
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12268-2
10.1016/j.heliyon.2024.e36237
e36237
Research Article
Sustainable performance in SMEs using big data analytics for closed-loop supply chains and reverse omnichannel
Khan Syed Abdul Rehman Sarehman_cscp@yahoo.com
a⁎
Tahir Muhammad Sohail sohailutm@gmail.com
b
Sheikh Adnan Ahmed Adnan.ahmed@aumc.edu.pk
cd
a School of Management and Engineering, Xuzhou University of Technology, Xuzhou, China
b Comsats University Islamabad, Vehari Campus, Pakistan
c Department of Business Administration, Air University Islamabad, Multan Campus, Pakistan
d College of Business, University of Buraimi, Oman
⁎ Corresponding author. Sarehman_cscp@yahoo.com
16 8 2024
30 8 2024
16 8 2024
10 16 e3623710 9 2023
12 8 2024
12 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
The study aims to examine the advantages of utilizing big data analytics (BDA) on circular economy (CE) dual systems, which are closed-loop supply chain (CLSC) and the reverse omnichannel (ROC), aiming to enhance sustainable firm performance (SFP) of the small and medium based enterprises (SMEs). The product return knowledge (PRK) can reinforce the proposed network of relationships and facilitate the approach of active returns in CE. Using the structural equation model (SEM) in AMOS v24, the researchers examined the hypotheses using data from a simple random sample of 232 SMEs in Pakistan. Though ROC solutions provide rare performance-related benefits, however, BDA makes the CE system more efficient. CLSC gets strengthened when the PRK is developed. The improved PRK facilitates the CLSC network and enhances its performance. This study adopts a novel approach to studying CE by considering the dual system of CE in the forms of CLSC and ROC. The research investigates whether the BDA facilitates improved product return processes by improving CLSC operations and achieving service capabilities using ROC. Finally, the proposed framework is the first to investigate the benefits evolving from the PRK, which enhance the capability to sense, seize, and reconfigure the process and facilitate the approach of active return in CE. The findings suggest that firms must decide carefully between CLSC and ROC. Choosing CLSC requires higher operational capabilities, whereas deciding on ROC requires higher service capabilities. Lastly, PRK is necessary for achieving the firm's performance objectives.

Keywords

Close-loop supply chain
Reverse omnichannel
Big data analytics
Circular economy
Product return knowledge
==== Body
pmc1 Introduction

The circular economy (CE) notion has recently acquired substantial attention from academicians and industry professionals. Environmental activists, society, and governments want businesses and supply chains to follow the principles of CE while developing their business models or designing their corporate strategies and achieving sustainable performance. Circular economy (CE) benefits society by conserving resource use and waste production through recycling, remanufacturing, and refurbishing [1]. The concept of a closed-loop supply chain (CLSC) has garnered significant interest within the realm of corporate social responsibility (CSR) in recent times. Integrating forward and reverse logistics in a novel design is a crucial aspect of CLSC and involves numerous schemes and processes of operations to control and maximize the creation of value throughout the product's life cycle [2]. Souza [3] described the CLSC concept as a phenomenon where the supply chain maintains and recovers the value from take-back products by keeping resource consumption and waste at a minimum. During this process, the CLSC ensures the designing of ad hoc return programs and administering the take-back circularity to close the CLSC properly [4]. CLSC networks have been CE's primary focus, but modern mechanisms like omnichannel are also linked to the collection systems in developed economies. The current study uses the reverse omnichannel (ROC) and defines it as the capability to offer and integrate multiple collection options to the customers. The present study was driven by the scarcity of opportunities to develop a connection and establish a structure to regulate Closed Loop Supply Chain (CLSC) systems and ROC. CLSC handles the operational parts of CE, while ROC covers the service aspects of CE.

Several prior research studies reinforce the importance of studying ROC in a CE system context. Ye, Lau, and Teo [5] conducted a study highlighting that 72 % of online customers physically return the products, thus utilizing ROC. Jin et al. [6] presented a theoretical framework where the buy-online-return-offline model presents many possibilities for building competitive advantages. Literature indicates the novelty in analyzing the dual reverse strategy with a prime focus on assessing the convenience of CE by concentrating on CLSC systems. These multiple return options inspire the current study, which primarily focuses on developing a framework that facilitates capitalizing on CLSC network and ROC choices to manage the CE systems. Therefore, the first objective of the current study is to investigate the association between CLSC, ROC, and sustainable firm performance.

In the CLSC, ROC creates circular opportunities, but digital technologies must support the diverse challenges associated with CE systems [7,8]. Multiple uncertainties surround CE systems, and big data analytics (BDA) provides the appropriate technological fit to close the loop properly and guarantee a trustable return. Though BDA is advantageous to CE systems, present literature on sustainable systems suggests further examining its impact [9]. The past studies primarily focused on implementing the blockchain to supply chains or CLSC or ROC [2]. As a result, the second objective of this study is to determine the benefits of big data analytics (BDA) in the circular economy (CE) system, which consists of the CLSC and ROC.

