholders' needs can also be met.</td></tr><tr><td>3BL</td><td>Triple bottom line</td><td>A sustainability framework incorporating economic, environmental and social sustainability.</td></tr><tr><td>CSP</td><td>Corporate sustainability practice</td><td>Initiatives/actions of the firm to achieve sustainability.</td></tr><tr><td>CSO</td><td>Corporate sustainability outcome</td><td>Outcomes/performance implications of implementing sustainability practices.</td></tr><tr><td>FCSP</td><td>Finite corporate sustainability practice</td><td>Sustainability practices whose desired benefits can be achieved quickly.</td></tr><tr><td>ICSP</td><td>Infinite corporate sustainability practice</td><td>Sustainability practices whose desired benefits are uncertain or will take a long period of time to materialize.</td></tr></tbody></table>

(continued on next column)

(continued)

<table><thead><tr><td>Abbreviation</td><td>Full term</td><td>Definition</td></tr></thead><tbody><tr><td>STCSO</td><td>Short-term corporate sustainability outcome</td><td>Survival and legitimacy, reflected by financial indicators and law/regulation compliance.</td></tr><tr><td>LTSCO</td><td>Long-term corporate sustainability outcome</td><td>Sustainable development, reflected by the meeting of stakeholder demands including future stakeholders.</td></tr><tr><td>OIM</td><td>Operational improvement</td><td>Distinct sets of skills, processes, and routines focused on gradually improving and strengthening existing operations.</td></tr><tr><td>OIN</td><td>Operational innovation</td><td>Distinct sets of skills, processes, and routines that radically change the existing way of operating or create entirely new ways, which requires a longer time and more effort to develop.</td></tr></tbody></table>

E-mail addresses: Lin.Wu@nottingham.ac.uk (L. Wu), N.Subramanian@sussex.ac.uk (N. Subramanian), Kulwant.Pawar@nottingham.ac.uk (K.S. Pawar), Miao-Wang@nottingham.edu.cn (M. Wang), akumar@em-lyon.com (A. Kumar), Kim.Tan@nottingham.ac.uk (K.H. Tan).

https://doi.org/10.1016/j.jclepro.2024.144082

Received 11 August 2023; Received in revised form 4 October 2024; Accepted 23 October 2024

Available online 24 October 2024

0959-6526/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> Nottingham University Business School, UK</footnote>

<footnote><sup>b</sup> University of Sussex, UK</footnote>

<footnote><sup>c</sup> Centre for English Language Education, UNNC-NFTZ Blockchain Laboratory, University of Nottingham Ningbo, China</footnote>

<footnote><sup>d</sup> EMLYON Business School, France</footnote>

<footnote>* Corresponding author.</footnote>
# 1. Introduction

Due to frequent supply chain disruptions caused by extreme weather, public health crises, and geopolitical conflicts in recent years, the need for a more sustainable way of life is increasingly stressed, with businesses being at the forefront. When applied to a business context, sustainability is often referred to as corporate sustainability (CS) (Dyllick and Hockerts, 2002). It is a development approach that satisfies the needs of a company's direct and indirect stakeholders while ensuring that future stakeholders' needs can also be met. Specifically, it is the voluntary inclusion of environmental and social considerations in business operations that enables firms to better interact with stakeholders now as well as in the future (Ozanne et al., 2016). This definition of CS has been widely accepted in academic research so far, which has a clear temporal focus as both the present and future generations are concerned. However, unfortunately, this time dimension has not been well studied in the existing literature, leading to scant understanding of how companies should best operate to achieve short- and long-term CS goals and satisfy current and future stakeholders (Slawinski and Bansal, 2015; Hockerts and Searcy, 2023).

The Triple-Bottom-Line (3BL) is unarguably the most popular perspective developed for understanding CS. According to Elkington (1998), CS is a three-dimensional concept including economic, environmental, and social sustainability, and organizations claiming to be sustainable should be able to all these requirements simultaneously. To date, the 3BL perspective has dominated studies on CS with significant advancements. For instance, the inter-relationships among the CS dimensions have been extensively studied, especially the impact of environmental or social sustainability on firm financial performance (e.g., Zhu et al., 2016; Danso et al., 2020; Khan et al., 2023). Theoretically, as Haffar and Searcy (2017) point out, sustainability is increasingly incorporated into the corporate value chain with the help of these studies.

While the 3BL provides a useful framework to understand CS holistically within the same time horizon, the inter-generational focus embedded in the definition is overlooked (Weiser et al., 2017). A notable lack of attention from the CS research community to time is increasingly recognized (Hockerts and Searcy, 2023). As a result, existing CS studies are mostly characterized by a short-sightedness, and we still do not know the long-term value or consequence of our behavior today. For example, Slawinski and Bansal (2015) suggest that a win-win situation could conceal problems and cause long-term issues, and similarly, a current trade-off may bring unexpected benefits in the future. However, both research and practice are still exploring how organizations should best operate to achieve short-as well as long-term CS goals (Shevchenko et al., 2016).

Driven by the pressing research gap on the time dimension of CS, our study aims to develop and empirically validate a time-based CS framework drawing upon the paradox theory. A paradoxical perspective views everything as consisting of two conflicting yet inter-related elements, which we apply to understand the time dimension of CS, i.e., short- and long-term CS. In existing studies, CS is seen as both processes (what is being done) and outcomes (what is achieved). In this study, we aim to apply a paradoxical perspective on both CS processes and outcomes, and empirically examine the short- and long-term CS outcomes (CSOs) of CS processes/practices (CSPs) firms are implementing. The theory of finite and infinite games (Carse, 1986) is employed to understand the underlying logic of CSPs with support from the mindset theory. According to Carse (1986), there are two types of games: finite and infinite. While the purpose of playing finite games is to win and end the game, infinite games are played to continue the game. Theoretically, CS, with an inter-generational focus, makes business an infinite game. However, we argue that it is not viable for organizations to conduct business as a pure finite or infinite game. Instead, they need a dynamic paradoxical finite-infinite mindset to guide their behavior when pursuing CS. Our study intends to shed light on the following research question: "What are

the short-term and long-term outcomes of CSPs implemented by organizations?"

We seek to answer this research question in the context of the Chinese automotive manufacturing sector due to its size, growth potential, and the need for more sustainable operations. China is the world's largest automotive producer and consumer, and its electric vehicle (EV) production and consumption have been booming since the pandemic and will remain strong in the foreseeable future (Fang et al., 2023). Given the massive volumes of products needed to serve this growing market, it is essential to ensure that manufacturers are operating sustainably without compromising their future development and the welfare of other stakeholders. Our study aspires to contribute to the literature in two important ways. First, we further advance the paradoxical approach to CS with a focus on the much-neglected aspect, time. To our best knowledge, this is among the first attempts to empirically operationalize the time dimension of CS. Our findings from the Chinese automotive manufacturing sector inform the short- and long-term implications of organizational behavior toward sustainability. Second, through the lens of finite and infinite games theory, our study explains the underlying logic of why companies are willing to do things that do not seem to give them a quick return. This is a novel perspective that enriches existing CS literature.

# 2. Theoretical background

## 2.1. CS in business and management research

According to Haffar and Searcy (2017), CS outcome refers to performance along certain sustainability measures (usually the 3BL), and CS process represents strategic actions taken to achieve improved CS performance. This aligns with the Practice-Based View (PBV) of the firm, which relates performance variations to the implementation of practices. According to Bromiley and Rau (2014), a practice is a specific activity or a series of activities that various organizations can carry out. The PBV serves as the main theoretical basis for our study, where the effect of CSPs on CSOs is investigated with a particular focus on time.

Prior CS studies have identified numerous CSPs with mixed findings on their CSOs. Firms implement certain CSPs expecting to achieve certain CSOs; however, the actual realization of the expected benefits has been found to differ. Recently, attempts are emerging to explain the mixed findings on the CSP-CSO link from the viewpoint of time. For instance, Horvathova (2012) found a time lag between the adoption of green CSPs and their performance implications. The immediate effect of implementing these practices can be insignificant or even negative due to the initial setup costs required (Liou and Sharma, 2012). However, in the long run, benefits increase as the initial costs are gradually recovered by factors such as increased employee loyalty, revenue, corporate image, as well as the R&D efforts fostered during implementation (Liou and Sharma, 2012). As time potentially plays a role, we call for attention to the time dimension of CS, which we define as the time period between implementing CSPs and realizing the benefits from them.

## 2.2. Cognitive approaches to CS

A cognitive frame shows the underlying beliefs and perceptions through which individuals interpret information (Grewatsch and Kleindienst, 2018). Regarding CS, prior studies demonstrate two major cognitive frames, namely, the instrumental approach and the integrative approach (Sharma and Jaiswal, 2018). Existing studies rely largely on the instrumental view, which sees CS as either trade-offs or win-win situations between the socio-environmental dimension and business objectives (Khizar et al., 2022; Broccardo et al., 2023). From the instrumental view, environmental or social CSPs can be broadly categorized into two types: those that align with business objectives and those that contradict. Managers are thus more motivated to adopt the former type and avoid the latter due to the for-profit nature of business
organizations and the resource constraints they always face (Haffar and Searcy, 2017). They have to be selective when investing their limited resources in CSPs, and it is understandable that those with relatively more certain and quicker benefits are prioritized (Pinnington and Meehan, 2023). Although the instrumental view has advanced our understanding of CS and the inter-relationships between CS dimensions, it has significant limitations as it oversimplifies CS by conceptualizing it at the organizational level and eliminating tensions among competing objectives (Hahn et al., 2014). It does not consider the time dimension of CS; that is, the possibility of temporary trade-offs turning into win-win situations over time and vice versa.

