Information technology and people involvement in organizational performance through supply chain collaboration pdf

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Information technology and people involvement in organizational

performance through supply chain collaboration

Aamir Rashid and Rizwana Rasheed

Department of Business Administration, Iqra University, Karachi, Pakistan, and

Noor Aina Amirah

Faculty of Business and Management, Universiti Sultan Zainal Abidin, Gong Badak, Terengganu, Malaysia

Abstract

Purpose – Supply chain collaboration (SCC) beneﬁts organizational performance (OP). Although it is complex, it is also hard to implement and measure collaborative initiatives in theﬁeld of the supply chain.

This study aims to examine the role of information technology (IT) and people’s involvement in OP through the mediation of supply chain collaboration.

Design/methodology/approach–This study used a deductive and quantitative approach to test the research hypotheses. Data was collected from 249 supply chain professionals working at various manufacturingﬁrms.

Findings–This research found a significant effect of IT on supply chain collaboration. Similarly, people involvement (PI) also significantly influenced the supply chain collaboration. For the mediation hypotheses, SCC significantly mediates the relationships between two independent variables (IT and PI) and OP.

Furthermore, the full mediation of SCC occurred.

Originality/value–This study provides a framework emphasizing manufacturingfirms’practices, SCC and OP. The“people involvement”was found as a novel variable in the tested model using the resource-based view as a supporting theory. The researchfindings can benefit the professionals working on supply chain business processes to improve OP.

Keywords Information technology, People involvement, Collaboration, Business performance, Manufacturing, Supply chain performance

Paper typeResearch paper

1. Introduction

Nowadays, the relationships of supply chain collaboration (SCC) within the organization dominate the literature on supply chain management, which clariﬁes the requirement for forming close associations and relationships among the supply chain partners (Kumaret al., 2016;Anwer and Siddiqui, 2019). The literature suggests that organizations take advantage of collaborative initiatives (Liaoet al., 2017). Organizations that use collaborative practices and highly integrative strategies enhance corporate performance and increase ﬁrm competitiveness (Hudnurkar and Rathod, 2017). Procter and Gamble, Hewlett-Packard, Dell

Since acceptance of this article, the following author has updated their aﬃliation: Aamir Rashid is at Faculty of Business and Management, Universiti Sultan Zainal Abidin, Gong Badak, Terengganu, Malaysia.

Supply chain collaboration

Received 29 December 2022 Revised 3 April 2023 Accepted 31 May 2023

Journal of Science and Technology Policy Management

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/2053-4620.htm

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and IBM have built long-term collaborative linkages in upstream and downstream supply chains to reduce the transaction cost and gain a solid competitive position in the market (Rashid et al., 2022a, 2022b; Anwer and Siddiqui, 2019). Furthermore, information technology (IT) enables an organization to respond quickly to the changing market environment. It makes organizations understand the strategic mechanism of business for improving their process and recognizing opportunities for new business. IT also enhances the efﬁciency level of business to support appropriate business and fundamentally change the level of business performance (Pradabwong et al., 2017). Recognition of such relationships based on empirical evidence can provide a high level of understanding (Brito and Miguel, 2017;Pradabwonget al., 2017).

Anwer and Siddiqui (2019)stated that most linkages and relationships might act as a subject of risk because they fail to fulfil the requirements and expectations of their participants. Furthermore, the organizational performance (OP) could face operational, strategic and tactical issues. The strategic level of difficulties includes topics related to top management, governance issues, business and IT support process organization issues of thefirm. The tactic level of problems includes work challenges related to process and process modeling performance measurement. The operational level of issues has mechanical problems in the selection of business process management (BPM), the likeability of technology, formation of service- oriented architecture in a landscape of technology. When SCC plays its role in OP, the organizations face reluctance from employees to implement that technology and the boundaries and limitations of the technology itself. Thefirms must collaborate and integrate appropriately to combine resources that will significantly facilitate achieving an ace level. The implementation of collaboration can produce high-quality and cost-effective products of working alone in the market. It will also improve overall performance in the country (Hashmiet al., 2021a,2021b;Baloch and Rashid, 2022).

SCC initiatives are encouraged by the firms (Ali et al., 2023), which is inevitable without technological transformation. An erratic utilization of human capital and IT technology (Andrews, 2015; Feldstein, 2017) cause deviations in firm’s performance (Bloom and Van Reenen, 2010;Fosteret al., 2018). The use of technology, particularly in manufacturingfirms, brought enormous changes in the supply chain (Rakowski, 2015;

Xu et al., 2020). SCC is all about leveraging the ﬁrm resources, including human resources, information sharing, IT and joint decision-making (Acquah et al., 2021;

Fawcettet al., 2015;Naumanet al., 2022;Sihiteet al., 2022;Wu and Chiu, 2018); that help in creating the benefits for manufacturing (Simaet al., 2020). Existing literature in the domain of SCC is based on collaborative efforts of logistic service providers with the help of ICT (Wijewickrama et al., 2021), high level of information sharing and SC re- engineering (Cloutieret al., 2020). But the use of people involvement (PI) for the SCC is limited, particularly in the context of developing countries like Pakistan. Furthermore, the significance and importance of IT and SCC is a well-recognized in supply chain management. However, the role of PI along with IT in SCC is a phenomenon that is less researched in supply chain literature. This highlights the importance of conducting research in this domain. Additionally, the current economic situation of the country also emphasizes effective SCC (Al-Doori, 2019). Furthermore, the results of the research studies examining the SCC onfirm performance are mixed (Li and Huang, 2017), which further create the need for research on this topic. Thus, this particular study aims tofill the research gap byfinding out the impact of IT and PI on OP directly and through SCC.

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1.1 Research questions

Based on the problem statement, the following research questions will be empirically investigated in this research:

RQ1. To what extent does IT inﬂuence SCC?

RQ2. To what extent does PI inﬂuence SCC?

RQ3. To what extent does SCC inﬂuence OP?

RQ4. To what extent does SCC mediate the relationship between IT and OP?

RQ5. To what extent does SCC mediate the relationship between PI and OP?

2. Relevant theory

2.1 Resource-based view theory

The theory of resource-based view (RBV) claims thatﬁrms can achieve competitive beneﬁts by developing a set of resources and competencies/capabilities (Barney, 1991;Barneyet al., 2001;

Sirmonet al., 2011). High OP is based on the extent to which it holds properly organized valuable, exceptional, imperfectly imitable assets/resources (Barney et al., 2001; Hashmi et al., 2021a, 2021b). Resources can be in different forms, such as tangible resources that include human, technological and reputational assets and intangible resources that involve sharing information and knowledge. When an organization holds variety in its resources, its resources have considerable value (Grant, 1991;Sirmonet al., 2008). In contrast, the capabilities of organizations are a constant subset of ﬁrm resources which are not exchangeable but hold intentions to enhance the effectiveness and productivity level of otherﬁrm resources (Hashmiet al., 2021b).

