Leagile supply chains and sustainable business performance: application of total interpretive structural modelling

Abstract

This study aims to develop an integrated model for the leagile (lean + agile) supply chain strategy to achieve sustainable business performance across the triple bottom line, including economic, environmental, and social performance. We draw upon a literature review and experts’ opinion, using total interpretive structural modelling (TISM), to determine interrelationships among leagile supply chain practices and sustainable business performance measures. To this end, first, unique and common practices of lean and agile supply chain strategies, i.e. leagile building blocks, are identified through a synthesis of the literature. Second, semi-structured interviews with four experts are conducted to gain an understanding of the mutual relationships among the leagile building blocks and to meticulously adjudicate their interactions with sustainable business performance measures. Validated by two independent researchers and literature enfolding, the final TISM model provides a 10-level hierarchical representation of the leagile supply chain strategy for sustainable business performance.

Keywords:

• Leagile supply chain
• lean
• agile
• sustainable business performance
• total interpretive structural modelling

1. Introduction

Nowadays companies are striving to find out an appropriate supply chain strategy to increase the level of competitiveness (Fadaki, Rahman, and Chan 2020; Frohlich and Westbrook 2001). Among various strategies (e.g. resilient supply chain and green supply chain, to name a few), ‘leanness’ and ‘agility’ have gained relatively more attention, both in research and practice, to be integrated into supply chain strategies to achieve competitive advantage and ameliorate organizational performance (Inman et al. 2011; Qrunfleh and Tarafdar 2013).

Lean supply chain (LSC, hereafter) is a strategy to create value for customers through eliminating waste (or non-value-added activities) in its various forms within the firm and throughout the whole supply chain (Jasti and Kodali 2015; Naranjo, Menor, and Johnson 2023). LSC not only focuses on waste removal at the shop-floor and mainly for production-related actions, but emphasizes that waste associated with external factors involved in the upstream and downstream supply chains (i.e. customers and suppliers) should be also minimized (Mohaghegh, Blasi, and Größler 2021). Despite the positive impacts of ‘leanness’ for cost efficiency (e.g. Tortorella, Miorando, and Marodin 2017; Vinodh and Aravindraj 2013), LSC seems to be inadequate to remain competitive in today’s volatile markets characterized by an increasing pace and complexity of business environments (Vázquez-Bustelo, Avella, and Fernández 2007). Christopher (2000), for example, argues that lean works best in high volume, low variety, and predictable environments. Similarly, Mohaghegh, Blasi, and Größler (2021) explain that LSC practices can be seen as ordinary (or zero-order) capabilities that, although they are effective for short-term, often fail to result in sustainable outcomes in highly dynamic environments. Rather, the authors claim that firms are also required to boost their ‘agility’ through meeting unpredictable customer demands, managing ever-shorter delivery times, and adjusting to advanced technological developments. Operations and supply chain researchers consensually advocate for agile supply chain (ASC, hereafter) as a recommended strategy to quickly and effectively match its resources, processes, and capabilities to the constantly changing needs of the marketplace (Blome, Schoenherr, and Rexhausen 2013; Gunasekaran 1998; Mohaghegh, Åhlström, and Blasi 2023).

A review of the literature discloses that although one strategy might be dominant (e.g. lean), other strategies (e.g. agile) can be simultaneously adopted to successfully deal with highly dynamic environments (Christopher 2000; Fadaki, Rahman, and Chan 2020; Furlan, Grandinetti, and De Toni 2023). The major findings of a study conducted by Fadaki, Rahman, and Chan (2020) indicate that business performance improves when a more balanced ‘leagile’ (i.e. lean + agile) strategy is employed. This is coherent with Naylor, Naim, and Berry (1999) perspective that ‘neither paradigm is better nor worse than the other, indeed they are complementary within the correct supply chain strategy’ (117). With a particular focus on organizational ambidexterity, i.e. getting the best of both worlds, Furlan, Grandinetti, and De Toni (2023) promote the simultaneous adoption of LSC and ASC as seemingly conflicting strategies to manage the exploitation-exploration dilemma. Adopting paradox theory, the authors suggest a both/and approach (adopting lean and agile strategies simultaneously) instead of an either/or approach (pure lean or pure agile) to exploit (through leanness) and explore (through agility) opportunities. Therefore, a switch from unilateral strategies (purely lean or purely agile) to a hybrid one, such as leagility is of critical importance for an effective supply chain strategy. Existing studies also provide empirical evidence for the effectiveness of leagility for both firm performance and supply chain performance. For instance, Rahimi and Alemtabriz (2022) identify 23 leagile supply chain practices (14 LSC practices and 9 ASC practices) in the supply chain of military industries and demonstrate how those practices possibly interplay with each other, leading to the improvements in the overall performance of the supply chain. In another study, Fadaki, Rahman, and Chan (2020) create the ‘deviation from leagility (DFL) index in the context of Australian manufacturing firms. The main findings of this research suggest that adhering to a balanced strategy, indicated by lower deviation from leagility, results in enhanced performance across various measures, including cost, flexibility, delivery speed, profitability, and market growth.

However, considering ‘sustainability’ as a contemporary challenge both in management research and practice, business success is no longer solely determined by profitability. Following the triple-bottom line (TBL) perspective initially proposed by Elkington (1997), economic, environmental, and social performance are equally crucial for today’s business success. Since Elkington’s (1997) original contribution, the TBL has become the primary means to measure and assess sustainable business performance. To advance sustainable business performance, sustainability goals (e.g. resource efficiency) should be integrated into operations and day-to-day business practices. Therefore, with no exception for supply chain strategies, LSC and ASC should also encompass environmental friendly actions and socially responsible activities (e.g. Geyi et al. 2020; Mohaghegh, Blasi, and Größler 2021).

In spite of the importance of the hybrid leagile supply chain strategy in our today’s dynamic business environments and the growing emphasis on sustainability driven by stakeholder pressure, there is a paucity of research on whether and how LSC and ASC strategies can jointly, i.e. leagile supply chain, embrace sustainability and simultaneously contribute to economic, environmental, and social performance. Existing studies predominantly treat LSC and ASC as individual strategies in isolation and examine their separate impacts on sustainability performance. Chavez et al. (2020), for instance, empirically show that lean practices appear to positively affect both social sustainability performance and environmental sustainability performance. El-Khalil and Mezher (2020) argue that agility improves sustainability performance and its triple dimensions, i.e. economic, environmental, and social performance. Also, Nath and Agrawal (2020) view LSC and ASC as separate strategies and argue that lean and agile practices are positively associated with social sustainability performance. The authors therefore conclude that lean and agile practices can be considered antecedents of social sustainability performance.

The lack of research on the relationship between the leagile supply chain strategy and sustainable business performance has also led Ciccullo et al. (2018) to issue a call to explore how sustainability can be integrated into the leagile paradigm. Naim and Gosling (2011) similarly discuss the necessity of further clarification on the associated performance characteristics of the leagile supply chains. In a recent study, Ding, Ferràs Hernández, and Agell Jané (2023) study lean and agile paradigms pointing out that there has been limited emphasis placed on the sustainability aspect of manufacturing performance to date. Motivated by this gap in the literature, the present study is an attempt to examine whether, or not, and how LSC and ASC coexist for advanced sustainable business performance. More specifically, this study aims to develop an integrated leagile model, where the core idea is to indicate how leagile supply chain practices interact for sustainable business performance, i.e. TBL, giving equal importance to economic, environmental, and social demands.

To this end, total interpretive structural modelling (TISM) as a structured methodology that combines literature review and experts’ opinions was employed. First, supply chain leagile practices, which we term ‘leagile building blocks’, were identified by synthesizing the literature on operations and supply chain management. Second, four practitioners from manufacturing companies were consulted to obtain their inputs on how the leagile building blocks interconnect to achieve sustainable business performance. With several expedients in place to ensure the consistency, reliability, and validity of our findings, such as literature enfolding and feedback from two independent researchers, the final TISM model presents a 10-level hierarchical structure that outlines the relationships between the leagile building blocks and sustainable business performance measures.

This study adds to the extant literature on operations and supply chain management in several ways. First, this is an innovative empirical attempt to propose an integrated leagile model for sustainable business performance across the TBL. Our proposed model sheds light on how unique and common practices of LSC and ASC, in an integrated leagile supply chain model, interact for economic, environmental, and social benefits. Our study therefore can serve as a response to Ciccullo et al. (2018) and Ding, Ferràs Hernández, and Agell Jané (2023) calls for greater attention to the integration between leagility and sustainability. Moreover, by proposing the building blocks of a leagile supply chain strategy, we also address the need initially identified by Naim and Gosling (2011) and recently reiterated by Bhamra et al. (2021) that future studies should provide a comprehensive understanding of the characteristics, attributes, and effective implementation practices of the leagile supply chain strategy. Second, the main findings of this study challenge the traditional viewpoint of treating lean and agile approaches as divergent strategies or mutually exclusive concepts (e.g. Goldsby, Griffis, and Roath 2006; Richards 1996) and provide additional evidence on how these two strategies should be developed, i.e. whether lean practices are necessary to develop agile practices or vice versa (Inman et al. 2011; Oliveira-Dias et al. 2023). The proposed leagile supply chain model shows that LSC practices can serve as the pre-requisite of ASC practices, thereby reinforcing the idea of considering ‘leanness’ and ‘agility’ as supportive strategies. In this regard, we also add to the existing body of literature the concept of supply chain ambidexterity. We argue that the simultaneous adoption of both strategies, each with its own objectives and implementation practices, is necessary for a firm to have an ambidextrous supply chain, pursuing sustainable performance. This study also offers significant implications for managers and practitioners. Our proposed model provides an actionable guideline for senior managers to prioritize their resources and efforts towards the implementation of the most critical leagile practices to enhance triple dimensions of sustainable business performance.