CE systems involve the networks of firms and a large consumer base. Current literature on CLSC highlights that incentive mechanisms have been investigated for both consumers and collectors as enablers in the CE system [10]. These incentives were implemented in a system of blockchain using behavioral agency theory, which illustrated that the stakeholders, in the presence of opportunities, are motivated to behave according to their abilities to align their actions with other agents. Nonetheless, a lack of empirical evidence has been noticed about using product return knowledge (PRK) as an enabler of product return in a dual CE system. Consequently, the third objective of this study is to examine the function of PRK in a dual circular economy (CE) system. Simultaneously, implementing BDA can use PRK to sense and reconfigure the approaches used by the stakeholders. Therefore, the PRK can play a dual role and act as an enabler in activating the CE system while initiating the critical functions of BDA.

To achieve these objectives, this study proposes a theoretical framework. It will investigate and verify that BDA is the best-fit technology [11] that improves the CE in its dual form of CLSC and ROC. Later, the benefits obtained from the BDA-CE system and its impact on sustainable performance (SP) will be explored. The findings of this study contribute to the literature by highlighting the benefits of BDA in the CE system, which includes business performance objectives and product knowledge reconfiguration. Therefore, it is considered within the scope of this study and is a novel contribution based on the behavioral agency theory. It also contributes by signifying the vital role PRK plays in achieving business performance objectives. The PRK can play a dual role and act as an enabler in activating the CE system and initiating the critical functions of BDA.

After the introduction, the next portion involves a detailed analysis of the current literature, where the links between the different components in the study model are thoroughly examined. The methodology section of the study paper details the specific methods used to gather and analyze the data. The article's findings are presented in the results and analysis sections. Furthermore, the article finishes by examining the hypotheses related to the study's findings and identifying areas of research that need further exploration. It also discusses the theoretical and practical implications of the study, provides a conclusion, acknowledges any limitations, and makes recommendations for future research.

2 Literature review

2.1 Behavioral agency theory (BAT) as underpinning theory

Pepper and Gore [12] presented the behavioral agency theory and argued that the model of economic man uses a narrow approach and is simple in agency theory. According to BAT, the model of economic man is based on the assumption of bounded rationality, which acknowledges the importance of the agent's knowledge and ability to perform an activity that makes them make rational choices, avoid risks, and time discounting in uncertain situations. BAT argues that the prime objective is to boost the agent's performance, which depends on monetary incentives and relies upon the knowledge the agent shares [13]. Moreover, if the relationship is not based on timely equitable compensations, the agent's actions and skills will have no benefits. The current study used BAT as a theoretical lens and considers implementing BDA as an effort to boost performance under CE systems.

2.2 Theoretical context and hypothesis development

Big data analytics (BDA) has excellent potential to bring revolutions in medicine, business, and operations management [14]. BDA is the process of collecting, organizing, interpreting, and storing complex, heterogeneous, and large volumes of datasets that require complex processes and tools to process and convert them to helpful information [15]. It creates new social and business opportunities using complementary procedures [16]. BDA is beneficial in developing an effective supply chain in circular systems. Previous studies have highlighted that BDA allows accurate, valuable information and extracts useful insights that facilitate the decision-making process in CE systems [17]. According to Jeble et al. [18], BDA positively impacts the firm's social, economic, and environmental performance and can facilitate improving firm performance [19]. End consumers can benefit from the BDA by accessing more accurate, reliable information during purchasing or returning decisions [20]. BDA provides all the details about the product and keeps records of its raw materials, production, equipment, machinery, usage, waste, etc. [9]. Using these datasets, BDA improves decision-making and enhances performance. BDA provides the information throughout the product life cycle (PLC) by sensing, seizing, and reconfiguring the return phases. It facilitates a reliable estimation of the residual value of the return-backs and its overall environmental impact on PLC (Rusch, Schöggl and Baumgartner, 2022), which are the key outcomes of responsible digitalization. BDA enhances the organization's capacity to seize opportunities, increase productivity, and improve the processes to achieve differential and competitive advantages [21]. BDA facilitates the business with enhanced decision-making capabilities at multiple avenues. Accordingly, companies using BDA demonstrate responsible behavior by ensuring improved social, economic, and environmental performance in a CE system [22]. Consequently, it allows the business to recognize and apply digital technologies with the duty to reap their rewards in reaching the triple-bottom-line performance objectives [20]. Therefore, it is inferred that BDA is connected with sustainable performance. We hypothesize that.H1 Implementing big data analytics (BDA) positively impacts sustainable firm performance (SFP).

The present literature highlighted a gap in the benefits of BDA for CLSC. BDA implementation helps to regulate water management [23], CO2 emissions [24], and waste management [9]. BDA utilization helps organizations better understand internal data and improve supply chain activities [25]. Jin et al. [6] highlighted that lack of information and uncertainty of available information affect CLSC. Bag et al. [26] analyzed the impact of BDA on reverse logistics decision-making. They concluded that BDA equips the firms strategically and tactically, and managers can make better decisions to improve the remanufacturing processes. However, the scarcity of literature requires filling the existing gap regarding the benefits of BDA in CE systems with an empirically verified framework [27]. CLSC is used to address the challenges arising from environmental concerns, resource scarcity, and product return-related aspects [28]. CLSC facilitates achieving the performance objectives by implementing presorting, overall quality of return, and most significantly reducing carbon emission [29]. Therefore, the current study investigates the advantages BDA provides to CLSC and proposes the following hypothesis.