Building on the organizational paradox theory, the emerging integrative/paradoxical view argues that companies should acknowledge and properly manage tensions when pursuing CS (Hahn et al., 2015; Carmine and De Marchi, 2022, 2023). As defined, a paradox involves two elements that are both contradictory and interconnected, coexisting simultaneously and ensuring over time (Smith and Lewis, 2011), and a paradoxical perspective on CS acknowledges the interrelated yet conflicting economic, environmental and social concerns, and aims to contribute to sustainable development through stronger business operations (Hahn et al., 2018). According to a recent review of CS paradox literature (Carmine and De Marchi, 2023), there are three streams of studies: tensions, paradoxical cognition, and paradoxical strategies. Tensions arise from managing competing elements in CS, such as conflicting demands and contradictory goals of different CS dimensions (Miron-Spektor et al., 2018). It is argued that properly managing tensions can result in unexpected outcomes, such as innovations that can potentially contribute to long-term performance improvements (Haffar and Searcy, 2017; Ivory and Brooks, 2018). However, there is insufficient empirical evidence to support this proposition as literature on CS paradox so far is dominated by conceptual contributions (e.g., Hahn et al., 2015; Van der Byl and Slawinski, 2015; Carmine and De Marchi, 2023), with only a few empirical attempts (e.g., Van Bommel, 2018; Brix-Asala et al., 2018; Zehendner et al., 2021; Carmine and De Marchi, 2022). To our best knowledge, most of these attempts focus on tension management of CS paradoxes, which are of high importance for understanding tension and developing tension management strategies in CS.

Within the organization, a paradox framework is developed where tensions are categorized into learning, belonging, organizing, and performing (Smith and Lewis, 2011). A systematic tension acknowledgement and management framework is proposed where the first step is to recognize tensions in CS, followed by strategies to manage the identified tensions (Hahn et al., 2015). According to this framework, tensions in CS can arise from four sources: the 3BL triad, level, change, and context. Haffar and Searcy (2017), based on a systematic review of CS trade-off studies, develop a hierarchical framework where tensions in CS are categorized into private value - shared value, scope - depth, measurement - management, which are seen as roots of CS trade-offs. More recently, according to Sharma and Jaiswal (2018), tensions in CS can take the forms of contradictions in values, identity, organizational practices, cognitive frames, roles and motivations, and horizons of decision-making.

In terms of tension management, acceptance and resolution strategies are proposed (Hahn et al., 2015). Acceptance strategies are characterized by opposition, which means distinguishing the two poles of a paradox, accepting the tensions, and finding ways to live with the situation. During this process, innovative ways of dealing with tensions can be developed, which have the potential to improve the CS performance of organizations. In terms of managing tensions between different time frames, the acceptance perspective suggests that firms can implement practices that combine short- and long-term outcomes to encourage more long-term considerations (Hahn et al., 2015). An example is a dual financial bonus system where short-term financial and long-term non-financial targets are both rewarded. On the other hand, resolution strategies focus on separation or synthesis, which transfer the situation

into a more easily manageable state. In practice, companies can either spatially separate short- and long-term CS responsibilities based on organizational levels or implement alternative governance structures where the line between short- and long-term CS is deliberately blurred. Overall, framing sustainability tensions as organizational cognitive paradoxes supports the achievement of higher sustainability outcomes (Carmine and De Marchi, 2022).

Though the paradoxical tension management perspective has largely redefined the way in which CS is approached, it focuses on tensions only and overlooks inter-relatedness/synergies and the coexistence of both, which is what a paradox is truly about. By implementing various CSPs, firms are dealing with tensions and synergies at the same time, consciously or subconsciously. As companies tend to face resource constraints, decisions on which CSPs to implement are strategic, and they tend to avoid focusing solely on serving either the current generation or future generations. Therefore, we believe that extracting useful information from the existing way of operating to generate a guiding framework is equally important as developing a brand-new approach or adding new practices, such as the dual rewarding system (Hahn et al., 2015), if not more important.

## 2.3. Time-based CS paradox

Our assumption in this study, in line with the CS paradox scholars (e. g., Hahn et al., 2015; Haffar and Searcy, 2017), is that compared with the instrumental approach, a paradoxical perspective offers wider opportunities to understand CS as it considers tensions and synergies between the two sides of a paradox. Therefore, we adopt the paradoxical approach as the theoretical basis for the time dimension of CS, for both CSPs and CSOs. According to Carse (1986), there are two different ways in which one can view their engagement with the world, using the metaphor of finite and infinite games. Finite games are played to win, and the point of winning or losing brings the game to an end. On the contrary, the purpose of playing an infinite game is not to win but to continue the game itself and be in it for as long as possible. As pointed out by Sinek (2019), business is an infinite game where it is difficult to define the rules, winners, or the finish line. How managers perceive the nature of the business reflects their mindset, and this guides their decision-making. A firm with a finite mindset sees business as a finite game, while one with an infinite mindset perceives the nature of business as an infinite game. As mindset determines behavior (Valentiner et al., 2011), we argue that organizations with a finite mindset aspire to win as quickly as possible and thus value quantifiable indicators such as financial performance. On the contrary, an infinite mindset will guide the organization to act in a way that builds for a better future. Therefore, an infinite mindset is of crucial importance for the business, and evidence shows that such organizations are stronger, more innovative, and more inspiring (Sinek, 2019).

Sustainability reinforces the infinite nature of business. However, with the severe competitiveness in the market, a purely infinite mindset is not enough for organizations to survive and develop. A paradoxical perspective is thus particularly useful to explain the coexistence of both mindsets and the associated behaviors. Within the course of business, organizations do need to regularly evaluate their performance relative to competitors (mostly financial) to make sure not to be beaten and driven out of the game (Sinek, 2019) and to satisfy current stakeholders. Although these evaluations or short-term goals do not mark the endpoint of the game, they do have affect the ability of firms to remain in the game, which is a crucial condition for an infinite game. On the other hand, nowadays it is no longer viable for organizations to focus on short-term gains only and neglect the needs of future stakeholders, making an infinite mindset crucial too. Therefore, business organizations should have both finite and infinite mindsets in the infinite game of CS as they cannot prioritize the needs of future generations over the current generation, or vice versa.
### 2.3.1. Short- and long-term CSOs

Like other games, CS has its goals and purposes. In the short run, organizations seek to survive legitimately, i.e., to remain in the game. Therefore, they must secure certain financial goals and comply with relevant environmental and social (e.g., safety) regulations and laws, and their performance needs to be reviewed at reasonable frequencies. Once survival is achieved, organizations will focus more on continuing the game and remaining in it for as long as they can (Carse, 1986). As a result, they aim to ensure their ability to be financially viable, environmentally friendly, and socially responsible so that they can be well accepted by the current generation of stakeholders. Based on existing studies, these objectives can be measured from both tangible (measurable and easily quantifiable) and intangible (evolving and implicit) aspects.

To date, from a tangible perspective, CS performance has mainly been measured from the 3BL perspective with various indicators for economic, environmental, and social sustainability performance respectively. For instance, manufacturing cost, environmental performance, and social reputation are used as proxies for different dimensions of CS performance (Gimenez et al., 2012). When time is taken into account, existing measures can be divided into short- and long-term CSOs. For instance, accounting indicators like Return on Assets (ROA) and Return on Sales (ROS) are believed to capture firms' efficiency to generate value in one year using their available assets, while long-term economic CS can be captured by market-based indicators like Tobin's q, which assesses a firm's future prospects based on external stakeholders' perceptions (Endrikat et al., 2014).

Regarding environmental and social CS, in the short term, companies are more likely to start small by reducing the negative impact of their current operations to gain legitimacy for survival, while in the long term, the infinite game ultimately goes beyond the organizational boundary, aiming to contribute to the natural environment and society in a positive way (Slawinski and Bansal, 2015).

While performance indicators are tangible and explicit, capabilities are intangible and implicit results of companies' implementation of CSPs. According to Ethiraj et al. (2005), organizational capabilities can be developed gradually through "learning by doing". In the context of CS, implementing CSPs can nurture various CS capabilities. Like tangible CSO measures, capabilities can also reflect the time dimension. For instance, operational improvement (OIM) capability, defined as distinct sets of skills, processes, and routines focused on gradually improving and strengthening existing operations (Wu et al., 2010). On the other hand, operational innovation (OIN) capability is sets of skills, processes, and routines that radically change the existing way of operating or create entirely new ways, which requires a longer time and more effort to develop (Wu et al., 2010). Based on their respective features, our study sees OIM and OIN as the short- and long-term intangible CSO aspects, respectively.

### 2.3.2. Finite and infinite CSPs

As a result of having both types of mindsets, firms tend to implement a variety of CSPs to achieve short- and long-term tangible and intangible CSOs. In this study, CSPs that benefit the implementing firm in a shorter period of time are termed as finite CSPs, and those contributing to performance indirectly with delayed CSOs are referred to as infinite CSPs. Understandably, literature suggests that finite CSPs tend to be more popular among companies, while the implementation of infinite CSPs, such as philanthropic donations to society, mostly results from external pressures (Zhu et al., 2012). Specific CSPs within each category are discussed below.

## 3. Hypothesis development

### 3.1. For the current generation – finite CSPs and short-term CSOs

To take care of the current stakeholders and be a legitimate player in

the marketplace, companies need to continuously reduce operating costs, improve efficiency and financial performance, as well as comply with regulations. Therefore, they usually start with incremental actions without making significant changes to existing operations. As literature suggests, waste reduction initiatives and reactive environmental management actions are effective means through which firms achieve these goals (e.g., Yang et al., 2011; Gotschol et al., 2014), and they are classified as finite CSPs in our study. The goal of implementing these practices is to increase profitability systematically by minimizing waste and increasing efficiency to survive the fierce competition (Hofer et al., 2012). According to a recent review (Leon and Calvo-Amodio, 2017), waste reduction practices improve the financial and environmental performance of the firm through mechanisms such as reduced waste and cost, increased productivity, and production efficiency, which helps firms to remain competitive. Since waste refers to activities that only consume resources and do not add value, removing them from operations can lead to prompt benefits.