Therefore, capabilities are considered an essential element for aﬁrm that also depends on certain conditions in which an organization performs.

Furthermore, the RBV theory identifies that only thefirm’s resources cannot help organizations achieve a competitive advantage.Sirmonet al.(2007)provided a high point to the executives or top management of afirm in creating the capabilities and developing the portfolio of resources by the following process, which includes acquiring, accumulating and divesting, whereas in other theoretical research studies the significance of top management decisions in the acquisition of resources and their deployment has been investigated. Previous studies also examine the role of managers or decision-makers in adequately organizing the resourcesfirm resources cannot help organizations (Chadwick et al., 2015). Few research studies examine the influence of resource combinations and capabilities onfirm performance (Brandon-Joneset al., 2014;Wuet al., 2006) argue that using capabilities may help organizations sustain competitive advantage.

Hashmiet al.(2021b)argued that for studying the performance implications in supply chain management, the RBV provides the appropriate theoretical framework. The pivotal role of RBV is in the capabilities and resources of aﬁrm. It differentiates on the bases of efﬁciency (Hashmiet al., 2021b). RBV can be implied to know the processes, procedures and other heterogeneous resources as these can transform the performance from short run to sustained strategic one. Such developments are already evident from RBV in the form of resource synergy along with IT, PI, SCC and OP (Hashmiet al., 2021a,2021b;Rashid and Rasheed, 2023). As discussed, the RBV sees the organizations as collections of assets;

however, the constraints to deal with resources; develop a perfect situation for collaboration toward OP. RBV suggested thatﬁrms are comprising resources and capabilities (Tho, 2018), which can help a ﬁrm to ensure its SCC (Acquah et al., 2021). Because human and IT

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capability are resources for aﬁrm, OP is dependent upon the relational capabilities that enable the SCC to enhance its performance (Albishriet al., 2020).

2.2 Literature review

2.2.1 Information technology and supply chain collaboration.IT plays a vital role in BPM (Mendlinget al., 2018). IT can be formed as a part of the design-implementation-analysis and business process improvement that is applied not only within theﬁrm but also in external IT interfaces and supply chain collaborations (Qrunﬂeh and Tarafdar, 2014;Salam, 2017).

For example, the ERP system can incorporate all the information related to the allocation of resources, regulation of activities and planning for various departments within the organization and its supply chain member (Powell, 2013). In linkages of supply chain collaboration, the open, balanced, multilevel and frequent communication between two supply chain partners is considered the main characteristic of collaborative relationships. These relationships offer trust-building, interdependence and mutual vision and build the foundation to share quality information for achieving mutual benefits for SC members. The collaboration built between a company and its partners through integration can enhance the efficiency of the flow of its products, services and information to achieve mutual benefits (Whitrlock, 2019;

Rong and Xu, 2020). Therefore, organizational technologies like communication-based applications, PCs, voice data and networks and laptops play an imperative role in sharing quality information among SC partners (Fartashet al., 2018;Wu and Chiu, 2018). The real-time supply chain information between partners based on the purchase order information, inventory levels, projected orders, information related to product design, demand forecasting and overall SC performance are a few examples. This information-sharing enables them to effectively plan upcoming purchases and levels of production and propagate collaboration (Pradabwonget al., 2017;Prajogoet al., 2007). The available literature of previous studies shows that a positive relationship and linkage exist between SCC and organizations’IT capability. The sharing of information and IT capability signiﬁcantly inﬂuence logistic integration.

2.2.2 People involvement and supply chain collaboration.In the BPM approach, the top management and authority over employees play a pivotal role. As it acts as an essential element of BPM. The commitment of top management to the empowerment of workers allows them to perform their duties efﬁciently and supports the concept of creativity. In an organization, the top management needs to communicate and commit with their workers to set organizational values and form a suitable style of leadership to enhance and improve the organisation’s performance (Pradabwonget al., 2017). Previous research shows a signiﬁcant relationship between PI and SCC (Nyagaet al., 2010;Trkmanet al., 2015). Furthermore, the higher level of employee involvement among supply chain partners, suppliers and customers offers better SCCs (Pradabwonget al., 2017).

2.2.3 Supply chain collaboration and organization performance.The SCC inﬂuences the performance level of an organization where supply chain members or partners collaborate and coordinate, conﬁrming the capability to respond to market dynamics (Singhet al., 2018).

To make strategic decisions relating to forecasting supply and demand, suppliers and their customers exchange quality information in a collaborative supply chain (Al-Doori, 2019).

The basic idea is that supply chain partners can achieve many beneﬁts by collaborating with other members of SC. The literature revealed that collaborative relationship between SC partners has a signiﬁcant impact on performance (Silva and Figueiredo, 2020;Wei, 2020;

Melander and Arvidsson, 2021; Rangelov et al., 2021), including mutual trust, practical innovation to attract their customers (Guptaet al., 2022;Kalkanciet al., 2019). Collaboration increases the accuracy of supply and demand forecasting, reduces the bullwhip effect, proﬁt enhancement and revenue increases, improves the level of responsiveness and a higher level

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of transparency in the supply chain and minimization of stock-outs (Anh et al., 2019;

Herczeget al., 2018;Nimmyet al., 2019;Panahifaret al., 2015;Singhet al., 2018). Collaboration schemes effectively facilitate collaborative planning, forecasting and replenishment and vendor- managed inventory that are being followed globally. Researchers also believed that supply chain members who followed SCC effectively could achieve considerable performance in their operations (Olorunniwo and Li, 2010;Simatupang and Sridharan, 2005;Jug (2020)suggested that to achieve sustainable development, ﬁrms should develop effective partnerships with other entities.

Previous studies have discussed the relationship between SCC and OP (Cao and Zhang, 2011;Pokuaa-Duah and Nadarajah, 2020;Wuet al., 2014). Thefirm’s performance requires fulfilling and achieving market-oriented as well asfinancial goals compared with rivalfirms (Chen, 2018;Zaridiset al., 2020). Therefore, the literature suggests that the SCC significantly influencesfirm performance.Figure 1illustrates the conceptual framework for this research.

2.3. Research hypotheses

Based on the underpinning theory, literature review and conceptual model of this research, the following research hypotheses were derived to further explore the effects of independent variables (IVs) on dependent variables (DVs) through the mediator:

H1. IT signiﬁcantly inﬂuences SCC.