H2 Implementing big data analytics (BDA) positively impacts closed-loop supply chain (CLSC)

H3 Closed-loop supply chain (CLSC) mediates the relationship between big data analytics (BDA) and sustainable firm performance (SFP).

ROC is defined as the choices provided to the customers to complete the product return process. It includes information regarding the product return collection points of product reverse that can be retrieved, traced, and modified [30]. Omnichannel is vital as a strategic tool to enable e-commerce and produce a flawless consumer purchase experience (Ye, Lau and Teo, 2018). An omnichannel approach in the circular economy (CE) context offers numerous benefits [31]. Omnichannel facilitates engaging customers through inverse activities, including the product's return to the retail store and shipment to the retailers or the manufacturers [32]. Besides, integrating channels through technology maps the guidelines of retailers' activities and adds value to the supply chain [33]. Implementing emerging technologies for reverse logistics (RL) impacts the supply chain functions in CE systems [34]. A recent survey on B2B firms indicated that BDA supports CRM as well [35]. Configuring the RL system according to the information collected from BDA shows equi-finality and customer satisfaction [36]. BDA develops risk resilience in business. In a CE context, BDA presents a unique landscape by involving the stakeholders as a significant component in RL decision-making [37]. Although many studies have provided evidence of digital technologies for forward logistics by facilitating the decision-making process through detailed information [8,21,38], Further investigation is required to analyze the impact of Big Data Analytics (BDA) on ROC. Thus, the subsequent hypothesis posits.

H4 Reverse Omnichannel (ROC) is positively impacted by implementing Big Data Analytics (BDA).

Challenges like complexity, lack of efficiency, rapid delivery systems, order flexibility, and compliance with quality standards, when coupled with CE systems, make it the firm's obligation to adopt omnichannel applications [39]. In these circumstances, existing reverse and forward logistics systems need to be integrated into an omnichannel solution where customers have several choices of returning the product and collecting agents use optimum utilization of logistics. The overall process of returning the product does not exhibit high uncertainty levels [40].

ROC is defined as the choices provided to the customers to complete the product return process. It includes information regarding the product return collection points of product reverse that can be retrieved, traced, and modified [30]. Kamal et al. [41] revealed that due to the abundance of e-waste, the return intention of electronic equipment was studied. It was determined that specific information about product return channels influences the return intention. De Giovanni [2] claimed that having a successful CLSC system and structure ensures that product returns are simple and affordable. It also provides ad hoc operational capabilities, such as investments in recycling technology and facilities [42], making it convenient for the consumer to return the product online or at the location [43]. Lastly, the relationship between CLSC and ROC is developed within the context of BDA, allowing for the accumulation, organization, and storage of a large dataset that provides enriched information on the ROC. Consequently, only in CLSC systems is ROC the optimal option, as it ensures seamless logistics and operational activities to support the take-back product. Therefore, it is hypothesized.

H5 The CLSC system management has a positive impact on ROC.

Past literature highlights that CLSC mediates the relationship between BDA and ROC. For instance, Schmidt et al. (2021) concluded that CLSC improves firm performance by optimizing its resources. Integrating the BDA, CLSC, and ROC facilitates the firms to manage better product returns, reduce waste, and foster sustainability [44]. Hence, CLSC intervenes in the relationship between BDA and ROC. Therefore, it is hypothesized that.

H6 CLSC mediates the relationship between BDA and ROC.

There is limited literature available on the relationship between CLSC and performance. The analysis of the impact of CLSC on economic and environmental performance concluded that CLSC systems need to be designed and constructed since they are not sustainable [2]. In another investigation, Shaharudin et al. [45] revealed that CLSC is positively associated with return process effectiveness. Based on pertinent studies, the literature revealed a deficiency in empirical research examining the benefits of CLSC on firm performance. Existing empirical research on the connection between the CLSC and performance has a limited scope. This empirical study investigates the impact of CLSC precursors on sustainable performance. Consequently, the following hypothesis is put forward.

H7 The implementation of the CLSC system management has a positive influence on sustainable firm performance (SFP).

Similar to CLSC, ROC also facilitates enhanced performance. The study conducted by Nageswaran, Cho and Scheller-Wolf [46] mentioned that a firm's profits are linked to the preferences of customers' selection of reverse channels. Bernon, Cullen and Gorst [47] analyzed the benefits of ROC for performance in a qualitative study and compared the strategies of 15 firms. The results concluded that ROC considerably enhances performance by saving costs, taking environmental protection initiatives, and increasing sales and returns. Another qualitative study gathered data from seven companies implementing omnichannel strategies. The study determined that the process of reverse logistics is closely linked with CLSC to ensure clients are provided with a return process, pricing information for recycling, and feedback [48]. Aćimović, Mijušković and Rajić [49] studied the RL impact on green supply chain competitiveness and concluded that it is dependent upon the customer choice for product return. Another study recommended investigating ROC's effects on performance [31]. So, the present study focuses on the ROC from the firm's perspective, and it is hypothesized, based on the preceding discussion, that.

H8 The reverse omnichannel (ROC) positively impacts sustainable firm performance (SFP).