The remainder of the paper is structured as follows. Section 2 provides a review of the relevant literature pertaining to the lean, agile, and leagile supply chain strategies. In Section 3, the research setting is outlined with particular attention given to data collection and data analysis. The primary findings of the study are presented in Section 4. Finally, Section 5 concludes the paper with the study contribution, managerial implications, and limitations of the study.

2. Theoretical background

2.1. Lean supply chain strategy

Lean production is the most comprehensive production system to improve a firm’s operational performance by eliminating different types of waste and variability sources (e.g. Cua, McKone, and Schroeder 2001). However, a synthesis of the literature indicates that a successful implementation of lean involves expanding and linking internal processes with external actors of the value chain, i.e. customers and suppliers (Bortolotti, Boscari, and Danese 2015; Frohlich and Westbrook 2001; Gohr 2023; Mohaghegh, Blasi, and Größler 2021; Raji et al. 2021). Implementing lean principles at the supply chain level refers to an integrative lean supply chain (LSC) strategy. Naranjo, Menor, and Johnson (2023) highlight the perspective-based definition of LSC which involves a cultural approach centred on joint problem-solving and collaboration across the entire supply chain. Shah and Ward (2007) define lean as ‘a socio-technical system whose main objective is to eliminate waste by concurrently reducing or minimizing supplier, customer, and internal variability’ (791). Therefore, waste or variability can be found not only on the shopfloor (and for production-related activities) but also along the entire supply chain, where customers and suppliers as the key actors in the upstream and downstream supply chain are involved.

Operations strategy researchers divide LSC practices into two major types: hard and soft practices. The former type refers to a set of technical practice to eliminate waste related to production activities, whereas the latter type relates to human and relations (e.g. Bortolotti, Boscari, and Danese 2015; Mohaghegh, Blasi, and Größler 2021). Shah and Ward (2003) identify 22 manufacturing practices for lean (lot size reduction, pull system, quick changeover techniques, process capability measurements, and self-directed work teams, to name a few) and group them into 4 main bundles, namely, just-in-time (JIT), total quality management (TQM), total productive maintenance (TPM), and human resource management (HRM). The first three bundles are mainly treated in the literature as hard practices, while the fourth one is known as a soft lean practice. Bortolotti, Boscari, and Danese (2015), based on a review of existing studies, add Kaizen (or continuous improvement), customer involvement, supplier partnership, and top management leadership to the list of soft lean practices.

Hard and soft lean practices seem to have complementarity effects. As an example, Mohaghegh, Blasi, and Größler (2021) discuss the idea of ‘lean as a full package’ and argue that too much attention just to production-related (hard or technical) practices may impede successfully implementing the LSC strategy and restricting its outcomes to the short-term. The authors conclude that equal attention should be given to soft practices, such as HRM, customer involvement, and supplier partnership to sustain the lean outcomes. Likewise, Bortolotti, Boscari, and Danese (2015) propose that successful lean firms go beyond hard practices (or lean technicalities) by investing extensively in soft practices.

2.2. Agile supply chain strategy

To become more responsive to the ever-changing business environments, firms require a high level of manoeuvrability that today has come to be termed ‘agility’ (Alfalla-Luque, Luján García, and Marin-Garcia 2023; Mohaghegh, Åhlström, and Blasi 2023; Sharifi and Zhang 1999). In the face of high environmental turbulence characterized by market unpredictability and constantly changing customer requirements, the LSC strategy seems to be inadequate to remain competitive (Vázquez-Bustelo, Avella, and Fernández 2007). Christopher (2000) explains that firms cannot expect to fully reap the benefits of lean in less predictable environments, where customer demand is volatile and the requirement for variety is rather high. Instead, agility appears to be the most appropriate response to cope with turbulent and highly dynamic environments (e.g. Blome, Schoenherr, and Rexhausen 2013; Gunasekaran 1998; Meng et al. 2023; Vázquez-Bustelo, Avella, and Fernández 2007). Lin, Chiu, and Chu (2006) argue that a highly dynamic environment with changing customer requirements, intense competition, turbulent markets, and advanced technological innovations serves as the primary driver of agility. Furthermore, some studies frame agility as a dynamic capability (Aslam et al. 2018; Blome, Schoenherr, and Rexhausen 2013; Mohaghegh, Åhlström, and Blasi 2023), representing an effective strategy to gain and maintain competitive advantage in volatile environments.

Sharifi and Zhang (1999) define agility as ‘the ability to cope with unexpected changes, to survive unprecedented threats of business environment, and to take advantage of changes as opportunities’ (9). Building on the notion that change is the main driving force of agility (Lin, Chiu, and Chu 2006; Yusuf, Sarhadi, and Gunasekaran 1999), scholars relate agility to change proficiency as well, which can be further interpreted as a firm’s ability to effectively and quickly match its resources, processes, and capabilities to changing business environments (Blome, Schoenherr, and Rexhausen 2013; Iqbal, Huq, and Bhutta 2018; Mohaghegh, Blasi, and Größler 2021). Given that a higher level of agility can be achieved when supply chain partnership is well-established (Swafford, Ghosh, and Murthy 2006), the agile supply chain (ASC) strategy has emerged with an emphasis on tight relationships with supply chain partners (Alfalla-Luque, Luján García, and Marin-Garcia 2023).

Despite the varied terminologies used to describe relevant supply chain practices, researchers consensually consider ASC as a multidimensional concept. Vázquez-Bustelo, Avella, and Fernández (2007) highlight several practices for ASC and group them into five strategic areas of human resources (practices to develop highly trained, motivated and empowered people), technologies (systematic implementation and integration of advanced technologies), integration in value chain (inter-departmental integration as well as cooperation between external agents), concurrent engineering (new product/process development practices), and knowledge management (learning-based practices). In another study, Geyi et al. (2020) classify ASC practices into five main categories, which are market sensitivity, employee empowerment, process alignment, technology integration, and network collaboration.

2.3. Leagile supply chain strategy

The convergence of LSC and ASC results in the hybrid leagile supply chain strategy which aims to exploit profitable market opportunities in a cost-efficient manner (Naylor, Naim, and Berry 1999). Singh and Pandey (2015) define the leagile supply chain strategy as supplying the customers with their requirements at the right time in a lean way. ‘Leagility’ indeed can be achieved by improving flexibility and responsiveness (through agility) and eliminating waste and non-value-added activities (through leanness). Christopher (2000) promotes the leagile supply chain strategy and encourages firms to adopt leagility when a supply chain should be lean for part of the time and agile for the rest. As an example, Toyota motor corporation, known for its famous ‘lean thinking’, produces base vehicles in Japan and ships them to the U.S. in a lean manner. However, customer-specific needs and substantive customization (such as interior items and custom lighting) at distributor facilities in the U.S. are accommodated using the agile strategy (Goldsby, Griffis, and Roath 2006). The pervasiveness of the leagile supply chain strategy is highlighted by Fadaki, Rahman, and Chan (2019) who argue that 90.3% of firms employ both lean and agile strategies (or the leagile strategy) with different magnitudes. Similarly, Singh and Pandey (2015) explain that ‘the proportions between lean and agile elements in operations of a single company or the whole supply chain might be changing all the time and only very seldom find an example of pure leanness and pure agility in real business’ (39). The importance of leagility is also highlighted by a study conducted by Furlan, Grandinetti, and De Toni (2023), arguing that a firm employing lean may successfully adapt and improve its operations but it may fail to deal with quick disruptions without the development of agile capabilities. Also, an agile firm, while highly flexible, appears to struggle with efficiently improving existing processes.

The adoption of the leagile supply chain strategy can be also seen from the lens of organizational ambidexterity, supported by the notion of ‘getting the best of both worlds’ (Furlan, Grandinetti, and De Toni 2023; Mason-Jones, Naylor, and Towill 2000) or ‘right on both sides’ (Simsek 2009). Raisch and Birkinshaw (2008) define organizational ambidexterity as ‘an organization’s ability to be aligned and efficient in its management of today’s business demands while simultaneously adaptive to changes in the environments’ (375). Scholars in operations and supply chain management also consensually refer to supply chain ambidexterity as the simultaneous adoption of two distinct strategies, e.g. lean and agile strategy (Furlan, Grandinetti, and De Toni 2023) or approaches, e.g. supply chain integration and supply chain reconfiguration (Lee 2021) or even goals, e.g. supply chain responsiveness and supply chain efficiency (Aslam et al. 2018). Therefore, those firms that adopt the leagile strategy can be considered ambidextrous as they integrate two distinct strategies, namely, lean and agile, each with its own objectives and characteristics. Within the ambidextrous firms adopting leagility for their supply chains, both efficiency and responsiveness can be effectively managed, with efficiency achieved through leanness and responsiveness achieved through agility.

As the word implies, the leagile supply chain strategy includes practices that are emphasized by both LSC and ASC, referred to as leagile building blocks. While certain practices are specific to LSC (or ASC), others are commonly shared between the two strategies. Table 1 shows the leagile building blocks based on a review of the literature.

Table 1. Leagile supply chain practices and their appearance in the literature.