Another important result of having a finite mindset is the adoption of reactive, regulation-driven green practices by firms. As Endrikat et al. (2014) point out, firms can take either reactive or proactive approaches when dealing with environmental issues. Reactive firms tend to take environmental initiatives to the minimum level, usually as a result of regulation compliance, which are often end-of-pipe solutions. It is a logical starting point for firms to move towards CS as these practices guarantee organizations' ability to survive and remain competitive.

In addition to tangible CSOs, finite CSPs are believed to have intangible CSOs. Successful implementation of finite CSPs requires extensive external learning as well as internal information sharing (Longoni and Cagliano, 2015), which forms the basis for capability emergence and development. Organizational efforts to systematically reduce, minimize, and eliminate waste foster continuous improvement capability (Hart, 1995). In sum, based on the PBV and expected CSOs of finite CSPs, we propose that.

**H1.** Implementing finite CSPs has a significant positive effect on short-term CSOs, both a) tangible and b) intangible.

### 3.2. For future generations – infinite CSPs and long-term CSOs

In addition to a finite mindset, to continue the game of business in the long run, firms need to go beyond the organizational boundary and consider the bigger picture, one that encompasses the whole of human society and the ecosystem. Therefore, the infinite mindset of managers regarding CS leads to the organizational adoption of infinite CSPs, such as socially responsible practices, which aim to take care of all stakeholders, including future stakeholders (Wang and Bansal, 2012). Gimenez et al. (2012) point out the internal and external orientations of long-term CS, which addresses concerns like equity, diversity, transparency, and democratic governance. Although a large body of literature has taken an instrumental logic and focused on the business case of CSR (Carroll and Shabana, 2010), CSR practices are believed to be a way in which business organizations contribute to society and our common future (Carroll, 1979). Thus, in this study, we define CSR-related activities as infinite CSPs.

We adopt the stakeholder framework by Shafiq et al. (2014) to understand infinite CSPs. Among all stakeholders, customers, employees, suppliers, the natural environment, and the community are the most frequently targeted when long-term CS is concerned. In terms of the natural environment, contrary to reactive environmental actions that pursue legitimacy to remain in the game, environmental proactivity refers to environmental initiatives that exceed mere compliance, with a focus on prevention efforts supported by higher-order learning (Endrikat et al., 2014). Such activities are usually undertaken by firms on a voluntary basis as a result of the strategic vision they have for the future. Characterized by voluntariness and anticipation (Calza et al., 2016), environmental proactivity constitutes a series of green practices,
including the voluntary reduction of hazardous/harmful/toxic raw materials in production (although legal), environmental design, the application of Life Cycle Analysis (LCA), and remanufacturing, among others. Proactive environmental practices are therefore a crucial element of infinite CSPs in this study, along with CSPs targeting other stakeholders, including customers, employees, suppliers, and the community. Net benefits associated with implementing these CSPs tend to be materialized in the long run, as firms can encounter higher costs that harm profitability initially (Nidumolu et al., 2009). However, this temporary monetary loss will be justified by future gains in economic, environmental, and social aspects (Wang and Bansal, 2012). Therefore, time should be taken into account when the effect of infinite CSPs is investigated (Bansal, 2005).

The implementation of infinite CSPs is also believed to have long-term intangible CSOs, i.e., OIN capability in this study. During the implementation, firms establish trust-based relationships with various stakeholders (Sharma and Vredenburg, 1998). As the development of OIN capabilities requires valuable input from different sources, the implementation of infinite CSPs can be seen as crucial in the accumulation of an organization's human and social capital (Kemper et al.,

2013; Donate et al., 2016). Furthermore, OIN capability requires extensive efforts in searching, discovering, experimentation, and implementation (Wu et al., 2010). Empirically, according to Sharma and Vredenburg (1998), proactive environmental activities can foster organizational capabilities of stakeholder integration, higher-order learning, and innovation. In sum, the adoption of infinite CSPs and the learning process derived from the implementation develop firms' OIN capability (Lai et al., 2015; Reverte et al., 2016). Therefore, we propose that.

H2. Implementing infinite CSPs has a significant positive impact on long-term CSOs, both a) tangible and b) intangible.

### 3.3. Paradoxical finite and infinite CSPs and their CSOs

Central to the time-based paradoxical CS perspective is the coexistence of finite and infinite CSPs, as well as the tensions and synergies between them. It is notable that the relative position of both elements of a paradox is not fixed; instead, they collectively form a dynamic system that changes over time with internal and external conditions (Smith and Lewis, 2011). The above discussions support that

<figure><img src="image_5.png" /><figcaption>Model 2: the effect of finite and infinite CSPs on short-term intangible CSOs (OIM capability)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Model 3: the effect of finite and infinite CSPs on long-term tangible CSOs</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Model 4: the effect of finite and infinite CSPs on long-term intangible CSOs (OIN capability)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 1. Conceptual models.</figcaption></figure>
finite CSPs mainly contribute to CSOs in the short term and infinite CSPs in the long run. However, finite CSPs also have long-term effects and infinite CSPs can also have short-term outcomes. For instance, waste reduction practices do not work in isolation, instead, they are a system composed of bundles of practices (Shah and Ward, 2007). Waste reduction and green practices are increasingly studied together as synergistic effects have been found during co-implementation (Galeazzo et al., 2014). However, time is needed for such synergies to occur. Furthermore, the effect of implementing infinite CSPs on OIM capabilities in the short run is not significant because, in the context of existing operations, such activities are seen as proactive and disruptive actions (Carroll and Shabana, 2010), which differs from the foundation of OIM capabilities (Wu et al., 2010). Therefore, we propose that.

H3. In the short term, finite CSPs play the dominant role in enhancing tangible and intangible CSOs. Specifically, a) the effect of finite CSPs on short-term tangible CSOs is greater than that of infinite CSPs; b) the effect of finite CSPs on short-term intangible CSOs is greater than that of infinite CSPs.

H4. In the long term, infinite CSPs play the dominant role in enhancing tangible and intangible CSOs. Specifically, a) the effect of infinite CSPs on long-term tangible CSOs is greater than that of finite CSPs; b) the effect of infinite CSPs on long-term intangible CSOs is greater than that of finite CSPs.

Based on the hypotheses, four research models are constructed, shown in Fig. 1.

# 4. Methodology

We adopted a quantitative survey approach to validate the proposed paradoxical research models, and the detailed steps of the research execution are illustrated in Fig. 2. Subjective, self-reported conceptual data were used due to the limited availability of objective data on the information we require, such as the implementation of CSPs. According to Ward et al. (1998), subjective data are not necessarily less reliable than their objective counterparts and have been widely used in research on CS (e.g., Zhu and Sarkis, 2004; Paulraj et al., 2017; Cousins et al., 2019; Dey et al., 2020), especially in the Chinese context (e.g., Li et al., 2020; Li et al., 2023). However, we acknowledge the shortcomings of self-reported data compared to objective data. For instance, there are potential biases associated with using perceptual measures and respondent selection (Flynn et al., 2018). Common method bias is a critical issue to be addressed in survey-based research (Podsakoff et al., 2003). Furthermore, in research contexts such as sustainability, where respondents are motivated to provide favorable information, social

desirability bias also requires careful handling (Roxas and Lindsay, 2012). When these biases are present in a study, the accuracy of the findings becomes questionable. Below, we explain our recognition of potential biases at different stages of the study and the mitigating strategies employed.

## 4.1. Sample and bias prevention

The survey was conducted in the Chinese automotive manufacturing sector, a typical context where finite and infinite mindsets coexist due to the need for higher efficiency today and the transition to cleaner energy-powered products in the future. With the support of a reputable survey company in China, we obtained an automotive company directory and approached a random sample of 2000 companies as our sampling frame. An online system was used to distribute the questionnaire to potential respondents, and weekly reminders were sent to those who did not respond. Special attention was paid to key informant selection, which is deemed crucial to address concerns over the subjective data-based survey method (Schoenherr et al., 2015; Flynn et al., 2018). Specifically, to ensure the quality of responses, we limited survey participation to those with more than one year of working experience in the same organization and holding positions such as CEO, manager, department head, or others with good knowledge about operations of their organizations. To minimize potential inaccurate information caused by the key informant's incorrect perception (Flynn et al., 2018), we explicitly encouraged respondents in the cover letter of the questionnaire to seek opinions from colleagues who they think are more knowledgeable about certain questions. The use of online survey ensured the collection of useable questionnaires, as the survey could not be submitted if a compulsory question was left blank. The online survey system, being a purely self-administered mode of survey, is believed to minimize social desirability bias, as there are no face-to-face interactions between the respondent and the enumerator, thus reducing pressure on respondents to provide false answers regarding the sustainability practices and performance of their organizations (Roxas and Lindsay, 2012). Additionally, we did not include any questions related to the identity of the respondents or their organizations, which further helps avoid potential social desirability bias (Leggett et al., 2003). To prevent possible common method bias, we deliberately separated conceptually similar constructs in the questionnaire. At the end of the survey, we collected 274 valid responses, resulting in a response rate of 14%, and the final sample size is considered sufficient for the complexity of the research models (Hair et al., 2014). Despite acknowledging the importance of the response rate for survey research, we did not offer monetary rewards to motivate responses and excluded responses considered of poor quality.

<figure><img src="image_9.png" /><figcaption>Fig. 2. Flow chart of methodology.</figcaption></figure>
Therefore, in the quality-quantity trade-off, we prioritized quality (Melnyk et al., 2012).