A study indicated that firms need dynamic capabilities to capitalize on the omnichannel [50]. BDA provides valuable insights, enhancing the business's operational performance [32]. Similarly, Le and Nguyen-Le [51] mentioned that ROC enables firms to utilize optimum resources, enhancing their performance. Integrating the BDA and ROC hypothesizes the firms in achieving sustainability goals [44]. Consequently, the research indicates that CLSC and ROC intervene in the relationship between BDA and SFP. The following hypotheses are therefore proposed.

H9 The ROC mediates the relationship between BDA and SFP.

H10 The CLSC & ROC sequentially mediate the relationship between big data analytics (BDA) and sustainable firm performance (SFP).

Shaharudin et al. [45] defined PRK as an individual's knowledge about returning the product at the end of PLC or after completely utilizing it. The current study considered PRK as the knowledge of product return-related activities. PRK creates awareness about carbon emissions during production and motivates customers to adopt CLSC to contribute to low carbon emissions [21]. PRK can provide leverage to the firms implementing BDA to develop strategies to integrate their cross-channels (Timoumi, Gangwar and Mantrala, 2022). In their investigation, Xie et al. (2023) revealed that customer services related to product return activities, buying online, and store returns create satisfied customers. In another study, perceived self-efficacy, product knowledge, and social influence developed an association to enhance customer experience during channel switching behavior. Hence, PRK is an essential factor in deciding about the channels, and in a CE context, it can influence the CLSC and ROC. So, we hypothesize that.

H11 The Product return knowledge (PRK) moderates the connection between big data analytics (BDA) and closed-loop supply chain (CLSC).

H12 The Product return knowledge (PRK) moderates the relationship between big data analytics (BDA) and reverse omnichannel (ROC).

The graphical representation of the presented hypotheses is depicted in Fig. 1.Fig. 1 Theoretical framework.

Fig. 1

3 3. methodology

The data for this study was obtained by implementing a survey questionnaire, which is widely recognized as an efficient approach for rapidly gathering responses from a sizable population. The questionnaire comprised inquiries about the participants' demographic characteristics and details regarding the constructs under investigation. Before data collection, the respondents were explained about the purpose of this study, their consent was taken according to ethical guidelines, and anonymity was ensured. Besides, the questionnaire was pre-tested in terms of content and face validity by experts, and they have also recommended that the questionnaire is relevant, ethical, and easy to understand.

A compilation of small and medium enterprises (SMEs) was generated using the database provided by the SME development authority [52] in Pakistan as the SMEs are the unit of analysis. SMEs in Pakistan contribute almost 40 % of the GDP [52] therefore it is important to study their role. The present investigation employed a hybrid sampling methodology. In the beginning, quota sampling was implemented, and the small and medium-sized enterprises (SMEs) were categorized by industry. An equal quota of 20 % was initially allocated, and the devised questionnaire was distributed randomly. The primary substantiation of the randomly selected SMEs' adherence to the CE is derived from secondary sources concerning the organizations with which these SMEs were engaged in digital transformation on a predominant basis. Additionally, a questionnaire was administered to various key personnel within the SMEs in Pakistan, including the owner, production manager, marketing manager, supply chain manager, procurement manager, logistics manager, and distribution manager. A total of 400 questionnaires were disseminated between November 2022 and January 2023. However, out of the intended sample, only 232 questionnaires with valid replies were received, resulting in a response rate of 58 %.

The current study uses structural equation modeling (SEM) to test the causal relationship between the proposed constructs. SEM is considered a handy tool because it is regarded as the best to analyze and understand the associations among different factors and how they relate to each other. SEM facilitates testing a theory by investigating how the different variables interact with each other. SEM also facilitates analyzing complex relationships by generating accurate measurements and comparing different scenarios to understand the clear connections between complex variables [53,54]. Fig. 2 below represents the summarized flow of the research methodology.Fig. 2 Research methodology flow.

Fig. 2

3.1 Measures

This study developed the questionnaire using already existing scales. The BDA scale was derived from investigations of Wamba et al. [55], Abbas [56], and Srinivasan & Swink [57] and consisted of a total of 10 items. The construct of CLSC was adopted from the studies of Shaharudin et al. [45] and Bhatia & Kumar Srivastava (2019) and contained 6 items. ROC included 4 items adapted from the studies of Verhoef et al. [58] and Shaharudin et al. [45]. SFP utilized a six-item scale adapted from studies of Hourneaux et al. [59], Abbas [56] and Wang et al. [60]. Additionally, six scale items of PRK were adopted from the study of Kamal et al. [41] and Jena and Sarmah [61]The participants' responses to the variables were evaluated using a five-point Likert scale, where "1″ represented strong disagreement and "5″ represented strong agreement. The questionnaire developed was subjected to a rigorous assessment process by specialists and professors. The specific question items utilized for each variable can be found in Appendix 1.

4 Results

Table 1 below represents the demographic characteristics of the current study sample.Table 1 Sample demographics (N = 232).