Key references	Research focus	Leagile supply chain practices
		JIT	TQM	TPM	HRM	CF	SP	SPV	KC	MAC	SCI	PD	MS	SCR	TU	SCL
Oliveira-Dias et al. (2023)	Lean and Agile	*	*			*						*	*	*
Naranjo, Menor, and Johnson (2023)	Lean	*					*		*	*					*
Mohaghegh, Åhlström, and Blasi (2023)	Agile					*		*		*		*	*	*	*	*
Gohr (2023)	Lean	*	*		*	*	*		*	*	*	*				*
Meng et al. (2023)	Agile											*	*	*
Lotfi and Sodhi (2022)	Agile	*	*		*	*	*				*	*	*	*	*	*
Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes (2022)	Lean and Agile	*	*			*		*					*	*
Raji et al. (2021)	Lean and Agile	*	*	*	*	*	*				*	*	*	*	*
Mohaghegh, Blasi, and Größler (2021)	Lean and Agile	*	*	*	*	*	*	*	*	*		*	*	*	*	*
Al Humdan et al. (2020)	Agile				*	*	*	*	*	*	*		*	*	*	*
Geyi et al. (2020)	Agile				*	*	*		*	*	*		*		*	*
Chavez et al. (2020)	Lean	*	*	*	*				*
Iqbal et al. (2020)	Lean and Agile	*	*		*	*	*	*	*	*		*			*	*
Green et al. (2019)	Lean	*	*			*	*					*
Virmani, Saha, and Sahai (2018)	Lean, Agile, and Leagile	*	*	*	*	*	*	*	*	*	*	*	*	*	*	*
Iqbal, Huq, and Bhutta (2018)	Lean and Agile	*	*	*	*	*	*	*	*	*		*	*	*	*	*
Tortorella, Miorando, and Marodin (2017)	Lean	*	*			*	*	*	*		*
Piercy and Rich (2015)	Lean	*	*	*	*	*	*	*
Chavez et al. (2015)	Lean	*				*	*
Alves and Alves (2015)	Lean	*		*				*	*	*						*
Dubey and Gunasekaran (2015)	Agile				*	*	*		*			*	*		*	*
Bortolotti, Boscari, and Danese (2015)	Lean	*	*	*	*	*	*		*	*
Blome, Schoenherr, and Rexhausen (2013)	Agile											*	*	*
Yusuf et al. (2012)	Agile				*	*	*				*		*	*		*
Inman et al. (2011)	Lean and Agile	*	*	*		*	*	*		*	*	*	*	*	*	*
Yang, Hong, and Modi (2011)	Lean	*	*		*
Kisperska-Moron and Haan (2011)	Leagile					*	*				*		*		*
Shah and Ward (2007)	Lean	*	*	*	*	*	*
Vázquez-Bustelo, Avella, and Fernández (2007)	Agile				*	*	*				*	*			*	*
Yusuf et al. (2004)	Agile				*	*	*			*	*		*	*	*	*
Shah and Ward (2003)	Lean	*	*	*	*
Lin, Chiu, and Chu (2006)	Agile					*	*				*	*	*		*	*
Narasimhan, Swink, and Kim (2006)	Lean and Agile	*	*		*	*	*	*	*		*	*	*	*	*	*
Swafford, Ghosh, and Murthy (2006)	Agile					*						*	*
Cua, McKone, and Schroeder (2001)	Lean	*	*	*	*	*	*	*	*	*					*
Christopher (2000)	Agile				*	*	*				*	*	*		*	*
Naylor, Naim, and Berry (1999)	Leagile										*			*		*
Gunasekaran (1998, 1999)	Agile				*	*	*	*	*	*	*	*	*	*	*	*

JIT: just-in-time; TQM: total quality management; TPM: total productive maintenance; HRM: human resource management; CF: customer focus; SP: supplier partnership; SPV: strategic planning and visioning; KC: Kaizen (continuous improvement) culture; MAC: managerial attention and commitment; SCI: supply chain integration; PD: product/process development; MS: market sensitivity; SCR: supply chain reconfiguration; TU: technology utilization; SCL: supply chain learning and information sharing.

In addition, Table 2 provides a summary of the description of the blocks and addresses how each practice might appear in extant studies.

Table 2. Leagile supply chain practices: Description and how they appear in key references.

Leagile practices	Description and relevant activities (conceptual/empirical items to operationalize practices)	Key references
Just-in-time (JIT)	Technical practices with the aim of continually eliminating production-related waste (e.g. scrape and inventory) Kanban pull system Small lot sizes Low setup times Low inventories and fast throughput JIT scheduling	Bortolotti, Boscari, and Danese 2015; Chavez et al. 2015, 2020; Cua, McKone, and Schroeder 2001; Gohr 2023; Green et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lotfi and Sodhi 2022; Mohaghegh, Blasi, and Größler 2021; Naranjo, Menor, and Johnson 2023; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Piercy and Rich 2015; Raji et al. 2021; Shah and Ward 2003; Tortorella, Miorando, and Marodin 2017; Virmani, Saha, and Sahai 2018; Yang, Hong, and Modi 2011
Total quality management (TQM)	Technical practices with the aim of improving and sustaining the quality of the product which in turn result in meeting/exceeding customer expectations Extensive use of statistical techniques Having processes under quality control Using control charts Process standardization	Bortolotti, Boscari, and Danese 2015; Chavez et al. 2020; Cua, McKone, and Schroeder 2001; Gohr 2023; Green et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lotfi and Sodhi 2022; Mohaghegh, Blasi, and Größler 2021; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Piercy and Rich 2015; Raji et al. 2021; Shah and Ward 2003; Tortorella, Miorando, and Marodin 2017; Virmani, Saha, and Sahai 2018; Yang, Hong, and Modi 2011
Total productive maintenance (TPM)	Technical practices with the aim of eliminating waste associated with low equipment availability and failures Daily planned equipment maintenance To maintain records of all equipment maintenance Routine proactive and preventive maintenance	Bortolotti, Boscari, and Danese 2015; Chavez et al. 2020; Cua, McKone, and Schroeder 2001; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Mohaghegh, Blasi, and Größler 2021; Piercy and Rich 2015; Raji et al. 2021; Shah and Ward 2003; Virmani, Saha, and Sahai 2018
Human resource management (HRM)	Practices related to people management and human relations Flat organizational structure Continuous training and regular development Small problem-solving groups Employee participation and empowerment	Al Humdan et al. 2020; Bortolotti, Boscari, and Danese 2015; Christopher 2000; Cua, McKone, and Schroeder 2001; Dubey and Gunasekaran 2015; Gohr 2023; Gunasekaran 1998; Gunasekaran et al. 2019; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lotfi and Sodhi 2022; Mohaghegh, Blasi, and Größler 2021; Narasimhan, Swink, and Kim 2006; Piercy and Rich 2015; Power, Sohal, and Rahman 2001; Raji et al. 2021; Shah and Ward 2003; Sharifi and Zhang 1999; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yang, Hong, and Modi 2011; Yusuf et al. 2004, 2012
Customer focus (CF)	Forward coordination with customers Close contacts with customers Formal customer satisfaction programs/customer feedback in place Responsiveness to customer requirements Consideration of customer requirements in early product design	Al Humdan et al. 2020; Bortolotti, Boscari, and Danese 2015; Chavez et al. 2015; Christopher 2000; Cua, McKone, and Schroeder 2001; Dubey and Gunasekaran 2015; Gohr 2023; Green et al. 2019; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Kisperska-Moron and Haan 2011; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Mohaghegh, Blasi, and Größler 2021; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Piercy and Rich 2015; Raji et al. 2021; Sharifi and Zhang 1999; Tortorella, Miorando, and Marodin 2017; Tse et al. 2016; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012
Supplier partnership (SP)	Backward coordination with suppliers Long-term contacts with suppliers High degree of mutual trust with suppliers Partnership and cooperative relationships with suppliers Fair return to suppliers Supplier inputs for product development	Al Humdan et al. 2020; Bortolotti, Boscari, and Danese 2015; Chavez et al. 2015; Christopher 2000; Cua, McKone, and Schroeder 2001; Dubey and Gunasekaran 2015; Gohr 2023; Green et al. 2019; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Kisperska-Moron and Haan 2011; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Mohaghegh, Blasi, and Größler 2021; Naranjo, Menor, and Johnson 2023; Narasimhan, Swink, and Kim 2006; Piercy and Rich 2015; Raji et al. 2021; Tortorella, Miorando, and Marodin 2017; Tse et al. 2016; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012
Strategic planning and visioning (SPV)	Formal strategic planning with the particular focus on written mission, long-term goals, and clear implementation strategies Formal planning Development of long-range strategic plans Well-defined manufacturing strategies in line with the corporate strategy Continuous monitoring and adjustment of strategies in response to changing environments	Al Humdan et al. 2020; Cua, McKone, and Schroeder 2001; Gunasekaran 1999; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Piercy and Rich 2015; Sharifi and Zhang 1999; Tortorella, Miorando, and Marodin 2017; Virmani, Saha, and Sahai 2018
Kaizen (continuous improvement) culture (KC)	A culture of sustained improvement as a shared responsibility throughout the organization Continual improvement (rather than a static approach) of all aspects of the organization, e.g. products, processes, and people Constant learning and mistake proofing Improvement as a moving target (a never-ending cycle) Organization as a dynamic entity	Al Humdan et al. 2020; Bortolotti, Boscari, and Danese 2015; Cua, McKone, and Schroeder 2001; Dubey and Gunasekaran 2015; Gohr 2023; Gunasekaran 1999; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Mohaghegh, Blasi, and Größler 2021; Naranjo, Menor, and Johnson 2023; Narasimhan, Swink, and Kim 2006; Sharifi and Zhang 1999; Tortorella, Miorando, and Marodin 2017; Virmani, Saha, and Sahai 2018
Managerial attention and commitment (MAC)	Top management commitment and support to create an environment where supply chain practices can successfully payoff. This involves promoting: Improvement initiatives and quality management tools Training Kaizen (continuous improvement), learning culture, and change management Resource allocation for supply chain strategies Knowledge transfer Staff empowerment	Al Humdan et al. 2020; Bortolotti, Boscari, and Danese 2015; Cua, McKone, and Schroeder 2001; Gohr 2023; Gunasekaran 1998, 1999; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lin, Chiu, and Chu 2006; Mohaghegh, Åhlström, and Blasi 2023; Naranjo, Menor, and Johnson 2023; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004
Supply chain integration (SCI)	To collaboratively remove all boundaries to ease the flow of material, resources, and information between different actors involved in the supply chain Rapid partnership with customers, suppliers, and other supply chain actors Virtual enterprise Teams across company boarders Joint planning and collaborative problem-solving teams	Al Humdan et al. 2020; Christopher 2000; Gohr 2023; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Kisperska-Moron and Haan 2011; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Narasimhan, Swink, and Kim 2006; Naylor, Naim, and Berry 1999; Raji et al. 2021; Sharifi and Zhang 1999; Tortorella, Miorando, and Marodin 2017; Tse et al. 2016; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012
Product/process development (PD)	Fast and concurrent design and development of products and processes based on customer requirements Quick new product development Adaptation of products sufficiently fast to new customer requirements Concurrent engineering Flexible production planning/processes in accordance with varying customer requirements	Aslam et al. 2018; Blome, Schoenherr, and Rexhausen 2013; Christopher 2000; Dubey and Gunasekaran 2015; Gohr 2023; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Meng et al. 2023; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Raji et al. 2021; Sharifi and Zhang 1999; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018
Market sensitivity (MS)	A firm’s capability to sense, perceive, and anticipate market changes which could eventually lead to quick responses to unexpected customer requirements Learning about customer requirements Identifying market trends and customer segments Immediate reactions to market changes Customer-driver innovation	Agarwal, Shankar, and Tiwari 2006; Al Humdan et al. 2020; Aslam et al. 2018; Blome, Schoenherr, and Rexhausen 2013; Christopher 2000; Dubey and Gunasekaran 2015; Eckstein et al. 2015; Geyi et al. 2020; Gohr 2023; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Kisperska-Moron and Haan 2011; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Raji et al. 2021; Sharifi and Zhang 1999; Tortorella, Miorando, and Marodin 2017; Tse et al. 2016; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012
Supply chain reconfiguration (SCR)	A firm’s capability to strategically reallocate and adapt its supply chain resources to create a new configuration that aligns with the demands of a changing environment Rapid and effective responses to market changes through accommodating changes in supply chain activities Rapid and effective response to competitor’s actions through accommodating changes in supply chain activities Efficient and effective communication with cooperative organization	Agarwal, Shankar, and Tiwari 2006; Al Humdan et al. 2020; Blome, Schoenherr, and Rexhausen 2013; Eckstein et al. 2015; Gohr 2023; Gunasekaran 1998, 1999; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Lotfi and Sodhi 2022; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Naylor, Naim, and Berry 1999; Oliveira-Dias, Maqueira Marín, and Moyano-Fuentes 2022; Oliveira-Dias et al. 2023; Raji et al. 2021; Sharifi and Zhang 1999; Tse et al. 2016; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012
Technology utilization (TU)	Adopting a wide range of high-impact technologies (including information and communication technologies) to respond to market changes and uncertain customer requirements Appropriate technology and technological capabilities for quick and effective responses to changes Real time information and communication technologies to facilitate communication and best-cooperation for value creation	Al Humdan et al. 2020; Christopher 2000; Dubey and Gunasekaran 2015; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Kisperska-Moron and Haan 2011; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Mohaghegh, Åhlström, and Blasi 2023; Naranjo, Menor, and Johnson 2023; Narasimhan, Swink, and Kim 2006; Power, Sohal, and Rahman 2001; Raji et al. 2021; Sharifi and Zhang 1999; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004
Supply chain learning and information sharing (SCL)	Using information and market knowledge to exploit profitable opportunities in a volatile marketplace Availability of information to everyone within the firm and across the supply chain Decision-making based on market knowledge Available knowledge and skills to manage changes Knowledge management database Formal mechanisms to disseminate knowledge among supply chain partners	Agarwal, Shankar, and Tiwari 2006; Al Humdan et al. 2020; Christopher 2000; Dubey and Gunasekaran 2015; Gohr 2023; Gunasekaran 1998, 1999; Gunasekaran et al. 2019; Inman et al. 2011; Iqbal, Huq, and Bhutta 2018; Iqbal et al. 2020; Lin, Chiu, and Chu 2006; Lotfi and Sodhi 2022; Mohaghegh, Åhlström, and Blasi 2023; Narasimhan, Swink, and Kim 2006; Naylor, Naim, and Berry 1999; Vázquez-Bustelo, Avella, and Fernández 2007; Virmani, Saha, and Sahai 2018; Yusuf et al. 2004, 2012