## 4.2. Research instrument

As discussed earlier, finite CSPs in this study mainly include waste reduction and reactive environmental practices due to their effectiveness in enhancing operational efficiency and ensuring the business' legitimacy in the market. Measurement scales for these practices are well-established and extensively validated in previous studies (e.g., Shah and Ward, 2007; Yang et al., 2011). We adapted measures from existing literature, including the implementation of: "pull" production systems; just-in-time (JIT) practices; quality improvement and control; inventory management; cross-functional training; regular maintenance; statistical process control (SPC); extensive use of statistical techniques; flow management; compliance with environmental regulations; and Environmental Management Systems.

Infinite CSPs, based on our definition, mainly encompass CSR practices, as they are perceived crucial for firms to contribute beyond organizational boundaries and sustain their business operations. Like finite CSPs, well-established measurement scales for CSR practices are found in prior studies (e.g., Gimenez et al., 2012; Jabbour et al., 2013; Shafiq et al., 2014). In this study, infinite CSPs include employee-, customer-, natural environment-, supplier-, and community-related CSR practices.

In terms of intangible CSOs, OIM items include the ability to continuously standardize and simplify production processes, reduce waste and variance, and learn. Items for OIN capability include the ability to create innovations that render prevailing processes obsolete, fundamentally change these processes, and make existing expertise obsolete.

Tangible short-term CSOs include short-term financial performance and environmental regulation compliance. Measures include improvements in return on assets (ROA), return on sales (ROS), profit, environmental performance, energy consumption, and compliance with environmental and safety regulations. Tangible long-term CSOs include Tobin's q, cost savings, reduced environmental impact, sales increases through CS, improved corporate image and stakeholder relations, and enhanced customer and employee relations.

In addition to the key constructs, we also included two control variables. Firm size, measured by the number of full-time employees, is often treated as a control variable in CS studies (e.g., Liu et al., 2017; Li et al., 2023). Larger organizations typically have stronger access to resources and are thus better positioned to invest in new CSPs compared to smaller firms (Hajmohammad et al., 2013). For instance, Moore and Manring (2009) suggest that large multinational conglomerates can distribute CSP-related costs across a broad and diversified global market, reducing the risk of CSP failures. Therefore, firm size is expected to influence a firm's engagement in CSPs.

Furthermore, since our study focuses on the time dimension of CS, we introduced firm age (measured as the number of operating years) as another control variable. Firms' involvement in CSPs is believed to increase as they mature (Reverte et al., 2016). Thus, it is reasonable to assume that firm age also affects the adoption of CSPs. Additionally, because some CSPs may not contribute to firm performance in the short term, older firms may realize more benefits from implementing CSPs compared to younger firms. Table 1 summarizes the main variables, their measurement items, and the sources.

We used a 9-point Likert scale in the questionnaire to capture respondents' perceptions of each item in relation to the actual situation in their organizations. This scale extends beyond the commonly used 5-point and 7-point scales, aiming to offer greater flexibility and independence for participants in selecting their responses (Joshi et al., 2015). The wider range of a scale can reduce the likelihood of respondents choosing the neutral midpoint, potentially leading to more accurate and reliable differentiation between responses and minimizing

<table><caption>**Table 1**
Measures with construct weights based on EWM.</caption><thead><tr><th>Construct</th><th>Measures</th><th>% of the measure based on EWM</th><th>Source</th></tr></thead><tbody><tr><td rowspan="14">CSPs - Finite (FCSP)</td><td>FCSP1: We use a "pull" production system</td><td>10.5</td><td rowspan="14">Shah and Ward (2007)<br>Jabbour et al. (2013)</td></tr><tr><td>FCSP2: We produce on a JIT basis</td><td>8.09</td></tr><tr><td>FCSP3: We undertake programs for quality improvement and control</td><td>5.28</td></tr><tr><td>FCSP4: We undertake programs for inventory management</td><td>5.76</td></tr><tr><td>FCSP5: Shop floor employees undergo cross functional training</td><td>14.77</td></tr><tr><td>FCSP6: We have systematic procedures to ensure all the equipment is under regular maintenance</td><td>6.04</td></tr><tr><td>FCSP7: We make extensive use of statistical techniques to reduce process variance</td><td>8.36</td></tr><tr><td>FCSP8: We believe the set-up times of equipment in our plant are low compared with competitors in similar industry</td><td>9.32</td></tr><tr><td>FCSP9: Our equipment is grouped to produce a continuous flow of families of products</td><td>4.74</td></tr><tr><td>FCSP10: We have Environmental Management Certification such as ISO 14000</td><td>13.38</td></tr><tr><td>FCSP11: We apply filters and controls for emissions and discharges</td><td>8.28</td></tr><tr><td>FCSP12: We give priority to employee safety during production</td><td>5.48</td></tr><tr><td rowspan="10">CSPs - Infinite (ICSP)</td><td>ICSP1: We avoid using legal, but poorly performing (from an environmental perspective) materials to manufacture products</td><td>6.18</td><td rowspan="10">Gonzalez et al. (2008)<br>Gimenez et al. (2012)</td></tr><tr><td>ICSP2: We use Life Cycle Analysis (LCA) for product design</td><td>3.93</td></tr><tr><td>ICSP3: We have clear environmental management policy</td><td>3.80</td></tr><tr><td>ICSP4: We provide regular environmental training for all employees</td><td>6.05</td></tr><tr><td>ICSP5: We systematically reduce the use of water, energy, and packaging</td><td>2.77</td></tr><tr><td>ICSP6: We have clear recycling procedures applied to water, energy, and packaging</td><td>5.90</td></tr><tr><td>ICSP7: We make continuous efforts to develop products with</td><td>3.39</td></tr></tbody></table>

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<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Construct</th><th>Measures</th><th>% of the measure based on EWM</th><th>Source</th></tr></thead><tbody><tr><td rowspan="20">Short-term intangible CSOs -OIM</td><td>lower environmental impact</td><td></td><td></td></tr><tr><td>ICSP8: We make continuous efforts to develop processes with lower environmental impact</td><td>3.32</td><td></td></tr><tr><td>ICSP9: We make continuous efforts to improve employee working conditions</td><td>3.02</td><td></td></tr><tr><td>ICSP10: All employees enjoy equal opportunity in our company e.g., employing disabled people, and/or promoting women to senior management positions</td><td>6.75</td><td></td></tr><tr><td>ICSP11: We provide all employees opportunities to develop their skills</td><td>4.98</td><td></td></tr><tr><td>ICSP12: We provide fair compensation to all employees</td><td>4.54</td><td></td></tr><tr><td>ICSP13: Employees participate in decision-making process</td><td>7.40</td><td></td></tr><tr><td>ICSP14: We have systematic procedures to improve employee satisfaction</td><td>6.56</td><td></td></tr><tr><td>ICSP15: We conduct regular customer surveys to hear customer voice/concerns</td><td>6.45</td><td></td></tr><tr><td>ICSP16: We have systematic procedures to take customer preference into our product development</td><td>3.62</td><td></td></tr><tr><td>ICSP17: We are responsible for product quality throughout the product life cycle</td><td>4.86</td><td></td></tr><tr><td>ICSP18: We work with our suppliers on a fair and transparent basis</td><td>4.23</td><td></td></tr><tr><td>ICSP19: We make philanthropic donations to the society</td><td>6.06</td><td></td></tr><tr><td>ICSP20: We provide sponsorship of local community initiatives</td><td>6.18</td><td></td></tr><tr><td>OIM1: Continuous standardization of production processes</td><td>14.04</td><td>Wu et al. (2010)</td></tr><tr><td>OIM2: Continuously simplification of production processes</td><td>13.28</td><td></td></tr><tr><td>OIM3: Continuous reduction of waste and variance in the production process</td><td>14.15</td><td></td></tr><tr><td>OIM4: Continuous motivation of human resource to higher levels of effort and effectiveness</td><td>33.84</td><td></td></tr><tr><td>OIM5: Long-term continuous increase and application of process understanding</td><td>12.10</td><td></td></tr></tbody></table>

<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Construct</th><th>Measures</th><th>% of the measure based on EWM</th><th>Source</th></tr></thead><tbody><tr><td rowspan="12">Long-term intangible CSOs -OIN</td><td>OIM6: Continuous learning from past success and failure to improve processes</td><td>12.59</td><td></td></tr><tr><td>OIN1: We pursue long-range programs, in order to acquire manufacturing capabilities in advance of our needs</td><td>21.25</td><td>Wu et al. (2010)</td></tr><tr><td>OIN2: We make an effort to anticipate the potential of new manufacturing practices and technologies</td><td>16.91</td><td></td></tr><tr><td>OIN3: Our plant stays on the leading edge of new technology in our industry</td><td>7.86</td><td></td></tr><tr><td>OIN4: We are constantly thinking of the next generation of manufacturing technology</td><td>12.98</td><td></td></tr><tr><td>OIN5: Innovations created that made the prevailing processes obsolete</td><td>10.91</td><td></td></tr><tr><td>OIN6: Innovations created that fundamentally changed the prevailing processes</td><td>14.36</td><td></td></tr><tr><td>OIN7: Innovations created that made the existing expertise in prevailing processes obsolete</td><td>15.73</td><td></td></tr><tr><td>Short-term tangible CSOs (STCSO)</td><td>STCSO1: ROA<br>STCSO2: ROS<br>STCSO3: Profit<br>STCSO4: The reduction of air emissions<br>STCSO5: The reduction of wastewater generation<br>STCSO6: The reduction of solid waste disposal<br>STCSO7: The reduction of hazardous/harmful/toxic materials<br>STCSO8: The reduction of energy consumption<br>STCSO9: Compliance with environmental regulation</td><td>12.19<br>12.56<br>11.38<br>12.22<br>11.33<br>10.75<br>10.39<br>9.90<br>9.31</td><td>Yang et al. (2011)<br>Hajmohammad et al. (2013) Jabbour et al. (2014) Fullerton et al. (2014)</td></tr><tr><td>Long-term intangible CSOs (LTSCO)</td><td>LTCSO1: Tobin's q<br>LTCSO2: Cost savings achieved from green and social initiatives<br>LTCSO3: Sales increase achieved from green and social initiatives<br>LTCSO4: The development of products with fewer negative environmental impacts<br>LTCSO5: The development of processes with fewer negative environmental impacts<br>LTCSO6: Improvement of corporate social image</td><td>11.95<br>12.23<br>11.61<br>10.17<br>8.68<br>9.57</td><td>Prajogo et al. (2012)</td></tr></tbody></table>

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<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Construct</th><th>Measures</th><th>% of the measure based on EWM</th><th>Source</th></tr></thead><tbody><tr><td></td><td>LTCSO7: Improvement of stakeholder relations</td><td>13.52</td><td></td></tr><tr><td></td><td>LTCSO8: Improvement of customer satisfaction</td><td>13.68</td><td></td></tr><tr><td></td><td>LTCSO9: Improvement of employee satisfaction</td><td>8.57</td><td></td></tr></tbody></table>

measurement error.