H2. People’s Involvement signiﬁcantly inﬂuences SCC.

H3. SCC signiﬁcantly inﬂuences Firm Performance.

H4. SCC signiﬁcantly mediates the relationship between IT and OP.

H5. SCC signiﬁcantly mediates the relationship between PI and OP.

3. Methodology

This section of the research comprises information and type of sources, which identify the variables’relationship and guides for data analysis (Cooper and Schindler, 2011). Therefore, a research design must describe and ﬁnd answers to research questions. According to Hashmi et al. (2021b), a researcher should consider a criterion before selecting an appropriate research design, such as time, effort, research objectives, research questions, accuracy, validity, economical procedures, methods, the nature of respondents and type of required information due to the involvement of great deal of time and money in academic research. Generically, qualitative and quantitative research methods are the two types.

However, this study adopted a quantitative research design to test the study hypotheses

Figure 1.

Conceptual framework

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(Hashmi and Tawﬁq, 2020). Quantitative research is an empirical investigation using computational, mathematical or statistical techniques intending to develop and employ hypotheses, theories and mathematical models (Khan et al., 2021). Therefore, the quantitative method is a consistent, quantiﬁable and procedural method to testify, establish and validate the hypothesis. Also, the quantitative method is the most acceptable method due to its applicability to some larger populations and the capability to deal with a larger number of samples (Rashidet al., 2022a,2022b).

Furthermore, data was collected using a survey method through a structured questionnaire from supply chain professionals working at manufacturingﬁrms in Karachi (Hashmiet al., 2021a). Furthermore, a convenient sampling on a sample of 249 respondents was carried out by calculating the G* power software (Saunders, 2011;Hairet al., 2010). G*

power software calculated the sample size based on several predictors of the statistical model (Faulet al., 2009). The instrument given inAppendixwas adopted from previous literature where three items for the construct “Information Technology” were borrowed from Rashid (2016). However, “People Involvement” ﬁve items and “Supply Chain Collaboration”six items borrowed fromAnwer and Siddiqui (2019). Finally,“Organizational Performance”took four items fromHashmiet al.(2020a)andHashmiet al.(2021b). The used constructs were measured on aﬁve-point Likert scale (1¼strongly disagree through 5¼ strongly agree) (Khanet al., 2022a;Rashidet al., 2020). As the items were adopted from previous studies. Therefore, to verify the context, the questionnaires were validated by three industrial experts and one academic subject specialist. Besides, the reliability test was performed to test the consistency of the instrument. The reliability results are presented in Table 1.

3.1 Data analysis

Before conducting a full-scale study, pilot testing was carried out on 30 respondents to confirm that the participants did not come across any issues about adopted questionnaire’s format, design or wording (Rashidet al., 2021;Rasheedet al., 2023). Pilot testing confirms whether the research expands smoothly (Rashid and Rasheed, 2023). All the items fulfilled the requirements and were retained for full-scale analysis.

Later, as suggested byDeSimoneet al.(2015), a four-step approach was followed for data screening and cleaning, i.e. (1) missing value analysis, (2) out-of-range values, (3) multivariate outliers and (4) univariate outliers. Subsequently, frequency measures among

Table 1.

Descriptive statistics, reliability and bivariate correlation test results

Construct Mean SD a ^Sk ^Kr

IT 3.24 0.87 0.72 –0.41 –0.49

PI 3.45 0.74 0.68 –0.40 –0.11

SCC 3.54 0.61 0.70 –0.29 0.04

OP 3.59 0.80 0.72 –0.58 –0.05

Bivariate correlation test results

Construct IT PI SCC OP

IT 1.00

PI 0.48 1.00

SCC 0.46 0.49 1.00

OP 0.39 0.44 0.46 1.00

Notes:SD = standard deviation;a¼Cronbach’s alpha; Sk¼Skewness, Kr¼Kurtosis Source:SPSS output

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the dataset of 249 responses found no missing or out-of-range value. Then, the Z-Score analysis was conducted to identify univariate outliers and found zero univariate outliers in the data set. According toTabachnick and Fidell (2007), Z-Score must be betweenþ3.29 and–3.29.

Finally, Mahalanobis distance (D2) for multivariate outliers based on the suggested value of D2<0.001 was used that found no multivariate outliers in the dataset (Tabachnick and Fidell, 2007); therefore, all the 249 responses were brought forward for data analysis.

Furthermore, this study carried out the normality of data followed by descriptive statistics, reliability analysis, bivariate correlation and an overall statistical model to test the hypothesis.

Table 1shows the skewness and kurtosis values within the acceptable range of63 (Hairet al., 2010). The highest skewness value, 0.58, is for construct OP with a Mean of 3.59 and Std. Dev. 0.80, whereas the most negligible value of skewness, 0.29, is for construct SCC (Mean 3.54, Std. Dev.

0.61). The highest kurtosis value of 0.49 is for construct IT (Mean 3.24, Std. Dev. 0.87). However, the minimum value of kurtosis 0.05 is for construct OP. All results are in an acceptable range of univariate normality (Hairet al., 2010). Furthermore, the reliability test found a good Cronbach’s alpha value (>0.60) for all constructs (Haqueet al., 2021;Khanet al., 2022b;Rasheed and Rashid, 2023;Ursachi et al., 2015). Table 1 also expresses the correlation results that fulﬁll the test assumptions and are in the range of60.30 to60.90 (Rashid, 2016).

3.2 Respondents’proﬁle

Before hypothesis testing, the demographic proﬁles were analyzed; where a distant smaller proportion of ﬁrm size with employees 500–1,000 (35, 14%) than 100–250 employees (159, 64%), and employees size of 250–500 (55, 22%) from the manufacturing industry. The respondents participated from Cement/Steel (25, 10%), Automobile (27, 10.8%), Food and Beverages (29, 11.6%), Pharmaceutical (30, 12%), Textile (32, 12.9%), FMCG (38, 15.3%), Chemical/Plastic (40, 16.1%) and others (28, 11.2%). Similarly, a smaller proportion of Deputy managers (59, 23.7%), Senior managers (59, 23.7%), Managers (61, 24.3%) and Assistant managers (70, 28.1%) participated in the survey. Finally, the experience level of employees in ascending order of numbers ranged from 74 (29.7%) with more than 10 years, 38 (15.3%) with 6–10 years, with 4–5 years 56 (22.5%) and with less than four years 81 (32.5%).

