Journal Article 5

Enhancing sustainable supply chain management performance

through alliance portfolio diversity: the mediating effect of sustainability collaboration

Angelina Nhat Hanh Le School of Management, University of Economics, Ho Chi Minh City, Vietnam

Tessa Tien Nguyen Management School, National Central University, Taoyuan, Taiwan, R.O.C and School of Management, University of Economics, Ho Chi Minh City, Vietnam, and

Julian Ming-Sung Cheng Management School, National Central University, Taoyuan, Taiwan, R.O.C

Abstract

Purpose –While strategic alliances is a concept increasingly discussed in the field of sustainable supply chain management (SSCM), an emerging and more crucial concept regarding alliances—namely, the alliance portfolio—is mostly ignored in the SSCM context. Mainly drawing on the categorisation–elaboration model (CEM), this research develops a three-layer model to explore the effects of three alliance portfolio diversity facets on the three triple-bottom-line SSCMperformances through themediation of sustainability collaboration. Design/methodology/approach – The field data are collected from 321 Vietnamese manufacturers. Scale accuracy is assessed through the confirmatory factor analysis method. Hierarchical linear regressions are applied to test the proposed model and hypotheses. Findings – Partner, governance, and functional alliance portfolio diversities have a U-shaped, inverted U-shaped, and positive linear effect, respectively, on sustainability collaboration. Sustainability collaboration is in turn found to enhance the SSCM performances in terms of economic, environmental, and social. Originality/value –This research introduced a new theoretical lens, CEM, to the SSCM field. It also provided findings that can help firms to manage their alliance portfolios more dynamically in terms of the nature and diversity level of the portfolio and in a way that adds to the triple bottom line through the mediating effect of sustainability collaboration.

Keywords Sustainable supply chain management, Diversity, Alliance portfolio, Categorisation–elaboration

model, Sustainable performance, Sustainability collaboration

Paper type Research paper

1. Introduction The aim of achieving sustainability performance, in particular optimising the three pillars of the triple bottom line—economic, environmental, and social sustainability—rather than simplymaximising profits, has dominated firms’ operations (see Abdul-Rashid et al., 2017). In recent years, as sustainability in supply chainmanagement (SCM hereafter) has been claimed as amajor contributor to firms’ overall sustainability performance (Lee and Tang, 2018), such a concept has been gradually introduced and applied in practice in SCM (Shafiq et al., 2017). Unilever’s waste-reduction strategy in its supply chain (Trebilcock, 2015) and Apple’s re-manufacturing process through a reverse logistics system (Liu et al., 2018) represent two

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This research is funded by Taiwan’s Ministry of Science and Technology (MOST) under grant number 109-2410-H-008 -032, and Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 502.02-2020.30.

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

https://www.emerald.com/insight/0144-3577.htm

Received 11 August 2020 Revised 30 April 2021

7 July 2021 Accepted 10 September 2021

International Journal of Operations & Production Management

Vol. 41 No. 10, 2021 pp. 1593-1614

© Emerald Publishing Limited 0144-3577

DOI 10.1108/IJOPM-08-2020-0505

illustrations of this phenomenon. Given the necessity of investigating the feasibility of sustainability implementation and performance achievement when managing supply chains (see Roy et al., 2018), academia has made significant efforts in this regard. A review of literature indicates that prior empirical studies have indeed put thought into discovering internal and external performance drivers/enablers in sustainable SCM (SSCM hereafter). These works discuss internal factors from perspectives such as resources, capabilities, and leadership (e.g. Nath and Agrawal, 2020; Paulraj, 2011), while investigating external factors from those including pressures related to requests from customers, suppliers, governments etc. (e.g. Meinlschmidt et al., 2018; Pakdeechoho and Sukhotu, 2018) and also strategic alliances (e.g. Liu et al., 2020; Meinlschmidt et al., 2018). However, an emerging and more crucial perspective in the literature that has received relatively little attention in SSCM is to view strategic alliances as a portfolio (Castiglioni and Gal�an Gonz�alez, 2020), defined in this case as a firms’ collection of direct alliances with partners (Lavie, 2007).