In addition, the distinct characteristics and objectives of LSC and ASC have led researchers to focus on the concept of the ‘decoupling point’, which separates the lean and agile portions of the supply chain (Bhamra et al. 2021; Mason-Jones, Naylor, and Towill 2000; Naylor, Naim, and Berry 1999). This point is strategically important as it marks a shift in attention from waste elimination (through leanness) to enhancing flexibility and responsiveness (through agility). Naylor, Naim, and Berry (1999) propose where to position the decoupling point based on the supply chain structure. The authors explain that for make-to-order (MTO) structures, the attention should be put on customer responsiveness (rather than waste elimination) with more agile practices in place. On the contrary, in the make-to-stock (MTS) structure, waste elimination is more important than customer responsiveness and hence the decoupling point moves towards the customers (rather than the manufacturer), resulting in having more lean practices than agile.

3. Research method

3.1. Total interpretive structural modelling

To address our research question, i.e. whether and how LSC and ASC coexist for advanced sustainable business performance, total interpretive structural modelling (TISM) was employed. TISM represents an evolution of conventional interpretive structural modelling (ISM). ISM is based on graph theory which converts poorly articulated intellectual models with imprecise relationships into detailed models with clear connections among variables (e.g. Kaswan and Rathi 2019; Sage 1977; Sorooshian, Tavana, and Ribeiro-Navarrete 2023). However, ISM is criticized due to its weaknesses, such as the limited interpretation of possible links among variables (Rajesh 2017; Sorooshian, Tavana, and Ribeiro-Navarrete 2023) and the neglect of transitive links (Vimal et al. 2023). In response to these ISM weaknesses, TISM has emerged which shows the interconnectedness of variables, both through direct and indirect (through transitive) links, and emphasizes the underlying logic behind connections.

TISM seemed to be an appropriate research method in our study primarily for two reasons. First, adopting the leagile supply chain strategy can be complex because it requires implementing a combination of unique and common practices of LSC and ASC with unclear relationships. This complexity can be possibly demonstrated by inconclusive findings in the literature on whether LSC and ASC are mutually connected and how if at all, their relevant practices can be associated with organizational excellence (Inman et al. 2011; Iqbal et al. 2020). In addition, the absence of consensus among researchers regarding whether LSC should be treated as a prerequisite for ASC or vice versa (Inman et al. 2011; Oliveira-Dias et al. 2023) contributes to this complexity further. TISM therefore was deemed suitable since it is descriptive and could sufficiently explain the complex pattern of relationships among LSC and ASC practices (if any) and represent them in a hierarchical configuration (Talib, Rahman, and Qureshi 2011). Second, the objective of this study is to propose a new theoretical model (rather than testing the hypotheses). Therefore, TISM seemed to function well since it is prescribed when the aim is to show (and not test) the specific relationships and portray the overall structure in a graphical way (Kaswan and Rathi 2019; Pfohl, Gallus, and Thomas 2011; Toktaş-Palut et al. 2014). In addition, both ISM and TISM are widely applied in the operations and supply chain management literature to specify the interactions between different enabling practices (or implementation barriers and challenges) of the studied phenomenon. The contextual relationships between challenges of Industry 4.0 technologies for lean Six Sigma (Vimal et al. 2023), barriers to the adoption of blockchain (Mathivathanan et al. 2021), barriers and enablers of e-procurement systems on its adoption decision (Toktaş-Palut et al. 2014), barriers to implement green supply chains (Luthra et al. 2011), and enablers of green lean six sigma implementation (Kaswan and Rathi 2019) are just a few examples, where ISM or TISM are employed.

3.2. Research approach

(T)ISM is built upon two main pillars of (a) literature review and (b) experts’ opinions. First, based on a synthesis of the literature, leagile building blocks were listed (Table 1). It should be noted that we included those articles that explicitly focus on lean and/or agile and/or leagile strategies in operations and supply chain management literature. Among the 15 identified practices, some were unique to LSC (e.g. JIT and TQM), and some practices were of particular importance mainly for ASC (e.g. product/process development and supply chain reconfiguration). Common practices between the two strategies, such as kaizen culture and HRM were also determined.

Then, experts’ opinions were relied on to identify the contextual relationships between the identified building blocks and the three measures of sustainable business performance, namely, economic, environmental, and social performance, making a total of 18 variables.

The flow diagram representing our research approach is depicted in Figure 1. In addition, following Mathivathanan et al. (2021) and Zhao et al. (2024), the detailed step-by-step procedure for the TISM methodology is described as follows.

Figure 1. Flow diagram of TISM-MICMAC methodology.

• Step 1. A list of relevant practices for the leagile supply chain strategy was derived from reviewing the existing literature, as shown previously in Table 1.

• Step 2. Based on experts’ opinions, contextual relationships between the identified blocks and the triple dimensions of sustainable business performance were established. This step involved the author’s interpretation of whether and how identified variables were connected.