3.3 Hypothesis testing (H1, H2 and H3)

IBM SPSS version 24, as a statistical tool, was used to analyze the study hypotheses through regression analysis. The results are given inTable 2.

H1: IT significantly influences SCC: The summarized results shown inTable 2indicate that the Adjusted R-square (0.205) of IT can measure 20.5% variance in SCC, whereas the possibility of error in the model is 0.54310. Furthermore, the ANOVA and coefficient (std.

beta coefficient) results illustrate that IT significantly influences SCC (p<0.05) (Daset al.,

Table 2.

Regression results (model summary, ANOVA and coefﬁcients) Hypotheses

Model summary ANOVA Coefficients

R Adj. R Sqr.

Std error

of estimate F p

Standardized

beta coefficient t p

H1: IT!SCC 0.457 0.205 0.54310 59.652 0.000 0.457 7.724 0.000

H2: PI!SCC 0.491 0.238 0.53184 71.881 0.000 0.491 8.478 0.000

H3: SCC!OP 0.461 0.209 0.71298 61.060 0.000 0.461 7.814 0.000

Notes:SD = standard deviation; Adj. R Sqr.¼adjusted R square;p¼signiﬁcant at level 0.05 (1 Tail) Source:SPSS output

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2021). The value of the beta coefﬁcient expresses that if IT increases by a single unit, SCC will increase positively by 0.457 units. Thus, hypothesisH1is supported.

H2: PI significantly influences SCC: The adjusted R square value (0.238) in Table 2 expresses that the predictor PI can measure 23.8% variance in SCC, whereas the possibility of error in this calculated model is 0.53184. The ANOVA and coefficient (std. beta coefficient) result illustrate the significant influence of PI on SCC (p < 0.05). The beta coefficient expresses that if PI increases by a single unit, then SCC will increase by 0.491 units. Thus, hypothesisH2is supported.

H3: SCC significantly influences OP: The adjusted R square (0.238) inTable 2expresses that the predictor SCC can measure 20.9% variance in OP, whereas the possibility of error in this calculated model is 0.71298. The ANOVA and coefficient (std. beta coefficient) results illustrate the significant influence of SCC on OP (p<0.05) (Hashmi and Tawfiq, 2020). The beta coefficient expresses that if SCC increases by a single unit, then OP will increase by 0.461 units. Thus, hypothesisH3is supported.

3.4 Analysis of mediation (H4 and H5)

Table 3illustrates the results of the overall model with mediation. The IV of this model was IT and PI. SCC was a mediator (M), whereas OP was a DV. The mediation analysis was performed through Preacher and Hayes’s (2013) suggested method. The results inTable 2 show the effect of the independent variable on the mediator, which is denoted by (a), and then the impact of the mediator (M) on the outcome variable, indicated by (b). After these effects, there is also direct and indirect impact denoted by (c⁰) and (ab), respectively, and the total effect (c).

Table 3indicates the effect of IT and PI on OP through the mediation of SCC. The results satisfy the test assumptions of mediation analysis as thep-values for direct effects, and the mediator are signiﬁcant (<0.05). Furthermore, the direct effect(c⁰)for IT and PI on OP is 0.210 and 0.313, respectively. The results indicate that the increased IT and PI (separately) will increase the organization’s performance. However, IT and PI (separately) have an indirect effect(ab)on OP through the mediation of SCC is 0.151 and 0.172, respectively.

Finally, the total effect(c)between IT and OP is 0.361 and 0.485 is for PI and OP. Moreover, it was also evident that the relationship between the variables was signiﬁcant after entering the SCC as a mediator. Hence, hypothesesH4andH5are supported.

4. Discussion

The study hypotheses were supported and found the signiﬁcant inﬂuence of IT and PI on OP through the mediation of SCC. More comprehensively, IT plays an immense and vital role in enhancing SCC and OP. For example, the ERP system can incorporate all the information related to the distribution of resources, regulation of activities and planning for multiple departments within the organization and its supply chain member (Powell, 2013).

Theﬁndings ofRashid (2016)andRashidet al.(2022a,2022b) support the results of this

Table 3.

Mediation analysis

Hypotheses IV M DV IV!M(a) M!DV(b) DE(c⁰) IE(ab) TE(c)

H4 IT SCC OP 0.322 0.471 0.210 0.151 0.361

H5 PI SCC OP 0.407 0.422 0.313 0.172 0.485

Notes:N¼249; *signiﬁcant level =p<0.05; DE = direct effect; IE = indirect effect; TE = total effect Source:SPSS Output

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study. Furthermore, in an organization, procurement portals also allow organizations to exchange information related to production and forecasting, resulting in an adequate level of coordination in operational activities (Pradabwonget al., 2017;Prajogoet al., 2007).

Moreover, the findings suggested that the higher level of PI brings synchronized collaboration among supply chain partners, suppliers and customers. Therefore, the top management to communicate and show commitment to set organizational values and form a suitable style of leadership to enhance and improve the performance of an organization (Pradabwonget al., 2017). Moreover, it is also evident that to make strategic decision that relates to forecasting supply and demand in collaborative supply chains, the suppliers and their customers exchange quality information among them (Al-Doori, 2019). Thefindings are supported by the results ofAnwer and Siddiqui (2019). Researchers also believe that those supply chain members who follow collaborative practices effectively can achieve multiple performance levels. Thefindings are consistent with the previous researchfindings ofOlorunniwo and Li (2010),Cao and Zhang (2011),Pokuaa-Duah and Nadarajah (2020),Wu et al.(2014),Pradabwonget al.(2015),Rashid (2016),Mendlinget al.(2018),Hashmiet al.

(2020a)andHashmiet al.(2021b).

5. Research implications

The study contributed to RBV by using IT, PI, OP and SCC as a mediator. The study emphasized that organizations working in a collaborative environment can attain more beneﬁts compared toﬁrms operating in isolation. In other words, by adopting supply chain collaboration, better OP can be achieved (Soosay and Hyland, 2015). Moreover, this study comprehensively extended an integrated model to understand the essential elements of BPM that include IT, PI, SCC and OP. Previous studies suggested that the PI was not examined with the SCC in supply chain literature.

This study also enhances the understanding of managers and decision-makers by affirming the induction of external supply chain partners to improve OP. Likewise, this study contributes to the managers’ knowledge by highlighting the multidimensional landscape of BPM practices and SCC in attaining collaborative advantage. Thefindings of this study can provide a guide to managers to evaluate their business processes for better OP. Thus, practitioners can adopt this model to enhance inter-organizational collaboration, ultimately bringing sustainable growth to thefirm. The research also highlighted thatfirms could be more efficient by using collaboration (like early supplier involvement) in critical decisions that will be prompted by reducing the complexity of logistics. As the collaboration can provide a more transparent supply chain, the actors can build trust by deciding how the performance will be managed. Firms can also save money by eliminating the traditional methods via intermediaries, vendors, third parties, etc., as these processes can efficiently be carried out through collaboration, people’s involvement and IT.