We also applied the back-translation method (Bhalla and Lin, 1987) to ensure equivalence between the original English items and the Chinese version of the questionnaire. The final Chinese version was pilot tested through discussions with a panel of 12 experts, including university professors specializing in supply chain management and CS, as well as industry managers. This testing aimed to ensure both face and content validity of the measures (Hair et al., 2014). The questionnaire was revised through several rounds of feedback before the official survey launch.

# 5. Analyses and results

## 5.1. Descriptive statistics

Each response was provided by a single key informant from each unit of analysis, which is a common practice in surveys due to time and cost constraints (Qi et al., 2017). Table 2 summarizes the characteristics of the respondents and their organizations. Notably, over 76% of respondents (209 out of 274) hold positions of departmental head or above. This high percentage of management-level respondents suggests that they possess sufficient knowledge to accurately answer the questions, thereby ensuring data quality.

Regarding company size, 40.1% of responses (110 out of 274) were from firms with 21–300 employees, and 30.3% (83 out of 274) were from firms with 301–1000 employees. In terms of company age, the largest proportion of responses (32.1%, or 88 out of 274) came from companies with 11–15 years of operating history, followed by 24.8% (68 out of 274) from companies 6–10 years old, and 17.9% (49 out of 274) from companies with 16–20 years of operation. Regarding ownership types, 65.3% of the sample (179 out of 274) are private companies, followed by joint ventures (17.2%, or 47 out of 274), state-owned companies (10.2%, or 28 out of 274), and foreign companies (7.3%, or 20 out of 274). Overall, the sample is considered representative of the

<table><caption>**Table 2**
Descriptive statistics.</caption><thead><tr><th>No. of Employees</th><th>No.</th><th>%</th><th>Age (Years)</th><th>No.</th><th>%</th></tr></thead><tbody><tr><td>&lt;20</td><td>6</td><td>2.2</td><td>1–5</td><td>17</td><td>6.2</td></tr><tr><td>21–300</td><td>110</td><td>40.1</td><td>6–10</td><td>68</td><td>24.8</td></tr><tr><td>301–1000</td><td>83</td><td>30.3</td><td>11–15</td><td>88</td><td>32.1</td></tr><tr><td>1001–2000</td><td>29</td><td>10.6</td><td>16–20</td><td>49</td><td>17.9</td></tr><tr><td>2001–3000</td><td>13</td><td>4.7</td><td>21–25</td><td>24</td><td>8.8</td></tr><tr><td>3001–5000</td><td>5</td><td>1.8</td><td>26–30</td><td>6</td><td>2.2</td></tr><tr><td>&gt;5001</td><td>28</td><td>10.2</td><td>&gt;30</td><td>22</td><td>8.0</td></tr><tr><td>Total</td><td>274</td><td>100</td><td>Total</td><td>274</td><td>100</td></tr></tbody></table><table><thead><tr><th>Respondent Position</th><th>No.</th><th>%</th><th>Ownership</th><th>No.</th><th>%</th></tr></thead><tbody><tr><td>CEO</td><td>7</td><td>2.6</td><td>State-owned</td><td>28</td><td>10.2</td></tr><tr><td>Senior manager</td><td>58</td><td>21.2</td><td>Private</td><td>179</td><td>65.3</td></tr><tr><td>Junior manager</td><td>95</td><td>34.7</td><td>Joint venture</td><td>47</td><td>17.2</td></tr><tr><td>Department head</td><td>49</td><td>17.9</td><td>Foreign</td><td>20</td><td>7.3</td></tr><tr><td>Supervisor</td><td>33</td><td>12.0</td><td>Total</td><td>274</td><td>100</td></tr><tr><td>Shop floor employee</td><td>16</td><td>5.8</td><td></td><td></td><td></td></tr><tr><td>Others</td><td>16</td><td>5.8</td><td></td><td></td><td></td></tr><tr><td>Total</td><td>274</td><td>100</td><td></td><td></td><td></td></tr></tbody></table>

research population.

## 5.2. Dealing with biases

In addition to preventive measures described earlier, Harman's single-factor test was performed to check for potential common method bias (Podsakoff et al., 2003). As shown in Tables 3 and 4, sampling adequacy was ensured, and a total of 5 factors emerged with the first one explaining 33% of the total variance. This indicates that common method bias is unlikely to be a factor that significantly influences the study's results.

To detect potential issues with non-response bias, we followed a common approach (e.g., Gimenez et al., 2012), which involves comparing early and late responses on the same indicators to see if there are any significant differences. Specifically, we compared the first 50 and the last 50 questionnaires collected with regard to organizational characteristics, including firm age, size, and ownership, using one-way analysis of variance (ANOVA). The results indicated that all *p* values were greater than 0.05 (0.746 for age, 1.000 for size, and 0.782 for ownership), suggesting no significant differences between early and late respondents, and thus, no evidence of non-response bias affecting the reliability of the results.

## 5.3. Data analysis

To analyze the collected data, we employed the entropy weight method (EWM) to determine the weight of each item and establish constructs, and ordinary least squares (OLS) regressions for hypothesis testing. This choice of methods is primarily motivated by the multicriteria decision context of this study (dos Santos et al., 2019). The EWM has proven valuable for both academic and practical purposes as an information weight model for decision-making and criteria determination (Li et al., 2011; Zhu et al., 2020).

EWM evaluates constructs by quantifying the differentiation among attributes, providing more information when data variation is greater. It assigns objective weights to indicators to reflect their importance. By using entropy values to assign weights, this method facilitates the selection of alternatives in a transparent and unbiased manner. According to the literature, the results from EWM are considered reliable and effective because it mitigates the influence of the decision-maker's preferences, producing results that are closer to the objective reality compared to other subjective weighting models (Kumar et al., 2017). Additionally, EWM does not impose strict requirements on data observation, distribution, or the number of constructs.

In the context of this study, the constructs of finite and infinite CSPs and short-term and long-term CSOs are not yet fully established in the literature. Since developing and validating these measures is beyond the scope of this study, our focus is on analyzing the information derived from the survey responses. Therefore, EWM is deemed suitable for our primary analysis. However, we acknowledge the need for rigorously established measurement scales for these variables in future research.

To perform EWM, we followed the following steps.

(1) Define the objectives and establish the initial matrix for the index system. In this study, the main objectives are finite CSPs (FCSPs), infinite CSPs (ICSPs), short-term intangible CSO (OIM), long-term intangible CSO (OIN), short-term tangible CSO (STCSO) and long-

<table><caption>**Table 3**
Results of Harman's single-factor test (KMO and Bartlett's Test).</caption><thead><tr><th>Indicator</th><th>Value</th></tr></thead><tbody><tr><td>Kaiser-Meyer-Olkin Measure of Sampling Adequacy</td><td>0.922</td></tr><tr><td>Bartlett's Test of Sphericity Approx. Chi-Square</td><td>5667.529</td></tr><tr><td>Df</td><td>496</td></tr><tr><td>Sig</td><td>0.000</td></tr></tbody></table>
<table><caption>**Table 4**
Total variance explained.</caption><thead><tr><th rowspan="2">Component</th><th colspan="3">Initial Eigenvalues</th><th colspan="3">Extraction Sums of Squared Loadings</th><th colspan="3">Rotation Sums of Squared Loadings</th></tr><tr><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th><th>% of Variance</th><th>Cumulative %</th><th>Total</th><th>% of Variance</th><th>Cumulative %</th></tr></thead><tbody><tr><td>1</td><td>10.717</td><td>33.489</td><td>33.489</td><td>10.717</td><td>33.489</td><td>33.489</td><td>7.000</td><td>21.874</td><td>21.874</td></tr><tr><td>2</td><td>5.635</td><td>17.609</td><td>51.098</td><td>5.635</td><td>17.609</td><td>51.098</td><td>6.479</td><td>20.247</td><td>42.121</td></tr><tr><td>3</td><td>1.903</td><td>5.948</td><td>57.046</td><td>1.903</td><td>5.948</td><td>57.046</td><td>3.030</td><td>9.469</td><td>51.591</td></tr><tr><td>4</td><td>1.232</td><td>3.849</td><td>60.895</td><td>1.232</td><td>3.849</td><td>60.895</td><td>2.890</td><td>9.030</td><td>60.621</td></tr><tr><td>5</td><td>1.089</td><td>3.404</td><td>64.299</td><td>1.089</td><td>3.404</td><td>64.299</td><td>1.177</td><td>3.679</td><td>64.299</td></tr></tbody></table>

**Note(s):** Extraction method: Principal Component Analysis.

term tangible CSO (LTCSO). For each objective mentioned above, suppose there are *m* indicators and *n* samples, we established

$$ \text{matrix A} = x_{ij} n^* m = \begin{bmatrix} x_{11} & \cdots & x_{1m} \\ \vdots & \ddots & \vdots \\ x_{n1} & \cdots & x_{nm} \end{bmatrix}. $$

(2) Standardize the data. In this study, constructs are composed of different numbers of measures. To eliminate its impact on the final estimation results, we standardized the calculation formula of each indicator:

$$ x'_{ij} = \frac{x_{ij} - \min x_{ij}}{\max x_{ij} - \min x_{ij}} $$

(3) Calculate the weights of measures. For an objective, the weight of each measure is defined as:

$$ p_{ij} = \frac{x'_{ij}}{\sum_{i=1}^{n} x'_{ij}} $$

(4) Estimate information entropy. The entropy value of the *j*th index is calculated as:

$$ e_j = \frac{\sum_{i=1}^{n} p_{ij} \cdot \ln p_{ij}}{-\ln n} $$

(5) Calculate the weight. The difference coefficient $d_j$ shows the diversification degree: $d_j = 1 - e_j$ and the weight value $w_j$ of *j*th indicator is defined as: $w_j = \frac{d_j}{\sum_{j=1}^{n} d_j}$. Weights of each measure are shown in Table 1.