Table 1Characteristics	No. of Respondents	%	
Annual Sales (In million PKR)		
<10	20	9 %	
6–10	34	15 %	
51–100	55	24 %	
>100	123	53 %	
Number of Employees	
<50	13	6 %	
50–100	44	19 %	
101–200	27	12 %	
>200	148	64 %	
Designation	
Owner	29	13 %	
Production Manager	51	22 %	
Marketing Manager	17	7 %	
Supply Chain Manager	23	10 %	
Procurement Manager	36	16 %	
Logistics Manager	36	16 %	
Distribution Manager	40	17 %	
Industry	
Food and drinks	34	15 %	
Fashion and clothing	28	12 %	
Healthcare and medicine	32	14 %	
Automobile	21	9 %	
Furniture and fixture	27	12 %	
Digital Marketing & E-commerce	25	11 %	
Entertainment	24	10 %	
Chemicals	29	13 %	
Other	12	5 %	
Source: Table created by authors

The convergent validity, reliability, and factor loadings are displayed in Table 2.Table 2 Convergent Validity, Reliability, and Factor loadings.

Table 2Variables	Items	Convergent Validity	Internal Consistency Reliability	
Loadings	AVE	Cronbach's alpha	CR	
>0.708	>0.50	>0.70	>0.70	
Big Data Analytics (BDA)		0.557	0.786	0.79	
Sensing	SENS1	0.836				
	SENS2	0.860				
	SENS3	0.802				
Seizing			0.736	0.881	0.891	
	SEIZ1	0.885				
	SEIZ2	0.747				
	SEIZ3	0.880				
Reconfiguring		0.624	0.859	0.862	
	RECO1	0.774				
	RECO2	0.871				
	RECO3	0.671				
	RECO4	0.842				
Reverse Omni-Channel (ROC)	0.644	0.842	0.861	
	ROC1	0.858				
	ROC2	0.847				
	ROC3	0.830				
	ROC4	0.849				
	ROC5	0.807				
Closed-loop Supply Chain (CLSC)	0.501	0.816	0.795	
	CLSC1	0.711				
	CLSC2	0.783				
	CLSC3	0.614				
	CLSC4	0.622				
	CLSC5	0.798				
	CLSC6	0.708				
Product Return Knowledge (PRK)	0.512	0.861	0.862	
	PRK1	0.717				
	PRK2	0.763				
	PRK3	0.783				
	PRK4	0.748				
	PRK5	0.679				
	PRK6	0.729				
Sustainable Firm Performance (SFP)	0.603	0.815	0.832	
	SFP1	0.792				
	SFP2	0.695				
	SFP3	0.856				
	SFP4	0.833				
	SFP5	0.854				
	SFP6	0.848				
Source: Table created by authors

The dataset's discriminant validity was estimated per the guidelines, and the results were checked through the correlation matrix by Ref. [62] and the diagonal values greater than 0.70, as recommended as shown in Table 3.Table 3 Discriminant validity.

Table 3	SFP	PRK	ROC	CLSC	RECO	SEIZ	SENS	
SFP	0.776							
PRK	0.094	0.715						
ROC	0.072	0.149*	0.802					
CLSC	−0.01	0.239***	0.171**	0.708				
RECO	0.181**	0.387***	0.159**	0.332***	0.79			
SEIZ	−0.008	0.525***	0.021	0.186**	0.335***	0.858		
SENS	0.073	−0.011	−0.059	0.009	−0.038	0.033	0.746	
Note: Significance of Correlations: †p < 0.100; *p < 0.050; **p < 0.010; ***p < 0.001.

SFP= Sustainable firm performance; PRK= Product return knowledge; ROC= Reverse omnichannel; CLSC= Closed-loop supply chain; RECO= Reconfiguring; SEIZ= Seizing; SENS= Sensing.

Source: Table created by authors

4.1 Measurement model

The estimation of the measurement model values was conducted using AMOS v24. Following the suggested guidelines, the values obtained from the initial measurement model were deemed satisfactory (Hair et al., 2018). However, for a good-fit model, the two items, CLSC4 and CLSC6, were dropped, and afterward, the measurement model values indicated an excellent fit. The findings of the measurement model are depicted in Fig. 3.Fig. 3 Measurement model.

Fig. 3Source: Figure created by authors

Table 4 below provides details of the measurement model fitness values. All the values indicate an excellent fit measurement model.Table 4 Measurement model fitness values.

Table 4CFA Indicator	Threshold Value	Initial model	Re-specified model	
CMIN/DF	≤3	1.823	1.409	
GFI	≥0.80	0.785	0.824	
AGFI	≥0.80	0.759	0.800	
CFI	≥0.90	0.933	0.969	
RMSEA	≤0.08	0.05	0.035	
NFI	≥0.90	0.779	0.91	
TLI	≥0.90	0.925	0.965	
IFI	≥0.90	0.933	0.97	
PCLOSE	>0.05	0.512	1.000	
SRMR	<0.08	0.05	0.046	
Source: Table by authors

4.2 Structural equation model (SEM) and hypotheses testing

As per the criteria, the next step was to estimate SEM and test the proposed hypotheses [63]. The SEM results are shown in Fig. 4.Fig. 4 Structural equation model.

Fig. 4

The results show that BDA R-Square is 73 % which shows significant improvement in the SFP. Whereas the positive effect of BDA on CLSC is 29.3 %, on ROC it is 35.6 %. Similarly, when CLSC mediated between BDA and SFP it resulted in an improvement of 6.7 % and 5.3 % respectively. These results indicate the significant role BDA, CLSC, and ROC play in enhancing the SFP for SMEs.