6. Limitations and recommendations

This study focused only on manufacturing ﬁrms located in Karachi, whereas it could consider other geographical locations. Furthermore, this study only evaluated two dimensions to measure BPM, i.e. IT and people’s involvement and can also be done in the service sector. Due to the COVID-19 situation and its associated restrictions, the availability of respondents was limited. To cope with the mentioned limitations, it is recommended that future researchers consider other sectors; more process-oriented dimensions could also be added to the study model with a different sample size to get more accurate results, and the model could be tested by applying structural equational modeling. Likewise, IT can be

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examined with the conjecture of blockchain as well as big-data analytics to improve the SCC leading toward supply chain integration for OP.

7. Conclusion

The objective of this study was to develop the role of IT and people’s involvement through SCC and to know how it affects the organization’s performance. This study was based on the theory of RBV, whereas the model was measured by IT and PI. This study was quantitative, and a structured questionnaire was developed by adapting constructs from existing previous studies. Moreover, the data was collected from 249 supply chain professionals working at manufacturing firms in Karachi, Pakistan. The regression and mediation analysis was used to test the research hypotheses, and the results found that the IT and PI significantly influenced SCC and the OP. Furthermore, it was also found that SCC has a significant mediating effect on OP. This study concluded that manufacturingfirms could perform well by adopting BPM practices and SCC activities in their operations.

References

Acquah, I.S.K., Naude, M.J. and Sendra-García, J. (2021),“Supply chain collaboration in the petroleum sector of an emerging economy: comparing results from symmetrical and asymmetrical approaches”,Technological Forecasting and Social Change, Vol. 166, p. 120568, doi:10.1016/j.

techfore.2020.120568.

Albishri, D.Y., Sundarakani, B. and Gomisek, B. (2020),“An empirical study of relationships between goal alignment, centralised decision-making, commitment to networking and supply chain effectiveness using structural equation modelling”,International Journal of Logistics Research and Applications, Vol. 23 No. 4, pp. 390-415, doi:10.1080/13675567.2019.1700219.

Al-Doori, J.A. (2019),“The impact of supply chain collaboration on performance in automotive industry:

empirical evidence”, Journal of Industrial Engineering and Management, Vol. 12 No. 2, pp. 241-253, doi:10.3926/jiem.2835.

Ali, E., Jianhua, L., Rasheed, M. and Siraj, A. (2023),“Measuring the impact of integration practices on ﬁrms’supply chain performance: role of organizational antecedents in this relationship”,Arab Gulf Journal of Scientiﬁc Research, Vol. 41 No. 3, doi:10.1108/AGJSR-10-2022-0232.

Andrews, D. (2015),“The circular economy, design thinking and education for sustainability”,Local Economy: The Journal of the Local Economy Policy Unit, Vol. 30 No. 3, pp. 305-315, doi:10.1177/

0269094215578226.

Anh, N.T.M., Hui, L., Khoa, V.D. and Mehmood, S. (2019), “Relational capital and supply chain collaboration for radical and incremental innovation”,Asia Paciﬁc Journal of Marketing and Logistics, Vol. 31 No. 4, pp. 1076-1094, doi:10.1108/APJML-10-2018-0423.

Anwer, S. and Siddiqui, D.A. (2019),“Business process management organizational performance and competitiveness: the mediatory role of supply chain collaboration”, SSRN 3510631, doi:10.2139/

ssrn.3510631.

Baloch, N. and Rashid, A. (2022),“Supply chain networks, complexity, and optimization in developing economies: a systematic literature review and Meta-Analysis”, South Asian Journal of Operations and Logistics, Vol. 1 No. 1, pp. 1-13, doi:10.57044/SAJOL.2022.1.1.2202.

Barney, J. (1991),“Firm resources and sustained competitive advantage”,Journal of Management, Vol. 17 No. 1, pp. 99-120, doi:10.1177/014920639101700108.

Barney, J., Wright, M. and Ketchen, D.J. Jr (2001),“The resource-based view of theﬁrm: ten years after 1991”,Journal of Management, Vol. 27 No. 6, pp. 625-641, doi:10.1177/014920630102700601.

Bloom, N. and Van Reenen, J. (2010), “Why do management practices differ across ﬁrms and countries?”,Journal of Economic Perspectives, Vol. 24 No. 1, pp. 203-224, doi:10.1257/jep.24.1.203.

JSTPM

(11)

Brandon-Jones, E., Squire, B., Autry, C.W. and Petersen, K.J. (2014),“A contingent resource-based perspective of supply chain resilience and robustness”,Journal of Supply Chain Management, Vol. 50 No. 3, pp. 55-73, doi:10.1111/jscm.12050.

Brito, R.P. and Miguel, P.L. (2017),“Power, governance, and value in collaboration: differences between buyer and supplier perspectives”,Journal of Supply Chain Management, Vol. 53 No. 2, pp. 61-87, doi:10.1111/jscm.12134.

Cao, M. and Zhang, Q. (2011),“Supply chain collaboration: impact on collaborative advantage andﬁrm performance”,Journal of Operations Management, Vol. 29 No. 3, pp. 163-180, doi:10.1016/j.

jom.2010.12.008.

Chadwick, C., Super, J.F. and Kwon, K. (2015),“Resource orchestration in practice: CEO emphasis on SHRM, commitment-based HR systems, andﬁrm performance”,Strategic Management Journal, Vol. 36 No. 3, pp. 360-376, doi:10.1002/smj.2217.

Chen, H.L. (2018), “Supply chain risk’s impact on corporateﬁnancial performance”,International Journal of Operations and Production Management, Vol. 38 No. 3, pp. 713-731, doi:10.1108/

IJOPM-02-2016-0060.

Cloutier, C., Oktaei, P. and Lehoux, N. (2020),“Collaborative mechanisms for sustainability-oriented supply chain initiatives: state of the art, role assessment and research opportunities”, International Journal of Production Research, Vol. 58 No. 19, pp. 5836-5850, doi: 10.1080/

00207543.2019.1660821.

Cooper, D.R. and Schindler, P.S. (2011),Business Research Methods, 11th ed., McGraw-Hill, New York, NY.