(6) Comprehensively construct each objective. For each objective $U_j = \sum_{i=1}^{n} w_j p_{ij}$.

## 5.4. Hypotheses testing

Ordinary least squares (OLS) regression is a fundamental method used to understand the relationships between variables, which we followed in this study for hypothesis testing. According to Esfahbodi et al.

(2016), correlations between variables should be examined prior to regression analysis. As shown in Table 5, all variables exhibit reasonable correlations with each other, which supports the validity of the subsequent regressions and suggests no major concerns with multicollinearity.

The hypotheses are formulated into the following OLS models:

$$ \text{STCSO}_i = \beta_{10} + \beta_{11}\text{FCSP}_i + \beta_{12}\text{ICSP}_i + \beta_{13}\sum \text{Controls}_{i,t} + \varepsilon_i \quad (\text{Model 1}) $$

$$ \text{OIM}_i = \beta_{20} + \beta_{21}\text{FCSP}_i + \beta_{22}\text{ICSP}_i + \beta_{23}\sum \text{Controls}_{i,t} + \varepsilon_i \quad (\text{Model 2}) $$

$$ \text{LTCSO}_i = \beta_{30} + \beta_{31}\text{FCSP}_i + \beta_{32}\text{ICSP}_i + \beta_{33}\sum \text{Controls}_{i,t} + \varepsilon_i \quad (\text{Model 3}) $$

$$ \text{OIN}_i = \beta_{40} + \beta_{41}\text{FCSP}_i + \beta_{42}\text{ICSP}_i + \beta_{43}\sum \text{Controls}_{i,t} + \varepsilon_i \quad (\text{Model 4}) $$

According to Table 6, the control variables, firm age and size, have insignificant effects on all dependent variables across all models. Analysis of Model 1 indicates that the effect of implementing finite CSPs on short-term tangible CSO is significant and positive ($\beta = 0.504, p = .000$), stronger than the effect of infinite CSPs ($\beta = 0.396, p = .000$).

Model 2 results show that both finite and infinite CSPs have positive impacts on OIM, with infinite CSPs being more significant ($\beta = 0.549, p = .000$) compared to finite CSPs ($\beta = 0.162, p = .000$).

In Model 3, both finite ($\beta = 0.408, p = .000$) and infinite CSPs ($\beta = 0.409, p = .000$) significantly and positively affect long-term CSO, with the effect of infinite CSPs being slightly stronger.

Model 4 suggests that while both finite and infinite CSPs develop OIN capability, the effect of finite CSPs ($\beta = 0.486, p = .000$) is significantly stronger than that of infinite CSPs ($\beta = 0.280, p = .000$).

In summary, hypotheses H1a, H1b, H2a, H2b, H3a, and H4a are supported by the results, while hypotheses H3b and H4b are not.

## 5.5. Robustness test

We assessed the robustness and sensitivity of our analysis and results following Sullivan et al. (2023). First, we conducted structural equation modeling (SEM) to compare the results with those obtained from EWM and OLS regressions. Given the relatively small sample size and high model complexity, we employed partial least squares structural equation

<table><caption>**Table 5**
Correlations between variables.</caption><thead><tr><th></th><th>FCSP</th><th>ICSP</th><th>OIM</th><th>OIN</th><th>STCSO</th><th>LTCSO</th><th>Age</th><th>Size</th></tr></thead><tbody><tr><th>FCSP</th><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>ICSP</th><td>0.469<sup>b</sup></td><td>1.000</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>OIM</th><td>0.436<sup>b</sup></td><td>0.639<sup>b</sup></td><td>1.000</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>OIN</th><td>0.636<sup>b</sup></td><td>0.487<sup>b</sup></td><td>0.643<sup>b</sup></td><td>1.000</td><td></td><td></td><td></td><td></td></tr><tr><th>STCSO</th><td>0.637<sup>b</sup></td><td>0.490<sup>b</sup></td><td>0.417<sup>b</sup></td><td>0.545<sup>b</sup></td><td>1.000</td><td></td><td></td><td></td></tr><tr><th>LTCSO</th><td>0.625<sup>b</sup></td><td>0.553<sup>b</sup></td><td>0.501<sup>b</sup></td><td>0.635<sup>b</sup></td><td>0.854<sup>b</sup></td><td>1.000</td><td></td><td></td></tr><tr><th>Age</th><td>0.084</td><td>0.038</td><td>-0.005</td><td>-0.009</td><td>0.015</td><td>0.016</td><td>1.000</td><td></td></tr><tr><th>Size</th><td>0.119<sup>a</sup></td><td>0.086</td><td>-0.091</td><td>-0.006</td><td>0.072</td><td>0.085</td><td>0.524<sup>b</sup></td><td>1.000</td></tr></tbody></table>

**Note(s).**

***p < 0.001.

a p < 0.05.

b p < 0.01.
<table><caption>**Table 6**
Regression results.</caption><thead><tr><th rowspan="3">Variables</th><th colspan="2">Model 1</th><th colspan="2">Model 2</th><th colspan="2">Model 3</th><th colspan="2">Model 4</th></tr><tr><th colspan="2">DV: STCSO</th><th colspan="2">DV: OIM</th><th colspan="2">DV: LTSCO</th><th colspan="2">DV: OIN</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Constant</td><td>5.1638<sup>c</sup><br>(0.7400)</td><td>-0.1283<br>(0.6863)</td><td>7.0288<sup>c</sup><br>(0.5825)</td><td>2.4311<sup>c</sup><br>(0.5231)</td><td>6.4965<sup>c</sup><br>(0.6408)</td><td>1.6016<sup>b</sup><br>(0.5725)</td><td>6.0839<sup>c</sup><br>(0.6396)</td><td>1.6808<sup>b</sup><br>(0.5955)</td></tr><tr><td>Independent variable</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FCSP</td><td></td><td>0.5040<sup>c</sup><br>(0.0651)</td><td></td><td>0.1620<sup>b</sup><br>(0.0496)</td><td></td><td>0.4084<sup>c</sup><br>(0.0543)</td><td></td><td>0.4860<sup>c</sup><br>(0.5650)</td></tr><tr><td>ICSP</td><td></td><td>0.3960<sup>c</sup><br>(0.0662)</td><td></td><td>0.5485<sup>c</sup><br>(0.0505)</td><td></td><td>0.4092<sup>c</sup><br>(0.0553)</td><td></td><td>0.2801<sup>c</sup><br>(0.575)</td></tr><tr><td>Control variable</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Size</td><td>0.0751<br>(0.0678)</td><td>-0.0135<br>(0.0514)</td><td>-0.0346<br>(0.0534)</td><td>-0.1004<sup>a</sup><br>(0.0392)</td><td>0.0944<br>(0.0587)</td><td>0.0148<br>(0.0429)</td><td>0.0095<br>(0.0586)</td><td>-0.0669<br>(0.0446)</td></tr><tr><td>Age</td><td>-0.0268<br>(0.0617)</td><td>-0.0377<br>(0.0466)</td><td>0.0300<br>(0.0486)</td><td>0.0337<br>(0.0355)</td><td>-0.0398<br>(0.0535)</td><td>-0.0472<br>(0.0388)</td><td>0.0033<br>(0.0534)</td><td>-0.0090<br>(0.0404)</td></tr><tr><td>Fixed effect</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Location</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Interviewee position</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Supply chain position</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ownership</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>N</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td></tr><tr><td>R<sup>2</sup></td><td>0.153</td><td>0.524</td><td>0.126</td><td>0.539</td><td>0.119</td><td>0.541</td><td>0.099</td><td>0.490</td></tr><tr><td>ΔR<sup>2</sup></td><td>0.089</td><td>0.484</td><td>0.060</td><td>0.501</td><td>0.053</td><td>0.502</td><td>0.032</td><td>0.448</td></tr></tbody></table>

Note(s): Standard errors in parentheses.

a p < .05.

b p < .01.

c p < .001.

modeling (PLS-SEM). As shown in Table 7, the results from PLS-SEM are mostly consistent with those from the main analysis except for the effect of ICSP on OIN. This could be caused by the underlying difference between two methods, and the significant change of the ICSP variable due to internal consistency among items for PLS-SEM.

Additionally, we conducted cluster analysis to evaluate the sensitivity of our regression results. This analysis, based on clustering firms by location and annual revenue, indicates that firms located in the same geographical area and with similar annual revenues tend to exhibit similar levels of CSPs and CSOs. As shown in Table 8, after accounting for cluster effects, the results remain consistent with the main analysis in terms of significance levels, directions, and magnitudes of the causal relationships between CSPs and CSOs.