• Step 3. A structural self-interaction matrix (SSIM) was developed to show the pairwise relationships among the variables.

• Step 4. The SSIM was then converted into the so-called ‘initial reachability matrix’ as a binary matrix with 1 for the existence of the relationship between two variables and 0 where there was no relationship.

• Step 5. The initial reachability matrix was then translated into the ‘final reachability matrix’, where transitivity among variables was also checked.

• Step 6. The final reachability matrix was portioned into different hierarchical levels. This step is called ‘level partitioning’.

• Step 7. The initial hierarchical model was developed which also involved significant transitive links, following experts’ opinions.

• Step 8. A binary interaction matrix (1 for the existence of significant transitive links and 0 otherwise), and an interpretive matrix, highlighting the logic behind the links were developed.

• Step 9. To ensure the reliability, validity, and generalizability of our findings, the proposed model was subjected to scrutiny from two distinct sources. First, we checked our findings with two independent researchers who specialize in the field, evidenced by their research projects and publications on circular economy and sustainable supply chains. Second, we corroborated our results using extant studies, the process called theoretical triangulation or literature enfolding (Farquhar, Michels, and Robson 2020; Mohaghegh and Größler 2021).

• Step 10. The final TISM model with the explanation of the most important direct and indirect relationships was depicted.

3.3. Data collection

To collect experts’ opinions (Step 2), initially, eleven requests were submitted to industry experts to see whether they were willing to participate in our study. Following Agarwal, Shankar, and Tiwari (2006) prescription for an ideal setting to adopt the leagile supply chain strategy, we focused on those industries that are subject to volatile environments with unpredictable market demands. As an example, we focused on the fashion (clothing) industry which is characterized by short product short life cycle as well as its unpredictable trends due to rapidly changing customer preferences. Also, rapid technological advances in the electronics industry result in high environmental dynamism, making it an ideal setting to adopt the leagile strategy as well. The characteristics of the industries targeted are summarized in Table 3.

Table 3. Industry characteristics.

Distinguishing attributes	Industry
	Clothing (fashion)	Chemical	Agricultural	Electronics
Environment	Volatile and unpredictable due to rapidly changing customer preferences/fashion trends	Volatile and unpredictable due to regulatory changes as well as emerging environmental sustainability concerns	Highly dynamic due to uncertain customer requirements/intense competition	Volatile and unpredictable due to rapid technological advances/need for continuous innovation for consumer electronics devices
Product life cycle	Short	Medium to long	Short	Short
Product variety	Medium	Medium to high	Medium compared to other competitors	Medium to high
Profit margin	Moderate	High	Moderate	Moderate

To proceed, a brief description of the research project and its intended objectives were submitted using a short cover letter. The experts were targeted from the operations and supply chain management teams of manufacturing firms. We ensured that our target population consisted of managers who were directly or partially engaged in operations/supply chain strategies. In addition, to make sure that our experts had a clear understanding of both LSC and ASC, we used years of experience in their respective positions as a measure. We require a minimum of three years of experience in their positions to guarantee their proficiency. Eventually, four practitioners agreed to participate in our study and hence formed our expert panel. The profile of the experts is provided in Table 4.

Table 4. The profile of practitioners.

Industry experts	Industry	Job position	Experience (year)	Firm size (no. of employees)
Professional 1	Clothing (fashion)	Quality manager	9	50
Professional 2	Chemical	Supply chain manager	12	3100
Professional 3	Agricultural	Plant manager	5	200
Professional 4	Electronics	Supply chain manager	4	160

While we acknowledge that our sample size may pose challenges for generalizing the findings, it is worth noting that our sample size is comparable with those of other studies using (T)ISM, such as Azevedo, Carvalho, and Cruz-Machado (2013), Govindan et al. (2015), Mathivathanan et al. (2021), Rajesh (2017), and Tan et al. (2019), to name a few. In addition, managing a large number of participants in (T)ISM demands a significant time investment and can also result in difficult-to-manage inconsistencies among the responses. Mathivathanan et al. (2021) note that discussing the interrelationships between various variables is time-consuming and hence a manageable number of respondents should be considered. Given that we considered 18 variables (15 variables for leagile building blocks and three variables for sustainable business performance measures), we had a total of 18*17 = 306 possible relationships to analyze. Therefore, having more respondents made it more challenging in terms of time and reaching a consensus.

Unlike questionnaires which often result in a high likelihood of unanswered questions and respondents failing to fully understand the questions (Zhao et al. 2024), an interview as a data collection method was employed. We followed the logic of semi-structured interviews. Rather than asking a set of direct and fixed questions, interviewees were requested to provide inputs on how each two variables could be potentially connected. This type of interview also aligns well with the essence of TISM which seeks to understand how one variable leads to another (Sorooshian, Tavana, and Ribeiro-Navarrete 2023). Also, semi-structured interviews with open-ended questions increased the degree of freedom and flexibility of participants, allowing them to inform researchers about different facets of the phenomenon under study (Mohaghegh and Größler 2021). To mitigate the risk of interviewees deviating from the research focus, as a general challenge of open-ended questions in semi-structured interviews, the talk was moderated by one of the researchers. This strategy is shown to ensure the effectiveness of data collection in the (T)ISM method (Azevedo, Carvalho, and Cruz-Machado 2013; Pfohl, Gallus, and Thomas 2011).

To proceed, practitioners were provided with a list of variables (identified before from the literature review or Step 1) and they were asked to discuss the potential relationships between each pair of variables. To ensure consistency in understanding, all 18 variables were explained thoroughly by one of the researchers. In our case, for example, the interviewees were consulted to focus on the relationship between TQM and environmental sustainability performance (if any), and to discuss the how mechanism, using the list of leagile supply chain practices. This strategy not only demonstrated the relationships between identified variables but offered insights into the logic behind how such variables can be connected (through what variables). The answers then were interpreted by the authors to ascertain the sequence of practices and the links between variables. This interpretation eventually revealed the contextual relationships between the leagile supply chain practices and sustainable business performance measures.

4. Research analysis: model development

4.1. Structural self-interaction matrix (SSIM)

To determine the contextual relationships between the identified leagile building blocks and sustainable business performance measures (Step 3) and denote the directions of the relationships, four symbols were used as follows:

• V: Element i enables/leads to/impacts on element j.

• A: Element j enables/leads to/impacts on element i.

• X: Elements i and j and are mutually interdependent.

• O: No relationship between elements i and j.

Following these rules, the full SSIM with 18 variables is provided in Table 5. In our case, as an example, strategic planning and visioning (SPV) was found to enable just-in-time (JIT) implementation. Therefore, symbol ‘V’ was used for the (SPV, JIT) entry. Customer focus (CF) and total quality management (TQM) appeared to mutually reinforce each other, leading to the use of symbol ‘X’ for the (CF, TQM) entry. Conversely, symbol ‘O’ was used to indicate the absence of any connection between total productive maintenance (TPM) and supply chain integration (SCI) in the SSIM.

Table 5. Structural self-interaction matrix (SSIM).

No.	Leagile practices	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
1	JIT		X	X	A	X	X	A	A	A	V	V	V	V	O	V	V	V	O
2	TQM	–		X	A	X	X	A	A	A	V	V	V	V	O	V	V	V	V
3	TPM	–	–		A	O	O	A	A	A	O	O	O	O	O	O	V	V	O
4	HRM	–	–	–		V	V	A	X	A	V	V	V	V	O	V	V	V	V
5	CF	–	–	–	–		O	A	A	A	V	V	V	V	V	V	V	V	O
6	SP	–	–	–	–	–		A	A	A	V	V	V	V	V	V	V	V	O
7	SPV	–	–	–	–	–	–		V	V	V	V	V	V	V	V	V	V	V
8	KC	–	–	–	–	–	–	–		A	V	V	V	V	V	V	V	V	V
9	MAC	–	–	–	–	–	–	–	–		V	V	V	V	V	V	V	V	V
10	SCI	–	–	–	–	–	–	–	–	–		V	V	V	A	X	V	V	V
11	PD	–	–	–	–	–	–	–	–	–	–		A	X	A	A	V	V	V
12	MS	–	–	–	–	–	–	–	–	–	–	–		V	A	A	V	V	V
13	SCR	–	–	–	–	–	–	–	–	–	–	–	–		A	A	V	V	V
14	TU	–	–	–	–	–	–	–	–	–	–	–	–	–		V	V	V	V
15	SCL	–	–	–	–	–	–	–	–	–	–	–	–	–	–		V	V	V
16	Econ.	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–		A	A
17	Env.	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–		O
18	Social	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–	–

4.2. Initial reachability matrix

In Step 4, the SSIM constructed was then translated into a binary matrix, following the rules that are summarized below:

• The (i, j) entry in the initial reachability matrix is 1 and the (j, i) entry is 0 if symbol ‘V’ is employed in the SSIM for the (i, j) entry.

• The (i, j) entry in the initial reachability matrix is 0 and the (j, i) entry is 1 if symbol ‘A’ is employed in the SSIM for the (i, j) entry.

• The (i, j) entry in the initial reachability matrix is 1 and the (j, i) entry is also 1 if symbol ‘X’ is employed in the SSIM for the (i, j) entry.

• The (i, j) entry in the initial reachability matrix is 0 and the (j, i) entry is also 0 if symbol ‘O’ is employed in the SSIM for the (i, j) entry.

In our case, for instance, symbol ‘V’ was allocated in the SSIM for the (JIT, market sensitivity) entry. As a result, the (JIT, market sensitivity) entry was set to 1 and the (market sensitivity, JIT) entry became 0 in the initial reachability matrix. This process was followed for all entries to generate the initial reachability matrix (Table 6).