As stated by Bruyaka and Durand (2012), the diversity concept in firms’ alliance portfolios, which describes the distribution of variance in alliance types and characteristics (Jiang et al., 2010), has evidenced its critical role in the success and survival of firms. An increasing level of diversity in alliance portfolios provides firms with different kinds of knowledge and resource benefits which generate various levels of competitive advantage (also see, Caner et al., 2018). Nevertheless, it should be cautioned that extensive alliance portfolio diversity requires an enormous amount of management time and effort (e.g. Jiang et al., 2010; Marhold et al., 2017). The distinct characteristics of alliance portfolio diversity have been examined for several types of firm performance (e.g. market value and profit margin, innovation performance, and firms’ survival; also see Bruyaka and Durand, 2012; Caner et al., 2018; Oerlemans et al., 2013) and inter-organisational performance (e.g. alliance performance and innovation cooperation performance/open innovation performance; also see Cobe~na et al., 2017). In SSCM, while the variance in terms of supply chain partners has also been noted as having a pivotal role in influencing a firm’s SSCM-related performance through collaboration and resource sharing (Blome et al., 2014), there is a dearth of empirical research on the effect of diversity in the supply chain alliance/partner portfolio on SSCM performance.

A comprehensive review of the literature indicates inconsistent results regarding the effects of alliance portfolio diversity on firms’ performance-related outcomes, such as positive (e.g. Caner et al., 2018), negative (e.g. Penney andCombs, 2020), U-shaped (e.g. Jiang et al., 2010), and inverted U-shaped (e.g. Oerlemans et al., 2013). According to Bruyaka and Durand (2012), such inconsistencies can be explained by the divergence of the facets of alliance portfolio diversity, a circumstance which offers dissimilar types of benefits and poses different sets of challenges for firms.Major research typically examines individual aspects of alliance portfolio diversity separately in different inter-firm contexts (see e.g. Bruyaka and Durand, 2012; Caner et al., 2018; Oerlemans et al., 2013). However, according to Jiang et al. (2010), a comprehensive concept of alliance portfolio diversity—including functional diversity, partner diversity, and governance diversity—should be examined to provide a full picture of the varying effects of alliance portfolio diversity on firms’ performance-related outcomes. The above controversial linkage of the alliance diversity–performance relationship (i.e. linear or nonlinear) can be explained by the theory of the categorisation–elaboration model (CEM hereafter) (van Knippenberg et al., 2004). CEM, which has been evidenced as applicable in the diversity– performance literature (e.g. Ancarani et al., 2016; Seong et al., 2015), is capable of providing a theoretical foundation to explain the aforementioned inconsistent effects through the interaction of two mechanisms—namely, social categorisation and information elaboration. This model generally posits that different types and levels of group diversity may cause positive and/or negative effects to different degrees on performance-related outcomes, depending on the dominance of the social categorisation or information elaboration process (Seong et al., 2015). Based on CEM, different shapes of effects of the three facets of alliance

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portfolio diversity on sustainability collaboration outcomes, such as U-shape, inverted U- shape, and linear, can be explained by the interaction of these two processes. Our research, therefore, expands the explanatory background of CEM in the SSCM literature to develop a framework that systematically justifies the linear or non-linear effects of the three key alliance portfolio diversity facets (i.e. partner, governance, and functional diversities) on sustainability collaboration performance in SSCM.

As collaboration outcomes are claimed to be a critical precursor of firm performance in general (see e.g. Blome et al., 2014; Pakdeechoho and Sukhotu, 2018) in the current SSCM research, we further argue that while successful sustainability collaboration between alliances largely depends on the ability to effectively manage alliance portfolio diversity, the ultimate purpose of SSCM is to achieve the three triple-bottom-line performances. Thus, the mediating mechanisms/nexuses from partner, governance, and functional diversities to the three sustainability performances through sustainability collaboration are also investigated.

In this research, we provide several theoretical and managerial contributions to the literature. Firstly, the SSCM research gap regarding the relationships between alliance portfolio diversity and sustainability performance is bridged. By applying CEM, we develop a comprehensive framework in the SSCM context, which investigates the dissimilar effects (i.e. linear/non-linear) of the three facets of alliance portfolio diversity (i.e. partner, governance, and functional) on sustainability collaboration, which then drive the three final sustainable triple-bottom-line performances (i.e. economic, environmental, and social). The findings, while advancing the literature by providing the empirical evidence of the applicability of CEM in the supply chain field, draw an effective set of guidelines for sustainability policy and strategy-making practitioners.

2. Theoretical background, literature review, and hypothesis development In this section, first of all, the direct-effect hypothesis of the proposed framework is developed. Then, the mediating hypothesis is formulated (see Figure 1).