Das, S., Ghani, M., Rashid, A., Rasheed, R., Manthar, S. and Ahmed, S. (2021), “How customer satisfaction and loyalty can be affected by employee’s perceived emotional competence: the mediating role of rapport”,International Journal of Management, Vol. 12 No. 3, pp. 1268-1277, doi:10.34218/IJM.12.3.2021.119.

DeSimone, J.A., Harms, P.D. and DeSimone, A.J. (2015),“Best practice recommendations for data screening”,Journal of Organizational Behavior, Vol. 36 No. 2, pp. 171-181.

Fartash, K., Davoudi, S., Baklashova, T., Svechnikova, N., Nikolaeva, Y., Grimalskaya, S. and Beloborodova, A. (2018),“The impact of technology acquisition and exploitation on organizational innovation and organizational performance in knowledge-intensive organizations”, EURASIA Journal of Mathematics, Science and Technology Education, Vol. 14 No. 4, pp. 1497-1507, doi:

10.29333/ejmste/84835.

Faul, F., Erdfelder, E., Buchner, A. and Lang, A.-G. (2009),“Statistical power analyses using G*power 3.1: tests for correlation and regression analyses”,Behavior Research Methods, Vol. 41 No. 4, pp. 1149-1160, doi:10.3758/BRM.41.4.1149.

Fawcett, S.E., McCarter, M.W., Fawcett, A.M., Webb, G.S. and Magnan, G.M. (2015),“Why supply chain collaboration fails: the socio-structural view of resistance to relational strategies”,Supply Chain Management: An International Journal, Vol. 20 No. 6, pp. 648-663, doi:10.1108/SCM-08-2015- 0331.

Foster, N.E., Anema, J.R., Cherkin, D., Chou, R., Cohen, S.P., Gross, D.P.,. . . and Woolf, A. (2018),

“Prevention and treatment of low back pain: evidence, challenges, and promising directions”, The Lancet, Vol. 391 No. 10137, pp. 2368-2383, doi:10.1016/S0140-6736(18)30489-6.

Grant, R.M. (1991),“The resource-based theory of competitive advantage: implications for strategy formulation”,California Management Review, Vol. 33 No. 3, pp. 114-135, doi:10.2307/41166664.

Gupta, A., Singh, R.K. and Mangla, S.K. (2022),“Evaluation of logistics providers for sustainable service quality: analytics based decision making framework”,Annals of Operations Research, Vol. 315 No. 2, pp. 1617-1664, doi:10.1007/s10479-020-03913-0.

Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2010),”SEM: an introduction”, Multivariate Data Analysis: A Global Perspective, Vol. 5 No. 6, pp. 629-686.

Supply chain

collaboration

(12)

Haque, I., Rashid, A. and Ahmed, S.Z. (2021),“The role of automobile sector in global business: case of Pakistan”,Pakistan Journal of International Affairs, Vol. 4 No. 2, pp. 363-383, doi:10.52337/pjia.

v4i2.195.

Hashmi, A.R., Amirah, N.A. and Yusof, Y. (2020a),“Mediating effect of integrated systems on the relationship between supply chain management practices and public healthcare performance:

structural equation modeling”,International Journal of Management and Sustainability, Vol. 9 No. 3, pp. 148-160, doi:10.18488/journal.11.2020.93.148.160.

Hashmi, A.R., Amirah, N.A. and Yusof, Y. (2021a),“Organizational performance with disruptive factors and inventory control as a mediator in public healthcare of Punjab, Pakistan”,Management Science Letters, Vol. 11 No. 1, pp. 77-86, doi:10.5267/j.msl.2020.8.028.

Hashmi, A.R., Amirah, N.A., Yusof, Y. and Zaliha, T.N. (2020b),“Exploring the dimensions using exploratory factor analysis of disruptive factors and inventory control”,The Economics and Finance Letters, Vol. 7 No. 2, pp. 247-254, doi:10.18488/journal.29.2020.72.247.254.

Hashmi, A.R., Amirah, N.A., Yusof, Y. and Zaliha, T.N. (2021b),“Mediation of inventory control practices in proﬁciency and organizational performance: state-funded hospital perspective”, Uncertain Supply Chain Management, Vol. 9, pp. 89-98, doi:10.5267/j.uscm.2020.11.006.

Hashmi, A.R. and Tawﬁq, A.M. (2020),“The effect of disruptive factors on inventory control as a mediator and organizational performance in health department of Punjab, Pakistan”, International Journal of Sustainable Development and World Policy, Vol. 9 No. 2, pp. 122-134, doi:

10.18488/journal.26.2020.92.122.134.

Herczeg, G., Akkerman, R. and Hauschild, M.Z. (2018),“Supply chain collaboration in industrial symbiosis networks”,Journal of Cleaner Production, Vol. 171, pp. 1058-1067, doi: 10.1016/j.

jclepro.2017.10.046.

Hudnurkar, M. and Rathod, U. (2017),“Collaborative practices with suppliers in Indian manufacturing multinationals”,Journal of Global Operations and Strategic Sourcing, Vol. 10 No. 2, pp. 206-231, doi:10.1108/JGOSS-07-2016-0022.

Jug, A. (2020),“Why should corporations partner with social enterprises and entrepreneurs?”,IEEE Engineering Management Review., Vol. 48 No. 2, doi:10.1109/EMR.2020.2976917.

Kalkanci, B., Rahmani, M. and Toktay, L.B. (2019),“The role of inclusive innovation in promoting social sustainability”,Production and Operations Management, Vol. 28 No. 12, pp. 2960-2982, doi:10.1111/poms.13112.

Khan, S.K., Ahmed, S. and Rashid, A. (2021),“Inﬂuence of social media on purchase intention and customer loyalty of generation Y with the mediating effect of conviction: a case of Pakistan”, Pakistan Journal of International Affairs, Vol. 4 No. 2, pp. 526-548, doi:10.52337/pjia.v4i2.207.

Khan, S., Rashid, A., Rasheed, R. and Amirah, N.A. (2022a),“Designing a knowledge-based system (KBS) to study consumer purchase intention: the impact of digital inﬂuencers in Pakistan”, Kybernetes, doi:10.1108/K-06-2021-0497.

Khan, S., Rasheed, R., Rashid, A., Abbas, Q. and Mahboob, F. (2022b),“The effect of demographic characteristics on job performance: an empirical study from Pakistan”,Journal of Asian Finance, Economics and Business, Vol. 9 No. 2, pp. 283-294, doi:10.13106/jafeb.2022.vol9.no2.0283.