Afterward, each hypothesis was tested. At first, the direct hypotheses were estimated, and the coefficient value β = 0.178 was significant at p < 0.046 accepting H1. The coefficient value β = 0.293 was significant at p < 0.003, accepting H2. The coefficient value β = 0.356 was significant at p < 0.006, accepting H4. The coefficient value β = 0.151was significant at p < 0.030, accepting H5. The coefficient value β = 0.127 was significant at p < 0.049, accepting H7. The coefficient value β = 0.146 was significant at p < 0.004, accepting H9. The results are shown in Table 5.Table 5 Direct Path effect coefficients.

Table 5Hypothesis	Structural Relationships	Coefficient (β)	Standard error	t Statistics	p-value	
H1	BDA-- > SFP	0.178	0.089	1.993	0.046	
H2	BDA-- > CLSC	0.293	0.098	2.979	0.003	
H4	BDA-- > ROC	0.356	0.125	2.848	0.006	
H5	CLSC-- > ROC	0.151	0.070	2.176	0.030	
H7	CLSC-- > SFP	0.127	0.064	1.984	0.049	
H9	ROC-- > SFP	0.146	0.062	2.354	0.004	
Note: SFP= Sustainable firm performance; PRK= Product return knowledge; ROC= Reverse omnichannel; CLSC= Closed-loop supply chain; BDA= Big data analytics.

Source: Table by authors

Next, the values for indirect hypotheses were estimated. The current study has proposed three mediations and one serial mediation hypothesis. The results of H3 indicate the indirect effects of BDA on SFP through CLSC are more positively significant and result in improved SFP. Similarly, the results of H6 indicated that the indirect relationship between BDA and ROC through CLSC exists and CLSC plays the mediating role to improve the outcomes of ROC. Finally, the results of H9 show that the ROC plays a significant mediating role in the relationship between BDA and SFP. Although BDA helps increase the SFP in the presence of ROC the results significantly and positively improved. The results are shown in Table 6.Table 6 Direct and indirect effects.

Table 6Hypothesis	Indirect Effect	Results	
BDA→CLSC→SFP	0.067***	Significant	
BDA→CLSC→ROC	0.044***	Significant	
BDA→ROC→SFP	0.053***	Significant	
Note: *p < 0.050; **p < 0.010; ***p < 0.001.

Note: SFP= Sustainable firm performance; ROC= Reverse omnichannel; CLSC= Closed-loop supply chain; BDA= Big data analytics.

Source: Table by authors

The results of serial mediation are shown in Table 7. The results show that serial mediation is supported.Table 7 Serial mediation results.

Table 7Hypothesis	Direct Effect	Indirect Effect	Results	
BDA→CLSC →ROC →SFP	0.178***	0.08***	Supported	
Note: *p < 0.050; **p < 0.010; ***p < 0.001.

Note: SFP= Sustainable firm performance; PRK= Product return knowledge; ROC= Reverse omnichannel; CLSC= Closed-loop supply chain; BDA= Big data analytics.

Source: Table by authors

This study has developed two hypotheses about the concept of moderation. Fig. 5 depicts the moderating effect of PRK on the association between BDA and CLSC. Based on the results, PRK enhances the favorable correlation between BDA and CLSC.Fig. 5 Moderation effects of PRK in the relationship between BDA and CLSC.

Fig. 5

The influence of PRK on the relationship between ROC and BDA is depicted in Fig. 6. According to the findings, PRK strengthens the positive relationship between BDA and ROC.Fig. 6 Moderation effects of PRK in the relationship between BDA and ROC.

Fig. 6

5 Discussion

The current study explored the implications of BDA in a dual CE system of CLSC and ROC. Digital transformation in the CE context benefits the CE systems and increases the performance of the firms. The current study examined BDA's effects on sustainable firm performance (SFP) in a dual CE system and developed multiple hypotheses. According to H1, the research findings suggest a good correlation between using Big Data Analytics (BDA) and sustainable firm performance (SFP) effectiveness. The results showed that organizations implement BDA to develop a sense of internal and external environment to track the environmental changes and continuously interpret information to achieve competitive advantages. Such advantages of implementing BDA help organizations achieve higher profits, increase market growth, develop a network of customers, become more efficient in resource utilization, and contribute well on social and environmental fronts. These findings align with Perçin's [23] study, which revealed that BDA implementation helps regulate water management and CO2 emission. Similarly, Tseng et al. [64] concluded that BDA implementation enhances the business's performance.

The findings of the H2 study revealed a significant correlation between the deployment of BDA (Big Data Analytics) and CLSC (closed-loop supply chain). The study results indicate that adopting Big Data Analytics (BDA) leads to increased engagement by enterprises in recycling, refurbishing products, monitoring product alterations, and providing cost-effective product return alternatives. The findings presented in this study are consistent with prior studies undertaken by Xie et al. [65], which mentioned that BDA implementation resolves the problem of low-quality recycling. Similarly, BDA equips the firms with an improved decision-making system and improves the remanufacturing of returned products [26]. The acceptance of H3 further substantiates the existence of a positive correlation between the deployment of BDA (Big Data Analytics) and the ROC (Reverse omnichannel). The study's results indicate that adopting Big Data Analytics (BDA) leads to increased investments in reverse omnichannel (ROC) across several channels, including manufacturers, distribution centers, and online and offline retail outlets. Edwin Cheng et al. (2022) shared similar findings and mentioned that BDA implementation provides more accurate information for investment decisions. Ren et al. [66] also concluded that BDA facilitates conducting more informed business activities and managing complex decisions throughout the complex PLC. H4 stated a positive relationship between CLSC and ROC, and the findings demonstrated that firms investing more in CLSC activities like recycling, refurbishing, and offering options of product replacements to improve the investment in ROC of return, like a return to manufacturer, distributor, etc. These results match the study of De Giovanni and Zaccour [10], which mentioned that having a system and structure of efficient CLSC guarantees the product return to be easy and less costly. H5 proposed a positive relationship and was accepted based on the findings that investments in CLSC enable firms to perform better economically, socially, and environmentally. Past studies share similar results, concluding that well-engineered CLSC systems enhance economic sustainability [28].