## 6. Discussions

This study introduces a new paradoxical approach to the time dimension of CS. Building on existing studies and the theory of finite and infinite games, we categorize widely acknowledged CSPs into two types: finite CSPs for short-term survival in a competitive environment and infinite CSPs for sustaining long-term business operations. Findings from a survey of 274 Chinese automotive firms suggest an overall positive tangible and intangible CSOs of both types of CSPs. The results support, yet simultaneously contradict, our argument, as discussed below.

Model 1 and Model 3 results indicate that both finite and infinite CSPs positively affect short- and long-term tangible CSOs, aligning with past studies (e.g., Yang et al., 2011; Sajan et al., 2017). Surprisingly, while existing studies suggest that infinite CSPs may require a longer period to benefit the implementing firm (Du et al., 2011), our study finds a much quicker return. This suggests a shift in mindset among companies from instrumental thinking to a paradoxical approach, recognizing the value of all CSPs especially the infinite. Although existing works such as Carmine and De Marchi (2022) have found an insignificant effect of CS paradoxes on firm economic performance, our findings indicate that paradoxical CSPs are perceived as beneficial by automotive manufacturing companies.

Results from Model 2 and Model 4 show a positive impact of CSPs on

organizational capability development. Both finite and infinite CSPs contribute to OIM and OIN, consistent with Peng et al. (2011), who argue that implementing operational practices is vital for organizational learning and capability development. However, it is surprising to see that infinite CSPs have a stronger positive effect on OIM than finite CSPs, while finite CSPs have a more significant effect on OIN compared to infinite CSPs. A possible explanation is that infinite CSPs are generally stakeholder-centric, and the collective input from various stakeholders—through knowledge sharing, trust, and collaborative problem-solving—benefits the firm’s capability to continuously improve. Conversely, firms pursuing innovation capability often invest heavily in internal absorptive capacity, particularly through employee training. The implementation of finite CSPs frequently involves approaches such as cross-functional employee involvement (Longoni and Cagliano, 2015), making them effective in nurturing OIN.

## 7. Conclusion

### 7.1. *Theoretical contributions*

The theoretical contributions of our study are twofold. First, it contributes to the business sustainability literature and the paradoxical perspective on CS by focusing on the definition-embedded but empirically neglected time dimension of CS. For decades, CS has been studied from an instrumental approach, resulting in win-win and trade-off perspectives. However, researchers (e.g., Hahn et al., 2018; Carmine and De Marchi, 2023) argue that CS is far more complex and dynamic, involving various tensions and synergies whose proper management can bring broader benefits. Consequently, a growing body of paradox discussions on CS has emerged in the past decade. Existing contributions, however, are predominantly conceptual and focus mainly on tension management (e.g., Hahn et al., 2015; Haffar and Searcy, 2017; Weiser et al., 2017; Zehendner et al., 2021; Carmine and De Marchi, 2022). How to empirically approach the time dimension, however, remains unclear (Hörisch et al., 2014).

Our study is among the earliest attempts to bridge the gap between the CS paradox and the time dimension of CS by regarding short- and
<table><caption>**Table 7**
Robustness test 1: PLS-SEM.</caption><caption>Panel A. Composite Reliability and Average Variance Extracted (AVE) of PLS-SEM</caption><thead><tr><th>Construct</th><th>Indicator (label)</th><th>Cronbach's Alpha (α)</th><th>AVE</th><th>Factor loading</th></tr></thead><tbody><tr><td rowspan="6">FCSP</td><td>FCSP3: We undertake programs for quality improvement and control</td><td rowspan="6">0.879</td><td rowspan="6">0.624</td><td>0.749</td></tr><tr><td>FCSP6: We have systematic procedures to ensure all the equipment is under regular maintenance</td><td>0.821</td></tr><tr><td>FCSP7: We make extensive use of statistical techniques to reduce process variance</td><td>0.845</td></tr><tr><td>FCSP9: Our equipment is grouped to produce a continuous flow of families of products</td><td>0.796</td></tr><tr><td>FCSP10: We have Environmental Management Certification such as ISO 14000</td><td>0.78</td></tr><tr><td>FCSP11: We apply filters and controls for emissions and discharges</td><td>0.743</td></tr><tr><td rowspan="8">ICSP</td><td>ICSP2: We use Life Cycle Analysis (LCA) for product design</td><td rowspan="8">0.896</td><td rowspan="8">0.615</td><td>0.763</td></tr><tr><td>ICSP3: We have clear environmental management policy</td><td>0.828</td></tr><tr><td>ICSP4: We provide regular environmental training for all employees</td><td>0.814</td></tr><tr><td>ICSP5: We systematically reduce the use of water, energy, and packaging</td><td>0.742</td></tr><tr><td>ICSP6: We have clear recycling procedures applied to water, energy, and packaging</td><td>0.791</td></tr><tr><td>ICSP7: We make continuous efforts to develop products with lower environmental impact</td><td>0.774</td></tr><tr><td>ICSP8: We make continuous efforts to develop processes with lower environmental impact</td><td>0.775</td></tr><tr><td rowspan="4">OIM</td><td>OIM1: Continuous standardization of production processes</td><td rowspan="4">0.863</td><td rowspan="4">0.647</td><td>0.858</td></tr><tr><td>OIM2: Continuously simplification of production processes</td><td>0.787</td></tr><tr><td>OIM3: Continuous reduction of waste and variance in the production process</td><td>0.745</td></tr><tr><td>OIM5: Long-term continuous increase and application of process understanding</td><td>0.809</td></tr><tr><td rowspan="4">OIN</td><td>OIM6: Continuous learning from past success and failure to improve processes</td><td rowspan="4">0.883</td><td rowspan="4">0.588</td><td>0.818</td></tr><tr><td>OIN1: We pursue long-range programs, in order to acquire manufacturing capabilities in advance of our needs</td><td>0.741</td></tr><tr><td>OIN2: We make an effort to anticipate the potential of new manufacturing practices and technologies</td><td>0.737</td></tr><tr><td>OIN3: Our plant stays on the leading edge of new technology in our industry</td><td>0.767</td></tr><tr><td rowspan="6">STCSO</td><td>OIN4: We are constantly thinking of the next generation of manufacturing technology</td><td rowspan="6">0.945</td><td rowspan="6">0.694</td><td>0.767</td></tr><tr><td>OIN5: Innovations created that made the prevailing processes obsolete</td><td>0.784</td></tr><tr><td>OIN6: Innovations created that fundamentally changed the prevailing processes</td><td>0.801</td></tr><tr><td>OIN7: Innovations created that made the existing expertise in prevailing processes obsolete</td><td>0.769</td></tr><tr><td>STCSO1: ROA</td><td>0.768</td></tr><tr><td>STCSO2: ROS</td><td>0.758</td></tr><tr><td rowspan="8">LTCSO</td><td>STCSO3: Profit</td><td rowspan="8">0.920</td><td rowspan="8">0.611</td><td>0.800</td></tr><tr><td>STCSO4: The reduction of air emissions</td><td>0.888</td></tr><tr><td>STCSO5: The reduction of wastewater generation</td><td>0.882</td></tr><tr><td>STCSO6: The reduction of solid waste disposal</td><td>0.880</td></tr><tr><td>STCSO7: The reduction of hazardous/harmful/toxic materials</td><td>0.875</td></tr><tr><td>STCSO8: The reduction of energy consumption</td><td>0.859</td></tr><tr><td>STCSO9: Compliance with environmental regulation</td><td>0.773</td></tr><tr><td>LTCSO1: Tobin's q</td><td>0.746</td></tr><tr><td>LTCSO2: Cost savings achieved from green and social initiatives</td><td>0.774</td></tr><tr><td>LTCSO3: Sales increase achieved from green and social initiatives</td><td>0.825</td></tr><tr><td>LTCSO4: The development of products with fewer negative environmental impacts</td><td>0.764</td></tr><tr><td>LTCSO5: The development of processes with fewer negative environmental impacts</td><td>0.785</td></tr><tr><td>LTCSO6: Improvement of corporate social image</td><td>0.822</td></tr><tr><td>LTCSO7: Improvement of stakeholder relations</td><td>0.807</td></tr><tr><td>LTCSO8: Improvement of customer satisfaction</td><td>0.768</td></tr><tr><td>LTCSO9: Improvement of employee satisfaction</td><td>0.742</td></tr></tbody></table>

<table><caption>Panel B. Estimation results of PLS-SEM with Bootstrapping</caption><thead><tr><th>Path</th><th>Coefficient</th><th>p-value</th><th>R<sup>2</sup><sub>a</sub></th></tr></thead><tbody><tr><td>Model 1: FCSP → STCSO</td><td>0.403</td><td>0.004</td><td rowspan="2">0.382</td></tr><tr><td>ICSP → STCSO</td><td>0.243</td><td>0.075</td></tr><tr><td>Model 2: FCSP → OIM</td><td>0.337</td><td>0.002</td><td rowspan="2">0.518</td></tr><tr><td>ICSP → OIM</td><td>0.414</td><td>0.001</td></tr><tr><td>Model 3: FCSP → LTCSO</td><td>0.065</td><td>0.607</td><td rowspan="2">0.428</td></tr><tr><td>ICSP → LTCSO</td><td>0.604</td><td>0.000</td></tr><tr><td>Model 4: FCSP → OIN</td><td>0.560</td><td>0.000</td><td rowspan="2">0.408</td></tr><tr><td>ICSP → OIN</td><td>0.094</td><td>0.458</td></tr></tbody></table>

long-term CS as both process and outcome. It is novel in combining the finite and infinite games theory, the mindset theory and the paradox concept to conceptualize and operationalize the time-based CS paradox. We re-categorize widely acknowledged CSPs and empirically study their short- and long-term CSOs through a paradoxical lens. Since implementing CSPs indicates resource commitments, finite and infinite CSPs compete for resources while at the same time facilitate each other, forming a paradox. By combining these theories, we propose a novel approach to understanding the co-implementation of various CSPs by firms and the rationale behind it.