Kumar, G., Banerjee, R.N., Meena, P.L. and Ganguly, K. (2016),“Collaborative culture and relationship strength roles in collaborative relationships: a supply chain perspective”,Journal of Business and Industrial Marketing, Vol. 31 No. 5, pp. 587-599, doi:10.1108/JBIM-12-2014-0254.

Li, Y.H. and Huang, J.W. (2017),“The moderating role of relational bonding in green supply chain practices and performance”,Journal of Purchasing and Supply Management, Vol. 23 No. 4, pp. 290-299, doi:10.1016/j.pursup.2017.06.001.

Liao, S.-H., Hu, D.-C. and Ding, L.-W. (2017),“Assessing the inﬂuence of supply chain collaboration value innovation, supply chain capability and competitive advantage in Taiwan’s networking communication industry”,International Journal of Production Economics, Vol. 191, pp. 143-153, doi:10.1016/j.ijpe.2017.06.001.

JSTPM

(13)

Melander, L. and Arvidsson, A. (2021),“Introducing sharing-focused business models in the B2B context: comparing interaction and environmental sustainability for selling, renting and sharing on industrial markets”, Journal of Business and Industrial Marketing, Vol. 36 No. 10, pp. 1864-1875, doi:10.1108/JBIM-01-2020-0032.

Mendling, J., Weber, I., Aalst, W.V.D., Brocke, J.V., Cabanillas, C., Daniel, F., Debois, S., Ciccio, C.D., Dumas, M. and Dustdar, S. (2018),“Blockchains for business process management-challenges and opportunities”,ACM Transactions on Management Information Systems, Vol. 9 No. 1, pp. 1-16, doi:10.1145/3183367.

Nauman, S., Bhatti, S.H., Imam, H. and Khan, M.S. (2022),“How servant leadership drives project team performance through collaborative culture and knowledge sharing”, Project Management Journal, Vol. 53 No. 1, pp. 17-32, doi:10.1177/87569728211037777.

Nimmy, J., Chilkapure, A. and Pillai, V.M. (2019),“Literature review on supply chain collaboration:

comparison of various collaborative techniques”,Journal of Advances in Management Research, Vol. 16 No. 4, pp. 537-562, doi:10.1108/JAMR-10-2018-0087.

Nyaga, G.N., Whipple, J.M. and Lynch, D.F. (2010),“Examining supply chain relationships: do buyer and supplier perspectives on collaborative relationships differ?”, Journal of Operations Management, Vol. 28 No. 2, pp. 101-114, doi:10.1016/j.jom.2009.07.005.

Olorunniwo, F.O. and Li, X. (2010), “Information sharing and collaboration practices in reverse logistics”,Supply Chain Management: An International Journal, Vol. 15 No. 6, pp. 454-462, doi:

10.1108/13598541011080437.

Panahifar, F., Heavey, C., Byrne, P.J. and Fazlollahtabar, H. (2015),“A framework for collaborative planning, forecasting and replenishment (CPFR)”, Journal of Enterprise Information Management, Vol. 28 No. 6, pp. 838-871, doi:10.1108/JEIM-09-2014-0092.

Pokuaa-Duah, G. and Nadarajah, D. (2020),“Value stream mapping in enhancing operational performance of Ghanaian food ProcessingIndustry: mediating role of supply chain collaboration”,International Journal of Business and Social Science, Vol. 11 No. 4, doi:10.30845/ijbss.v11n4a6.

Powell, D. (2013),“ERP systems in lean production: new insights from a review of lean and ERP literature”,International Journal of Operations and Production Management, Vol. 33 Nos 11/12, pp. 1490-1510, doi:10.1108/IJOPM-07-2010-0195.

Pradabwong, J., Braziotis, C., Pawar, K.S. and Tannock, J. (2015),“Business process management and supply chain collaboration: a critical comparison”,Logistics Research, Vol. 8 No. 1, p. 6, doi:

10.1007/s12159-015-0123-6.

Pradabwong, J., Braziotis, C., Tannock, J.D. and Pawar, K.S. (2017),“Business process management and supply chain collaboration: effects on performance and competitiveness”, Developments in Logistics and Supply Chain Management, pp. 138-150, doi:10.1057/9781137541253_13.

Prajogo, D.I., Laosirihongthong, T., Sohal, A. and Boon-itt, S. (2007),“Manufacturing strategies and innovation performance in newly zindustrialized countries”,Industrial Management and Data Systems, Vol. 107 No. 1, pp. 52-68, doi:10.1108/02635570710719052.

Qrunﬂeh, S. and Tarafdar, M. (2014),“Supply chain information systems strategy: impacts on supply chain performance and ﬁrm performance”, International Journal of Production Economics, Vol. 147, pp. 340-350, doi:10.1016/j.ijpe.2012.09.018.

Rakowski, E. (2015),“Tech trends 2016”, Part II: The supply chain goes digital, Procurement Leaders.

Rangelov, M., Dylla, H., Mukherjee, A. and Sivaneswaran, N. (2021),“Use of environmental product declarations (EPDs) of pavement materials in the United States of America (USA) to ensure environmental impact reductions”, Journal of Cleaner Production., Vol. 283, doi: 10.1016/j.

jclepro.2020.124619.

Rasheed, R., Rashid, A., Amirah, N.A. and Afthanorhan, A. (2023),“Quantifying the moderating effect of servant leadership between occupational stress and employee in-Role and Extra-Role performance”,Quality–Access to Success.

Supply chain

collaboration

(14)

Rasheed, R. and Rashid, R. (2023),“Role of service quality factors in word of mouth through student satisfaction”,Kybernetes, doi:10.1108/K-01-2023-0119.

Rashid, A. (2016),“Impact of inventory management in downstream chains on customer satisfaction at manufacturingﬁrms”,International Journal of Management, IT and Engineering, Vol. 6 No. 6, pp. 1-19.

Rashid, A., Ali, S.B., Rasheed, R., Amirah, N.A. and Ngah, A.H. (2022a),“A paradigm of blockchain and supply chain performance: a mediated model using structural equation modeling”,Kybernetes, doi:10.1108/K-04-2022-0543.

Rashid, A., Amirah, N.A., Yusof, Y. and Mohd, T.A. (2020),“Analysis of demographic factors on perceptions of inventory managers towards healthcare performance”, The Economics and Finance Letters, Vol. 7 No. 2, pp. 289-294, doi:10.18488/journal.29.2020.72.289.294.

Rashid, A. and Rasheed, R. (2023),“Mediation of inventory management in the relationship between knowledge andﬁrm performance”,SAGE Open, Vol. 13 No. 2, doi:10.1177/21582440231164593.