Similarly, the internal and external returns positively affected performance [67]. Finally, the direct relationship between ROC and SFP was also accepted, and the findings concluded that companies investing in CLSC also invest in ROC to develop their systems based on the CE principles. The results match De Giovanni and Zaccour [10], who revealed that products purchased online are returned online and vice versa.

According to the first indirect hypothesis, CLSC intervenes in the relationship between BDA and SFA, and the analysis confirmed this association. The findings indicated that the firms implementing BDA also develop the capabilities and investments in developing CLSC that enhance the sustainable performance of the business. The results also align with Ma, Zhao and Ke [68] study. Based on the second indirect hypothesis, it is posited that CLSC plays a role in influencing the link between BDA and ROC. The study supports the idea that CLSC is a positive mediator in this relationship. According to the findings, implementing BDA facilitates strategic and tactical decision-making. The companies invest in the design of a dual CE system that includes CLSC and ROC. According to the last set of mediation hypothesis tests, ROC positively mediates the relationship between BDA and SFP. The results match a prior study that a firm's profits are linked with what customers prefer for reverse channels (Nageswaran, Cho and Scheller-Wolf, 2020). Consequently, it is necessary to invest in implementing BDA and developing a dual CE system based on CLSC and ROC. A system of this calibre assists the company in attaining competitive advantage and sustaining performance.

The moderation hypothesis examined the effect of PRK on the relationship between BDA and CLSC. The research shows that companies using BDA with low PRK have difficulty following CE rules, and the CLSC is very low. However, when the PRK is high among customers, implementing BDA increases CLSC. Therefore, PRK has a substantial and positive effect on the relationship. In addition, the influence of PRK between BDA and ROC was examined. The outcomes demonstrated that ROC options are minimal when BDA is implemented with awareness of PRK. Nevertheless, when the PRK is high, implementing BDA results increases the use of ROC. The results match the results of the study of Timoumi et al. [69].

6 Conclusions

This research analyzed the implications of BDA to a CE system developed on a dual system of CLSC and ROC and contributed to BDA in CE systems through the benefits organizations can gain to achieve SFP. Moreover, this study also bridged the gap by investigating the implementation of BDA in a CE context, discussing the potential implications of the influencing role of PRK. The results show that implementing BDA provided operational capabilities to the firm, further embedding with CLSC. Simultaneously, it also develops the service capabilities that ROC embeds. By implementing BDA, firms make more effective decisions regarding operational and service-related activities. The findings mention that the impact of BDA on SFP in terms of R-Square is higher than the effect size of CLSC and ROC. Still, firms cannot ignore the importance of CLSC and ROC in achieving sustainability goals.

6.1 Theoretical implications

This study, as indicated in the introduction, focuses on three domains. The initial objective is to investigate CE systems that consist of two major components: the CLSC and reverse omnichannel. Incorporating the dual system improves the theoretical basis of the field of CE, as its concurrent execution establishes a comprehensive framework for supervising CE initiatives effectively. The CLSC incorporates the fundamental and practical aspects of a Circular Economy (CE) system, encompassing the necessary operational functionalities for the collection, remanufacturing, recycling, and refurbishment processes [1,70]. Consequently, the CLSC addresses the industrial issues associated with CE.

Conversely, the reverse omnichannel approach combines the many elements of a Circular Economy system, with a specific emphasis on the intangible and service-related parts. This approach aims to provide consumers with effortless and cohesive experiences when returning things. The reverse omnichannel encompasses a range of capabilities associated with the collection of services. These capabilities can be pursued through several choices, including returning items to physical stores, online, to a centralized facility, and returning items directly to the manufacturer. Consequently, the reverse omnichannel concept addresses consumer concerns in the CE industry. It aims to increase the availability of resources to support operations within a closed-loop supply chain (CLSC) system. Incorporating service options offered to consumers via a reverse omnichannel into CE systems represents a significant theoretical advancement that has been the primary focus of the literature on the CLSC.

Furthermore, the implementation of Big Data Analytics (BDA) provides businesses with the capability to make informed decisions to identify, seize, and adapt to various opportunities. Furthermore, the role of the CLSC as an intermediary in the interaction between BDA and ROC is noteworthy due to its contribution to the existing literature on ROC. By examining the impact of PRK in a Circular Economy (CE) setting supported by big data analytics (BDA), the current methodology makes a significant contribution to the existing corpus of research. Hence, promoting PRK can guarantee the efficient execution of the BDA in a CE setting.