Second, our study contributes to the cleaner production literature by including intangible CSOs alongside tangible ones when discussing benefits of time-based paradoxical CSPs. Ambidextrous organizations, which balance improvement and innovation, have been found to be more successful and sustainable (Zimmermann et al., 2018). However, how ambidextrous capabilities, such as OIM and OIN, can be developed through paradoxical CSPs is less clear. Combining paradox theory and the finite and infinite games perspective, we find empirical evidence that ambidextrous operational capabilities of improvement and innovation are intangible benefits of implementing finite and infinite CSPs.
<table><caption>**Table 8**

Robustness test 2: cluster analysis.</caption><thead><tr><th rowspan="3">Variables</th><th colspan="3">Model 1</th><th colspan="3">Model 2</th><th colspan="3">Model 3</th><th colspan="3">Model 4</th></tr><tr><th colspan="3">DV: STCSO</th><th colspan="3">DV: OIM</th><th colspan="3">DV: LTSCO</th><th colspan="3">DV: OIN</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th></tr></thead><tbody><tr><td>Constant</td><td>-0.1283<br>(0.8891)</td><td>-0.1283<br>(1.1837)</td><td>-0.1283<br>(0.5658)</td><td>2.4311<sup>c</sup><br>(0.6127)</td><td>2.4311<sup>b</sup><br>(0.3859)</td><td>2.4311<sup>b</sup><br>(0.6103)</td><td>1.6016<sup>a</sup><br>(0.6477)</td><td>1.6016<br>(0.8101)</td><td>1.6016<sup>a</sup><br>(0.5916)</td><td>5.1638<sup>c</sup><br>(0.7400)</td><td>5.1638<sup>c</sup><br>(0.7400)</td><td>1.6808<sup>a</sup><br>(0.7135)</td></tr><tr><td>Independent variable</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FCSP</td><td>0.5040<sup>c</sup><br>(0.1022)</td><td>0.5040<sup>c</sup><br>(0.0637)</td><td>0.5040<sup>c</sup><br>(0.0385)</td><td>0.1620<sup>a</sup><br>(0.0684)</td><td>0.1620<br>(0.0744)</td><td>0.1620<br>(0.0875)</td><td>0.4084<sup>c</sup><br>(0.0826)</td><td>0.4084<sup>c</sup><br>(0.0506)</td><td>0.4084<sup>c</sup><br>(0.0307)</td><td>0.4860<sup>c</sup><br>(0.0970)</td><td>0.4860<sup>b</sup><br>(0.0921)</td><td>0.4860<sup>b</sup><br>(0.1234)</td></tr><tr><td>ICSP</td><td>0.3960<sup>c</sup><br>(0.0618)</td><td>0.3960<sup>c</sup><br>(0.0399)</td><td>0.3960<sup>c</sup><br>(0.0650)</td><td>0.5485<sup>c</sup><br>(0.0629)</td><td>0.5485<sup>c</sup><br>(0.0482)</td><td>0.5485<sup>c</sup><br>(0.0703)</td><td>0.4092<sup>c</sup><br>(0.0593)</td><td>0.4092<sup>b</sup><br>(0.0597)</td><td>0.4092<sup>c</sup><br>(0.0572)</td><td>0.2801<sup>c</sup><br>(0.0676)</td><td>0.2801<sup>b</sup><br>(0.0517)</td><td>0.2801<sup>a</sup><br>(0.0839)</td></tr><tr><td>Control variable</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Size</td><td>-0.0135<br>(0.0505)</td><td>-0.0135<br>(0.0651)</td><td>-0.0135<br>(0.0379)</td><td>-0.1004<sup>a</sup><br>(0.0443)</td><td>-0.1004<sup>a</sup><br>(0.0351)</td><td>-0.1004<sup>a</sup><br>(0.0389)</td><td>0.0148<br>(0.0404)</td><td>0.0148<br>(0.0223)</td><td>0.0148<br>(0.0388)</td><td>-0.0669<br>(0.0453)</td><td>0.0669<sup>b</sup><br>(0.0139)</td><td>-0.0669<br>(0.0408)</td></tr><tr><td>Age</td><td>-0.0377<br>(0.0456)</td><td>-0.0377<br>(0.0585)</td><td>-0.0377<br>(0.0341)</td><td>0.0337<br>(0.0434)</td><td>0.0337<br>(0.0322)</td><td>0.0337<br>(0.0381)</td><td>-0.0472<br>(0.0409)</td><td>-0.0472<br>(0.0325)</td><td>-0.0472<br>(0.0350)</td><td>-0.0090<br>(0.0375)</td><td>-0.0090<br>(0.0257)</td><td>-0.0090<br>(0.0308)</td></tr><tr><td>Fixed effect</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Location</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Interviewee position</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Supply chain position</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ownership</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Robust</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Cluster (location)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cluster (revenue)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td><td>274</td></tr><tr><td>R<sup>2</sup></td><td>0.153</td><td>0.524</td><td>0.524</td><td>0.126</td><td>0.539</td><td>0.539</td><td>0.119</td><td>0.541</td><td>0.541</td><td>0.153</td><td>0.490</td><td>0.490</td></tr></tbody></table>

Note(s): Standard errors in parentheses.

Furthermore, the implementing organization realize tangible benefits from finite and infinite CSPs on a continuous basis. Companies can thus capture wider benefits when finite and infinite mindsets towards CS are in place at the same time.

## 7.2. Practical implications

The results of our study offer valuable insights for manufacturing practitioners, policymakers, and automotive stakeholders, including customers.

First, our study helps practitioners understand CS, particularly the embedded time dimension. Recently, sustainability or CS has become a buzzword and the "right and good" thing to do. However, what it really means and how it can be effectively incorporated into business operations remains ambiguous. Our findings show that CS is not just about pursuing better economic and environmental performance in the short term or taking care of existing stakeholders. Instead, it is the firm's

ability to do so while leaving sufficient space and creating opportunities for future development. Managers should shift from a simple understanding of CS as being environmentally and socially responsible to one that is dynamic and paradoxical. This mindset change is key to successful decision-making on CS.

Our findings also encourage corporate executives to adopt a paradoxical perspective in designing CS strategies, as illustrated in Fig. 3. With resource constraints, it is not feasible for firms to adopt all sustainability practices at equal levels. Strategic decisions on balancing CS elements at different points in different situations are thus needed. A sustainable organization should be able to invest limited resources in a suitable combination of finite and infinite CSPs that maximizes short-term gains while ensuring future prosperity. Although there is no one-size-fits-all solution regarding the finite-infinite CSPs ratio, our study provides useful insights on the benefits of each category of CSPs to the implementing firm over different time frames. These insights can guide firms in allocating resources to CSPs based on their own visions and

<figure><img src="image_10.png" /><figcaption>**Fig. 3.** Deployment framework for manufacturing practitioners.</figcaption></figure>

<footnote>a p < .05.</footnote>

<footnote>b p < .01.</footnote>

<footnote>c p < .001.</footnote>
objectives. For instance, finite CSPs seem to be more effective and certain than their infinite counterparts in improving short-term performance. This means they should receive relatively more attention if the organization currently prioritizes cost reduction and efficiency and cannot afford too much risk. However, infinite CSPs also play a significant role in nurturing capabilities and enhancing future performance, so a decent level of investment in these practices is crucial as well.

Our study also offers a reference for policymakers interested in promoting sustainability. Currently, the Chinese automotive industry is transitioning towards producing and delivering electric vehicles (EVs), which aligns with the country's dual carbon goals (Chen et al., 2023). Along with more environmentally friendly products, the production process needs to be more sustainable, given the current volume and expected market growth. Therefore, policies should encourage sustainability practices, such as ecological process innovation and philanthropic activities, even if companies do not solve they can bring them quick benefits. Resource support can largely think the organization's lack of motivation to engage in long-term sustainability activities. As companies tend to respond to the requirements of key stakeholders, customers can extend their demand beyond the product to the production process.

### 7.3. Limitations and future work

Despite significant theoretical and practical contributions, our study has limitations that suggest directions for future research.

First, due to the novelty of the time dimension in the CS literature, exploratory studies using qualitative approaches are needed to advance this concept. Specifically, this concept should be developed and applied in the field of cleaner production. Second, fully established measurement scales for key variables such as finite and infinite CSPs and short- and long-term CSOs are lacking. Future research should focus on developing rigorous measurement scales for these constructs, which can then be used to advance theories related to the time dimension of CS. Third, our findings are based entirely on subjective, self-reported data from a single respondent in each sample firm. Although this type of data is widely used in CS studies, future research should combine subjective data with objective data to enhance the reliability of results and minimize social desirability bias. Fourth, our study is conducted within a single sector and country, with a relatively small sample size, which may limit the generalizability of our findings. Cross-sector and cross-country studies are recommended to address this limitation. Fifth, we encourage researchers to be innovative in operationalizing the time dimension of CS in cleaner production, which can deepen our understanding of CS paradoxes. Last, longitudinal studies are ideal when considering the time dimension of CS. Future studies can take various methods such as observations or use archival data to capture the evolution of CS across time. These future research directions will help build on our findings and further develop the understanding of the time dimension in CS.

### CRediT authorship contribution statement

**Lin Wu:** Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Project administration, Methodology, Data curation, Conceptualization.
**Nachiappan Subramanian:** Validation, Supervision, Resources, Conceptualization.
**Kulwant S. Pawar:** Supervision, Resources, Conceptualization.
**Miao Wang:** Visualization, Formal analysis.
**Ajay Kumar:** Supervision, Conceptualization.
**Kim Hua Tan:** Supervision, Conceptualization.

### Declaration of competing interest

There are no relevant financial or non-financial competing interests among the authors.

### Data availability

Data will be made available on request.

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