Table 6. Reachability matrix.

No.	SC practices	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	Driving power
1	JIT	1	1	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	0	13
2	TQM	1	1	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1	14
3	TPM	1	1	1	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	6
4	HRM	1	1	1	1	1	1	0	1	0	1	1	1	1	0	1	1	1	1	15
5	CF	1	1	0	0	1	0	0	0	0	1	1	1	1	1	1	1	1	0	11
6	SP	1	1	0	0	0	1	0	0	0	1	1	1	1	1	1	1	1	0	11
7	SPV	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	18
8	KC	1	1	1	1	1	1	0	1	0	1	1	1	1	1	1	1	1	1	16
9	MAC	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	17
10	SCI	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	8
11	PD	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	5
12	MS	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	1	6
13	SCR	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	5
14	TU	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	9
15	SCL	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	8
16	Econ.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1
17	Env.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	2
18	Social	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1
Dependence power		9	9	7	4	7	7	1	4	2	11	14	12	14	9	11	18	16	12	–

4.3. Final reachability matrix

The final reachability matrix was obtained in Step 5 after incorporating transitivity into the initial reachability matrix. The basic principle is that if variable ‘A’ is associated with variable ‘B’, and ‘B’ is related to ‘C’, then variable ‘A’ and variable ‘C’ should be connected as well. Applying transitivity allowed for the inclusion of all indirect relationships between variables, resulting in a more complete understanding of the interconnections between the leagile building blocks and sustainable business performance measures. In our case, for instance, TPM and JIT were connected, and JIT and CF were also linked. As a result, we could infer that an indirect relationship (through a transitive link) existed between TPM and CF, and the corresponding value was set to 1. Table 7 illustrates the final reachability matrix, where 1* signifies transitivity.

Table 7. Final reachability matrix.

No.	SC practices	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	Driving power
1	JIT	1	1	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1*	14
2	TQM	1	1	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1	14
3	TPM	1	1	1	0	1*	1*	0	0	0	1*	1*	1*	1*	1	1*	1	1	1*	14
4	HRM	1	1	1	1	1	1	0	1	0	1	1	1	1	1*	1	1	1	1	16
5	CF	1	1	1*	0	1	1*	0	0	0	1	1	1	1	1	1	1	1	1*	14
6	SP	1	1	1*	0	1*	1	0	0	0	1	1	1	1	1	1	1	1	1*	14
7	SPV	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	18
8	KC	1	1	1	1	1	1	0	1	0	1	1	1	1	1	1	1	1	1	16
9	MAC	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	17
10	SCI	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	8
11	PD	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	5
12	MS	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	1	6
13	SCR	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	5
14	TU	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	9
15	SCL	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	1	8
16	Econ.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1
17	Env.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	2
18	Social	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	2
Dependence power		9	9	9	4	9	9	1	4	2	12	15	13	15	10	12	18	16	16	–

^1* : transitive links.

4.4. Level partitioning

In Step 6, all the variables were partitioned into different hierarchical levels based on reachability, antecedent, and intersection sets. The reachability set for each variable was primarily comprised of the variable itself and other variables that could be attained with its help. Indeed, the reachability set of a variable positioned in a given row included those variables that contained 1 in the corresponding row. The antecedent set included the variable itself and the other variables which may help in achieving it. The antecedent set of the variable positioned in a given column included those variables that contained 1 in the corresponding column. The intersection set for each variable was determined as the intersection between the corresponding reachability set and the antecedent set. Those variables for which the reachability and intersection sets were identical constituted the top level in the TISM hierarchy. Once a variable was assigned to a level, it was excluded from the list. This process was repeated for the remaining variables until all the variables were assigned to a level. Table 8 shows the level partitioning of the variables in various iterations.

Table 8. Level partitioning.

No.	Reachability set	Antecedent set	Intersection	Level
1	1,2,3,5,6,10,11,12,13,14,15,16,17,18	1, 2, 3, 4, 5, 6, 7, 8, 9	1, 2, 3, 5, 6	7
2	1,2,3,5,6,10,11,12,13,14,15,16,17,18	1, 2, 3, 4, 5, 6, 7, 8, 9	1, 2, 3, 5, 6	7
3	1,2,3,5,6,10,11,12,13,14,15,16,17,18	1, 2, 3, 4, 5, 6, 7, 8, 9	1, 2, 3, 5, 6	7
4	1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18	4, 7, 8, 9	4, 8	8
5	1, 2, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9	1, 2, 3, 5, 6	7
6	1, 2, 3, 5, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9	1, 2, 3, 5, 6	7
7	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18	7	7	10
8	1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18	4, 7, 8, 9	4, 8	8
9	1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18	7, 9	9	9
10	10, 11, 12, 13, 15, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15	10, 15	5
11	11, 13, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15	11, 13	3
12	11, 12, 13, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15	12	4
13	11, 13, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15	11, 13	3
14	10, 11, 12, 13, 14, 15, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 14	14	6
15	10, 11, 12, 13, 15, 16, 17, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15	10, 15	5
16	16	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18	16	1
17	16, 17	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17	17	2
18	16, 18	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18	18	2

After assigning the variables to their hierarchical levels, the initial hierarchical diagram was created (Step 7). This diagram incorporated the most significant transitive links, achieved through consulting with our expert panel. Out of 16 transitive connections, only 8 appeared to be significant.

Subsequently, in Step 8, we first presented the interaction matrix with only the most significant transitive links, as shown by 1* in Table 9. Then, we also provided the interpretive matrix (Table 10) to show the logic behind each significant transitive link. As an example, the relationship between JIT and social performance appeared to be significant. The reason is that JIT as an LSC practice results in waste (overproduction and transportation) reduction. Minimizing waste not only results in reduced rework but also enhances employee satisfaction, increasing workplace productivity and thereby contributing to enhanced social sustainability performance.

Table 9. Interaction matrix.

No.	SC practices	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
1	JIT	–	1	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1*
2	TQM	1	–	1	0	1	1	0	0	0	1	1	1	1	1	1	1	1	1
3	TPM	1	1	–	0	0	0	0	0	0	0	1*	0	0	1	0	1	1	1*
4	HRM	1	1	1	–	1	1	0	1	0	1	1	1	1	1*	1	1	1	1
5	CF	1	1	0	0	–	1*	0	0	0	1	1	1	1	1	1	1	1	1*
6	SP	1	1	0	0	1*	–	0	0	0	1	1	1	1	1	1	1	1	1*
7	SPV	1	1	1	1	1	1	–	1	1	1	1	1	1	1	1	1	1	1
8	KC	1	1	1	1	1	1	0	–	0	1	1	1	1	1	1	1	1	1
9	MAC	1	1	1	1	1	1	0	1	–	1	1	1	1	1	1	1	1	1
10	SCI	0	0	0	0	0	0	0	0	0	–	1	1	1	0	1	1	1	1
11	PD	0	0	0	0	0	0	0	0	0	0	–	0	1	0	0	1	1	1
12	MS	0	0	0	0	0	0	0	0	0	0	1	–	1	0	0	1	1	1
13	SCR	0	0	0	0	0	0	0	0	0	0	1	0	–	0	0	1	1	1
14	TU	0	0	0	0	0	0	0	0	0	1	1	1	1	–	1	1	1	1
15	SCL	0	0	0	0	0	0	0	0	0	1	1	1	1	0	–	1	1	1
16	Econ.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	–	0	0
17	Env.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	–	0
18	Social	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	–

^1* : significant transitive links.

Table 10. Interpretive matrix (only with significant transitive links).

SC practices	CF	SP	PD	TU	Social
JIT	–	–	–	–	T1: Less waste leads to less rework and hence employees feel satisfied being more productive.
TPM	–	–	T2: Improved equipment reliability, efficiency, and reduced downtime contribute to more efficient development.	–	T3: Implementing machinery maintenance programs enhances employee safety, thereby contributing to improved social performance.
HRM	–	–	–	T4: Employing effective practices, such as training or job rotation results in the development of skills and competences necessary to manage technologies.	–
CF	–	T5: A customer centred firm establishes supplier relationship to collaboratively meet customer requirements.	–	–	T6: Sustainability as a newly emphasised customer requirement leads to the development of social sustainability practices.
SP	T7: Supplier partnership results in shared customer focus, leading to enhanced product quality and timely delivery.	–	–	–	T8: Collaboration with suppliers promotes social sustainability standards.

T: transitivity links.

Step 9 safeguarded the reliability and validity of the proposed model based on two sources. First, two experienced independent researchers from the field of operations/supply chain management were asked to review our model and provide feedback. Second, we also employed the so-called ‘theoretical triangulation’ or ‘literature enfolding’ (Farquhar, Michels, and Robson 2020; Mohaghegh and Größler 2021) that is to ensure that our findings were supported by the extant studies in operations and supply chain management. Table 11 summarizes the logic behind some of the most important direct connections based on literature enfolding.

Table 11. Interpretation of direct relationships (literature enfolding).