Rashid, A., Rasheed, R., Amirah, N.A. and Afthanorhan, A. (2022b),“Disruptive factors and customer satisfaction at chain stores in Karachi, Pakistan”,Journal of Distribution Science, Vol. 20 No. 10, pp. 93-103, doi:10.15722/jds.20.10.202210.93.

Rashid, A., Rasheed, R., Amirah, N.A., Yusof, Y., Khan, S. and Agha, A.A. (2021),“A quantitative perspective of systematic research: Easy and step-by-step initial guidelines”,Turkish Online Journal of Qualitative Inquiry, Vol. 12 No. 9, pp. 2874-2883.

Rong, L. and Xu, M. (2020), “Impact of revenue-sharing contracts on green supply chain in manufacturing industry”, International Journal of Sustainable Engineering, Vol. 13 No. 4, pp. 316-326, doi:10.1080/19397038.2019.1709105.

Salam, M.A. (2017),“The mediating role of supply chain collaboration on the relationship between technology, trust and operational performance”, Benchmarking: An International Journal, Vol. 24 No. 2, pp. 298-317, doi:10.1108/BIJ-07-2015-0075.

Saunders, M.N. (2011),Research Methods for Business Students, Pearson Education, India.

Sihite, O.B., Poltak, S., Hidayat, D. and Sijabat, R. (2022),“The nexus between academic culture and collaborative culture as an academic collaborative culture for effective teaching in higher education”,Jurnal Pendidikan Bisnis Dan Manajemen, Vol. 8 No. 3, pp. 128-140.

Silva, M.E. and Figueiredo, M.D. (2020),“Practicing sustainability for responsible business in supply chains”,Journal of Cleaner Production, Vol. 251, p. 119621, doi:10.1007/978-3-030-23165-1.

Sima, V., Gheorghe, I.G., Subic, J. and Nancu, D. (2020),“Inﬂuences of the industry 4.0 revolution on the human capital development and consumer behavior: a systematic review”,Sustainability, Vol. 12 No. 10, p. 4035, doi:10.3390/su12104035.

Simatupang, T.M. and Sridharan, R. (2005),“An integrative framework for supply chain collaboration”, The International Journal of Logistics Management, Vol. 16 No. 2, pp. 257-274, doi:10.1108/

09574090510634548.

Singh, H., Garg, R. and Sachdeva, A. (2018),“Supply chain collaboration: a state-of-the-art literature review”, Uncertain Supply Chain Management, Vol. 6 No. 2, pp. 149-180, doi: 10.5267/j.

uscm.2017.8.002.

Sirmon, D.G., Gove, S. and Hitt, M.A. (2008),“Resource management in dyadic competitive rivalry: the effects of resource bundling and deployment”,Academy of Management Journal, Vol. 51 No. 5, pp. 919-935, doi:10.5465/amj.2008.34789656.

Sirmon, D.G., Hitt, M.A. and Ireland, R.D. (2007),“Managingﬁrm resources in dynamic environments to create value: looking inside the black box”,Academy of Management Review, Vol. 32 No. 1, pp. 273-292, doi:10.5465/amr.2007.23466005.

Sirmon, D.G., Hitt, M.A., Ireland, R.D. and Gilbert, B.A. (2011), “Resource orchestration to create competitive advantage: breadth, depth, and life cycle effects”,Journal of Management, Vol. 37 No. 5, pp. 1390-1412, doi:10.1177/0149206310385695.

JSTPM

(15)

Soosay, C.A. and Hyland, P. (2015),“A decade of supply chain collaboration and directions for future research”,Supply Chain Management: An International Journal, Vol. 20 No. 6, pp. 613-630, doi:

10.1108/SCM-06-2015-0217.

Tho, N.D. (2018),“Firm capabilities and performance: a necessary condition analysis”,Journal of Management Development, Vol. 37 No. 4, pp. 322-332, doi:10.1108/JMD-06-2017-0204.

Trkman, P., Budler, M. and Groznik, A. (2015), “A business model approach to supply chain management”,Supply Chain Management: An International Journal, Vol. 20 No. 6, pp. 587-602, doi:10.1108/SCM-06-2015-0219.

Ursachi, G., Horodnic, I.A. and Zait, A. (2015),“How reliable are measurement scales? External factors with indirect inﬂuence on reliability estimators”,Procedia Economics and Finance, Vol. 20, pp. 679-686, doi:10.1016/S2212-5671(15)00123-9.

Wei, D. (2020),“Modeling and simulation of a multi-agent green supply chain management system for retailers”,Journal Europeen Des Systèmes Automatises, Vol. 53 No. 4, pp. 549-557, doi:10.18280/

jesa.530414.

Whitrlock, V.G. (2019),“Multidimensional environmental social governance sustainability framework:

integration, using a purchasing, operations, and supply chain management context”,Sustainable Development, Vol. 27 No. 5, pp. 923-931, doi:10.1002/sd.1951.

Wijewickrama, M.K.C.S., Chileshe, N., Rameezdeen, R. and Ochoa, J.J. (2021),“Information sharing in reverse logistics supply chain of demolition waste: a systematic literature review”,Journal of Cleaner Production, Vol. 280, p. 124359, doi:10.1016/j.jclepro.2020.124359.

Wu, L. and Chiu, M.L. (2018), “Examining supply chain collaboration with determinants and performance impact: social capital, justice, and technology use perspectives”, International Journal of Information Management, Vol. 39, pp. 5-19, doi: 10.1016/j.

ijinfomgt.2017.11.004.

Wu, L., Chuang, C.-H. and Hsu, C.-H. (2014),“Information sharing and collaborative behaviors in enabling supply chain performance: a social exchange perspective”,International Journal of Production Economics, Vol. 148, pp. 122-132, doi:10.1016/j.ijpe.2013.09.016.

Wu, F., Yeniyurt, S., Kim, D. and Cavusgil, S.T. (2006),“The impact of information technology on supply chain capabilities andﬁrm performance: a resource-based view”,Industrial Marketing Management, Vol. 35 No. 4, pp. 493-504, doi:10.1016/j.indmarman.2005.05.003.

Xu, S., Zhang, X., Feng, L. and Yang, W. (2020),“Disruption risks in supply chain management: a literature review based on bibliometric analysis”,International Journal of Production Research, Vol. 58 No. 11, pp. 3508-3526, doi:10.1080/00207543.2020.1717011.

Zaridis, A., Vlachos, I. and Bourlakis, M. (2020),“SMEs strategy and scale constraints impact on agri- food supply chain collaboration and ﬁrm performance”, Production Planning and Control, Vol. 32 No. 14, pp. 1-13, doi:10.1080/09537287.2020.1796136.