6.2 Managerial implications

The findings of this study emphasized the role of BDA and its subsequent influence on the dual CE system, comprising CLSC and ROC. The results also indicated that the impact of BDA on ROC is greater than its impact on CLSC. This study has explored the possibilities of addressing the issues associated with multiple options for reversing omnichannel, which are diverse and difficult to combine with external sources beyond the organization's control. On the contrary, CLSC is an internal matter to the organization, and implementing the BDA facilitates data collection and building connections based on the information to connect the online and offline systems. Therefore, managers focus on effectively utilizing BDA in designing effective CLSC systems for internal matters. Regarding external issues, the BDA provides information for comparing internal and external sources and builds strategies. The results also reveal that effective CLSC systems facilitate firms in implementing and strengthening their options for ROC.

Hence, managers should know that implementing the BDA enhances CLSC, which is the key to engaging the customer to rerun the products at the end of PLC using the options of ROC. Managers must develop the CLSC and ROC mechanisms to gain maximum benefits. However, managers must also concentrate on creating more customer knowledge about the return process and other options for the customer, as it all affects the SFP.

6.3 Limitations and future research

This research has several limitations. First, implementing BDA as a digital transformation mechanism in CE systems served as the basis for this study. Other available digital technologies offer different but unique benefits to CE systems. This study suggested studying CE in Industry 4.0 because the product return through CLSC can be used to develop other materials for 3D printers. Moreover, using artificial intelligence and the Internet of Things system [71] can also offer vital information about the ecosystem in CE. Second, to better understand the mechanism, the scholars could check the inverse relationships between CLSC and ROC using different digital technologies. Thirdly, it is a cross-sectional study, whereas future researchers could conduct longitudinal studies to gain a deeper understanding. In addition the current study utilized a non-representative sample by collecting data from diverse industries therefore future studies can replicate the study by collecting data from a specific industry to gain deep insights. Finally, other research methodologies, such as decision trees and logistics models, can be utilized by scholars to gather additional SFP-related data.

Data availability statement

Data will be made available on request.

Ethics statement

The ethics committee at the Air University Multan Campus, Pakistan reviewed and approved this study (case number: AU/EC/107).

CRediT authorship contribution statement

Syed Abdul Rehman Khan: Writing – review & editing, Writing – original draft, Visualization, Resources, Funding acquisition, Conceptualization. Muhammad Sohail Tahir: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Adnan Ahmed Sheikh: Writing – review & editing, Writing – original draft, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix

Appendix – 1 (Questionnaire)

Appendix – 1Constructs & Items	Source Citations	
Sustainable Firm Performance
We Achieve	(Hourneaux, Gabriel and Gallardo-Vázquez, 2018; [56,60])	
	SFP1: Profitable expansion through time		
	SFP2: Increase in market proportion over time		
	SFP3: Customer database expansion with time		
	SFP4: Resource efficiency over time		
	SFP5: Performance of the environment over Time		
	SFP6: Our contribution to the improvement of social welfare grows with time.		
Reverse Omni-Channel	The following are some reverse omnichannel solutions our organization has invested in over the past two years.	(Verhoef, Kannan and Inman, 2015)	
	ROC1: Return the product to the manufacturer		
	ROC2: Return to the store		
	ROC3: Return to an origin		
	ROC4: Return from the online setting		
	ROC5: Return to a storage facility.		
Closed-loop Supply Chain	Our firm has made the subsequent investments in its closed-loop supply chain over the past two years:	[28,45]	
	CLSC1: The implementation of recycling initiatives		
	CLSC2: Investing in the refurbishment and remanufacture of returned goods.		
	CLSC3: Improving the Information System's monitoring of returns		
	CLSC4: Using Third-Party Logistics Know-How to Acquire Returns		
	CLSC5: Offering replacement products to consumers		
	CLSC6: Offering affordable return options		
Product Return Knowledge	[41,61]	
	PRK1: The centre where the return product is received: Store		
	PRK2: The effects of the used product: Harm		
	PRK3: An adequate number of features are available for this Product Return		
	PRK4: I can easily find and choose a proper method to Dispose the product		
	PRK5: Support the idea of product Recycle		
	PRK6: Support the idea of product return Efforts		
Big Data Analytics	Our organization:	[55,57,72]	
Sensing	BDASn1: Monitors changes in the corporate environment, including internal and external.		
	BDASn2: Continuously interprets and processes information		
	BDASn3: Utilizes available opportunities to enhance the competitiveness of the organization		
Seizing	BDASz1: Utilizes cutting-edge auxiliary technologies		
	BDASz2: Utilizes sophisticated analytic techniques		
	BDASz3: Combining and integrating information from multiple data sources for decision-making		
Reconfiguring	BDARc1: Utilizes data visualization techniques routinely to aid users or decision-makers in highly volatile and complex environments in identifying new market opportunities.		
	BDARc2: Reduces risk by employing dashboards that provide data to facilitate root cause analysis and continuous improvement.		
	BDARc3: Deploys dashboard applications and data to our managers' communication devices (e.g., computers and mobile phones).		
	BDARc4: Renewal of business strategy founded on cutting-edge data techniques to preserve competitive advantage		

Acknowledgment

This research is supported by the 10.13039/501100001809 National Natural Science Foundation of China (72250410375 ) and the Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application (KF202207 ).
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