Link code	Relationship	Interpretation	Key references
D1	Principal antecedents → Soft/hard lean practices	Principal antecedents (SPV, MA, KC, and HRM) are necessary to provide an environment where LSC/ASC practices can flourish.	Bortolotti, Boscari, and Danese 2015; Dubey and Gunasekaran 2015
D2	Soft/hard lean practices → TU	Reducing waste, which encompasses waste generated during production as well as waste related to customers and suppliers, requires collecting data from production processes and supply chain partners. This data collection necessitates the utilization of information and communication technology (ICT) and advanced data processing technologies, such as Big Data analytics.	Ding, Ferràs Hernández, and Agell Jané 2023
D3	TU → SCL (and SCI)	Immediate access to data and effective data management could lead to a more agile firm with a deeper understanding of market trends and customer requirements, including sustainability-related demands. Furthermore, the effective use of technologies could facilitate collaboration and knowledge sharing among supply chain actors.	Christopher 2000; Raji et al. 2021; Yusuf et al. 2004
D4	SCL → MS	Supply chain learning entails continuous monitoring of market trends, customer demands, and changes in business environments. Knowledge sharing among supply chain partners empowers firms to understand and effectively respond to evolving market dynamics, including the growing emphasis on sustainability preferences among their customers	Christopher 2000
D5	SCI → MS	Close and collaborative relationships among supply chain actors (customers and suppliers) enhance responsiveness. Integrated supply chains with real-time data sharing facilitate the effective identification of market trends (e.g. sustainability preferences of customers) and enable immediate responses to address customers’ sustainability requirements (e.g. sustainable product development)	Swafford, Ghosh, and Murthy 2006
D6	MS → SCR (and PD)	Market-sensitive firms are well-positioned to understand market trends and respond to them successfully. To this end, agile firms are required to adjust their supply chain (supply chain reconfiguration) as well as their operations and products (product/process development) quickly and effectively.	Mohaghegh, Åhlström, and Blasi 2023
D7	SCR → Social	Easily reconfigured supply chains offer a flexible environment to implement social sustainability practices throughout the supply chain, including supplier evaluation measures aimed at ensuring compliance with social sustainability standards.	Geyi et al. 2020; Mohaghegh, Åhlström, and Blasi 2023
D8	PD → Env.	Developing products and processes while considering environmental factors leads to improvements in environmental sustainability performance.	Geyi et al. 2020
D9	Social → Econ.	Adopting social sustainability practices (e.g. training and employee engagement) fosters a more skilled workforce, elevating productivity and ultimately driving higher profitability.	Nath and Agrawal 2020
D10	Env → Econ	Implementing environmental sustainability practices (e.g. green supply chain/production practices) leads to resource efficiency and reduced waste, thereby resulting in cost savings.	Nath and Agrawal 2020

D: direct connections.

Finally, the resulting TISM model (Figure 2) depicts how the leagile supply chain practices interact to contribute to sustainable business performance.

Figure 2. Final TISM diagram.

4.5. MICMAC analysis

Cross-impact matrix multiplication (MICMAC)¹ analysis complemented the (T)ISM methodology² (e.g. Mathivathanan et al. 2021; Vimal et al. 2023; Zhao et al. 2024) and validated the results (Zhao et al. 2024). Based on dependence power and driving power obtained from the final reachability matrix, MICMAC analysis entailed developing a diagram, where all practices were classified into four clusters (Sorooshian, Tavana, and Ribeiro-Navarrete 2023), as follows:

• Cluster 1 (Autonomous variables): The first cluster consists of practices with low dependence power and low driving power.

• Cluster II (Dependent variables): The second cluster consists of practices with high dependence power and low driving power.

• Cluster III (Linkage enablers): The third cluster consists of practices with high dependence power and high driving power.

• Cluster IV (Independent enablers): The fourth cluster consists of practices with low dependence power and high driving power.

Following the rules of the MICMAC analysis, all variables were grouped into 4 clusters, as depicted in Figure 3.

Figure 3. MICMAC analysis.

Based on the findings, SPV, MAC, KC, and HRM, promoted by both LSC and ASC, are independent variables (Cluster IV). Considering the highest driving power and the lowest dependence power, we consider these practices as principal antecedents of other leagile practices. As expected, sustainable business performance measures are dependent variables with the highest (and lowest) dependence (driving) power and hence are positioned in Cluster II. LSC practices (JIT, TQM, TPM, CF, SP) located in the linkage cluster, are considered essential to establish connections between principal antecedents and ASC practice (and sustainable business performance measures). In addition, having no variable within the autonomous cluster is evidenced to conclude that LSC and ASC are mutually supportive strategies, challenging the traditional viewpoint of serving the two as exclusive strategies.

5. Discussion

Formulation of an effective supply chain strategy is crucial to achieve competitive advantage and enhance the performance of a firm, as indicated by research (e.g. Fadaki, Rahman, and Chan 2020; Frohlich and Westbrook 2001). Nevertheless, supply chain strategies, such as lean supply chain (LSC) and agile supply chain (ASC) should incorporate environmental and social considerations alongside their traditional focus on profitability, in response to the sustainability and ethical demands raised by stakeholders, such as customers.

Although extant studies show that business performance can improve through ‘leanness’ (e.g. Godinho Filho, Ganga, and Gunasekaran 2016; Shah and Ward 2003) and ‘agility’ separately (e.g. Inman et al. 2011; Mohaghegh, Åhlström, and Blasi 2023; Tarafdar and Qrunfleh 2017; Yusuf et al. 2012) or jointly through ‘leagility’ (e.g. Fadaki, Rahman, and Chan 2020; Rahimi and Alemtabriz 2022), little is known in the literature on whether, or not, and how the hybrid leagile supply chain strategy contributes to sustainable business performance, i.e. TBL, giving equal importance to economic, environmental, and social performance. The reason can relate to either the dominance of studies that focus on lean and/or agility in isolation and investigate their individual effects on sustainable performance (Geyi et al. 2020; Meng et al. 2023; Mohaghegh, Åhlström, and Blasi 2023; Nath and Agrawal 2020) or the tendency of researchers in operations and supply chain management to link the leagile strategy to only operational (Fadaki, Rahman, and Chan 2020) or supply chain performance (Rahimi and Alemtabriz 2022).

Using total interpretive structural modelling (TISM) together with the MICMAC analysis, we propose an integrated leagile supply chain model in 10 levels, aiming to analyze how unique and common practices of LSC and ASC strategies interact for sustainable business performance. To effectively apply the TISM method, we first relied on a synthesis of the literature to identify pertinent practices of the leagile supply chain strategy, referred to as the leagile building blocks in our study. Then, four experts were consulted to comprehend how identified blocks are connected to attain economic, environmental, and social gains. Finally, the results were corroborated through feedback provided by two independent researchers and literature enfolding.

Our analysis indicates that common practices among LSC and ASC appear to have the highest driving power and hence they rest at the bottom positions of the proposed TISM model. These practices, namely, strategic planning and visioning (SPV), managerial attention and commitment (MAC), human resource management (HRM), and Kaizen culture (KC) can be viewed as the principal antecedents for the successful implementation of the leagile supply chain strategy. We add to a huge body of literature that leadership (Achanga et al. 2006) and strategic planning (Iqbal, Huq, and Bhutta 2018), top management commitment (Iqbal et al. 2020), organizational culture (Dubey and Gunasekaran 2015), HR practices and policies (Geyi et al. 2020), and Kaizen (continuous improvement) culture (Mohaghegh, Blasi, and Größler 2021; Naranjo, Menor, and Johnson 2023) are crucial in developing effective supply chain strategies, including the leagile supply chain strategy. Based on this finding, we further claim that insufficient attention to such antecedents can potentially curb leagility and hence the hybrid leagile strategy most likely fails to result in sustainable outcomes.

In a higher hierarchical level, mainly unique practices of LSC are positioned. These practices include technical (or hard) practices, i.e. just-in-time (JIT), total quality management (TQM), and total productive maintenance (TPM) with the goal of improving cost efficiency through eliminating waste. Such practices are crucial for sustainable business performance. The reason is that hard lean practices focus on waste elimination, mainly at the shopfloor. Waste in the form of inventory or work-in-progress reduction is associated with higher profitability through decreased cost of material handling, thereby contributing to the economic dimension of sustainability. Additionally, the implementation of JIT with the focus on lot sizes and inventory reduction promotes resource and process efficiency, resulting in improvements in environmental sustainability performance. Also, reducing waste in the form of rework or overproduction fosters greater productivity among employees, thus making a positive contribution to social sustainability performance.

We also consider collaboration with external factors involved in the upstream and downstream supply chain equally important and hence position them on the same hierarchical level. On the one hand, customer focus and supplier partnership complement hard practices, maximizing the benefits of technical practices in terms of waste reduction. Given that waste is what customers are not willing to pay (Chen, Li, and Shady 2010), focusing on customer wants/needs (or value-added activities) helps firms better understand what activities are beneficial to customers, i.e. value-added activities, and what activities should be removed, i.e. waste (or non-value-added activities) with the help of technical practices. Our results are consistent with the conclusion of previous studies, such as Bortolotti, Boscari, and Danese (2015) and Mohaghegh, Blasi, and Größler (2021) that soft lean practices, such as effective relationships with customers and suppliers are as important as hard practices for a firm to be lean. On the other hand, effective supply chain partnership per se can also enhance sustainability. To this end, customer-centred firms tend to have a deeper understanding of customer requirements, including the sustainability demands. Collaboration with suppliers also matters in the context of sustainability. Working with suppliers can assure their commitment to environmental and social sustainability considerations, leading to the sustainability assessment of suppliers and the selection of those suppliers that comply with sustainability standards. This is in line with Wu’s (2013) empirical findings that both collaborating with suppliers and cooperating with customers have a positive effect on sustainability practices (green product innovation and green process innovation).

LSC practices then form the foundation for the implementation of the ASC strategy. More specifically, lean technical practices, when coupled with customer focus and supplier partnership, generate data. For instance, this data may potentially contain information about customers’ sustainability requirements or whether, or not, suppliers adhere to sustainability standards. However, to maximize the value of the data, efficient data processing is essential. Information and communication technologies (ICT) for example can be leveraged to facilitate communication and cooperation between supply chain partners, i.e. supply chain integration (SCI), and to advance supply chain inter-firm knowledge articulation, i.e. supply chain learning and information sharing (SCL). Our highlight on the effectiveness of ICT for the ASC strategy aligns with the findings of a study conducted by Raji et al. (2021), claiming that Big Data (analytics) influences agile practices, such as supply chain integration in the form of involving suppliers in the new product develop projects.

SCI and SCL could patently reinforce market sensitivity (MS) practices. This is due to the fact that a virtual enterprise with all supply chain partners integrated into a value-creating chain with perfectly shared market knowledge is more inclined to actively learn about market trends, customers, and competitors, as discussed by Tse et al. (2016), for example. Therefore, firms with a strong market sensitivity orientation are more likely to understand and respond to customer requirements, including the sustainability demands and expectations. More specifically, firms that are well-equipped with the MS practices tend to adopt environmental and social sustainability practices into their existing processes, i.e. process development (PD). This could involve integrating sustainability goals/objectives (e.g. resource efficiency or energy saving) into production processes, adopting circular economy principles, and monitoring suppliers in different tiers to ensure they meet sustainability standards, as well as designing new products (i.e. product development or PD) while respecting environmental and social issues demanded by customers (i.e. sustainable product developments).

In addition, when firms are more responsive to the customer preferences for sustainable products, they may seek to modify their current supply chain configuration, that is supply chain reconfiguration (SCR). For example, they may source materials only from those suppliers that prioritize sustainability, evidenced by environmental certifications, such as ISO 14001. This is coherent with Mohaghegh, Åhlström, and Blasi (2023) main findings that supply chain reconfiguration is essential for the agility-sustainability relationship. The authors conclude that supply chain reconfiguration can serve as a transformational capability by which firms can modify their existing practices and employ a new configuration by incorporating sustainability practices.

On the top of the proposed TISM model, the three interconnected dimensions of sustainable business performance, namely, economic, environmental, and social performance are located. It is inferred that product and process development (PD) emphasizing the integration of the sustainability standards (e.g. green packaging, waste reduction, clean production, eco-design principles) and supply chain reconfiguration (SCR) (e.g. employing suppliers the use renewable energy sources) can lower their energy consumption and associated costs, leading to increased profitability. Furthermore, investing in employee safety, value, and satisfaction by fostering a supportive work environment that enables personal growth and development can result in substantial benefits in terms of enhanced productivity, motivation, and resilience. Indeed, when employees feel secure, appreciated, and respected in the workplace (through providing training, job development programs, and fair compensation), they are more likely to be actively engaged and committed to their work, which can in turn translated into lower costs and higher profitability. Although we consider economic, environmental, and social dimensions equally important, following the TBL perspective, we argue that environmental and social dimensions are necessary to improve economic performance.

From a broad perspective and with the focus on how LSC and ASC can be developed, our results align with the proposition that LSC and ASC are supportive strategies, where ASC can potentially serve as a natural development or the next logical step of LSC (Mason-Jones, Naylor, and Towill 2000; Mohaghegh, Blasi, and Größler 2021; Narasimhan, Swink, and Kim 2006). We indeed offer a counterargument to the conventional viewpoint of treating lean and agile approaches as divergent paradigms or mutually exclusive concepts (e.g. Goldsby, Griffis, and Roath 2006; Richards 1996). Building upon LSC and ASC as supportive strategies, we argue that eliminating waste and value-added activities (through leanness) and improving flexibility and responsiveness (through agility) are both extremely vital for sustainable business performance in terms of economic, environmental, and social gains. Our findings also highlight the importance of establishing an ambidextrous supply chain for sustainable business performance, where LSC and ASC as seemingly two conflicting but interdependent strategies should be simultaneously adopted. However, we argue that, in practice, it is difficult to rule out where to precisely locate the leagile decoupling point. The reason is that some leagile supply chain practices are commonly shared by both LSC and ASC strategies. As an example, customer focus and supplier partnership, although considered as the LSC practices in our proposed TISM model, are equally crucial for agility as well. Similarly, supply chain integration and supply chain learning which are currently placed at the agility level, are also promoted in the extant studies as relevant practices for enhancing leanness.

6. Conclusion

6.1. Theoretical contributions

This study contributes to the operations and supply chain management literature in four ways. First, our study is an innovative empirical attempt to propose how common and unique practices of LSC and ASC, in an integrated leagile model, interact to improve the triple dimensions of sustainable business performance (i.e. economic, environmental, and social performance). In response to the need of studying the characteristics, attributes, and good implementation practices of leagility (Bhamra et al. 2021; Naim and Gosling 2011), we show the building blocks of the leagile supply chain strategy and demonstrate their interactions for sustainable business performance, giving equal importance to economic, environmental, and social gains. Second, we promote LSC and ASC as mutually supportive approaches, where LSC can serve as the potential pre-requisite of ASC. Our study indeed provides additional evidence for the effectiveness of hybrid supply chain strategies (i.e. leagile) rather than unilateral ones (i.e. purely lean or purely agile strategies) to achieve sustainable outcomes. However, we argue that to achieve sustainable outcomes, first cost efficiency (leanness) and then flexibility and responsiveness (agility) should be followed. Third, the main findings of this study shed light on why many firms, despite the implementation of lean management, fail to achieve long-lived and sustainable outcomes. To this end, we argue that LSC practices, if successfully coupled with ASC practices, can result in sustainable outcomes in the form of economic, environmental, and social gains. Forth, we add to the existing literature on ambidexterity. Given that, lean and agile strategies are seemingly conflicting yet interdependent strategies, firms, in the face of turbulent environments, are required to manage the paradox of exploitation-exploration by simultaneously adopting LSC and ASC.

6.2. Managerial implications

Our study also provides significant implications and insightful information for managers and practitioners. First, we advocate for the adoption of the hybrid leagile supply chain strategy, arguing that neither LSC (or ASC) is better nor worse than ASC (or LSC). Rather, we reinforce the notion that ‘leanness’ and ‘agility’ are complementary strategies of an effective supply chain strategy. We encourage managers to simultaneously adopt LSC and ASC not only for economic gains but also for environmental and social benefits, promoting an ambidextrous supply chain. Based on our findings, we suggest managers that to achieve sustainable business performance, sufficient and equal attention should be given to both waste elimination (through LSC) and enhancing responsiveness and flexibility (through ASC). Second, with a particular focus on ‘leanness’ and ‘agility’ within the supply chain context, we address a set of relevant supply chain practices and explain how they should be employed for sustainable outcomes. Our findings are valuable for managers because understanding the building blocks of leagile supply chains and how to develop and prioritize them could be necessary for successfully implementing the leagile strategy. Third, we emphasize the crucial role of senior managers in providing a supportive context for the leagile strategy. To this end, we suggest managers to follow a formal strategic plan, establish long-term goals and a sustainability vision for the firm, demonstrate full commitment to improvement projects aligned with sustainability objectives, promote continuous improvement culture, and consider HR policies and strategies for training and employee empowerment.

6.3. Limitations and directions for future studies

There are several limitations to this study that can provide indications for future studies. First, this study does not aim to test the hypotheses. Rather, we propose a hierarchical model for the leagile supply chain strategy with the goal of enhancing economic, environmental, and social performance. Future studies are therefore encouraged to validate our proposed model. As an example, researchers could use structural equation modelling (SEM) to empirically test the relationships between the leagile supply chain strategy (and its constituting blocks) and sustainable business performance, across the TBL. To this end, the proposed building blocks of the leagile supply chain strategy can serve as a guide for the empirical operationalization of the leagility construct. Second, the proposed TISM model is based on the authors’ interpretation of the data obtained from the interviews. Although several expedients were employed to ensure the reliability and validity of our findings (e.g. literature enfolding and researchers’ feedback), other researchers may have different perspectives and understandings, which could limit the generalizability of our study. We hence encourage fieldwork and the application of secondary data (e.g. archival data) to validate our findings, mitigating subjectivity and human biases. Third, the developed TISM model is based on the opinions of only four experts, which may not comprehensively represent the views of the entire population. Fourth, a general limitation of (T)ISM is that it mainly assumes that the relationships among variables are linear and static, which may not fully reflect the real-world scenarios with dynamic and evolving systems. Thus, we promote the need of adopting a ‘systems thinking’ approach to study the complex system, having non-linear relationships and feedback loops for future studies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This research is partially funded by Mistra Center for Sustainable Markets (MISUM).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This research is partially funded by Mistra Center for Sustainable Markets (MISUM).

Notes on contributors

Matin Mohaghegh

Dr. Matin Mohaghegh is a postdoctoral researcher at the House of Innovation, Stockholm School of Economics. His research focuses on operations and supply chain strategies, including lean management and agile supply chain. Particularly, he investigates how such strategies integrate sustainability to enhance sustainable business performance across the triple bottom line (TBL: economic, environmental, and social performance). His research has been published in Production Planning & Control, Journal of Cleaner Production, Management Decision, Management Research Review, and Systemic Practice and Action Research.

Andreas Größler

Dr. Andreas Größler is a full professor of operations management at the University of Stuttgart (Germany) where he currently is also managing director of the School of Management. He teaches operations strategy and behavioral operations management courses at the undergraduate, master, and doctoral levels. His research interests lie in the fields of sustainable operations management, in applying system dynamics modelling and simulation in business and not-for-profit organizations, and in investigating individual and organizational dynamic decision-making. Dr. Größler is the executive editor of System Dynamics Review and has served on the editorial board of other scientific journals.

Notes

1 Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) is translated as cross-impact matrix multiplication.

2 For the procedure, check the flow diagram (Figure 1).