2.1 The effect of alliance portfolio diversity on sustainability collaboration Alliance portfolio diversity is defined as the extent of the variance of types and characteristics between alliances (Jiang et al., 2010). Due to limited resources, firmswill not suddenly diversify their alliance portfolio when having no alliance managing experience, but they will do it through a gradual transformation process instead (cf. Shukla and Mital, 2018). Sustainability collaboration describes firms’ engagement in the operational activities of alliances in terms of planning and executing sustainable solutions, as well as the devotion of its resources to striving for sustainability goals (Blome et al., 2014). Mutual understanding, joint efforts, and

Figure 1. Research model and proposed hypotheses

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feedback are regarded as required activities for collaboration (Blome et al., 2014). A robust review of the literature indicates that there is a dearth of research investigating the effects of alliance portfolio diversity in collaboration and performance outcomes in the SSCM context (a summary of the literature review will be provided on request). Deriving from CEM, all three facets of alliance portfolio diversity—partner, governance, and functional diversities—can stimulate both the social-categorisation and information-elaboration processes and eventually lead to various collaboration outcomes. Nevertheless, the salience of these two processes varies across low, medium, and high levels of each specific diversity.

Partner diversity can be described as the variance in terms of strategic alliances firms have with external organisations possessing different types of strategic and technical knowledge, such as buyers, suppliers, competitors, universities, and research labs (Oerlemans et al., 2013). Firms can diversify partners for the purposes of facilitating and strengthening their competitive advantages through utilising different types of knowledge (Oerlemans et al., 2013). However, these diverse allied partners may have notable dissimilarities in terms of aspects such as goals and decision-making processes, thus causing difficulties and problems in communication and collaboration (Jiang et al., 2010).

Therefore, at the initial stage of collaboration and when alliance diversity is at low-to- moderate levels, the divergences related to partners’ backgrounds, experience, knowledge, and technological bases will strongly activate the social categorisation process, which is strongly associated with negative impacts on partnership effectiveness. This occurs as such strong, deep levels of difference tend to cause negative emotions, thus reducing social integration (van Knippenberg et al., 2004).When the level of partner diversity increases, firmswill immediately face negative effects, such as conflict with competitors with whom those firms may work together, a lack of synergywith partners from unrelated industries etc (Jiang et al., 2010). This, in turn, inhibits/hinders collaboration between firms and partners. On the other hand, at this stage, firms can also access a broader knowledge domain and greater resources through the information elaboration process (van Knippenberg et al., 2004), but within a limited scope, as only a few diverse partners are involved (see, Jiang et al., 2010). Therefore, any benefits/gains from the information elaboration process are modest. In short, when partner diversity levels are low, the negative effect of social categorisation outweighs the benefits brought by information elaboration, thus impinging sustainability collaboration in the supply chain.

When the partner diversity level becomes high (i.e. from moderate to high levels), firms will accumulate certain experience from previous stages, thereby being able to manage diverse partners for better sustainability collaboration (see van Knippenberg et al., 2004). The developed knowledge, skills, and abilities when diversity levels increase will override the stereotype-based perceptions of similarities and differences and their negative effects on collaboration (Gaertner and Dovidio, 2000), which can then lower the salience of social categorisation/dissimilarity and give rise to the information elaboration process where firms and partners can exchange, process, and integrate diverse information and knowledge (van Knippenberg et al., 2004). That is to say, the benefits of partner diversity for collaboration will be enhanced, thus better facilitating collaboration regarding sustainability issues.

In summary, low levels of partner diversity harm sustainability collaboration due to the stronger negative effect of the social categorisation mechanism. However, beyond a certain threshold, this effect will gradually give way to the information elaboration process, positively impacting sustainability collaboration. We thus propose that:

H1a. There is aU-shaped relationship between alliance partner diversity and the level of sustainability collaboration.

Governance diversity refers to the variance of the equity-based structures (shared equity) of alliances, including non-equity and various equity-ownership arrangements (Jiang et al., 2010). Unlike partner diversity, governance diversity in alliance portfolios does not

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emphasise differences in terms of partners’ characteristics. Instead, it mainly focuses on the mechanism through which firms and their allied partners attempt to promote commitment among the alliance group (Li, 2013).

At the initial stage of governance diversity (i.e. from low-to-moderate levels), because of the emphasis of commitment, the social categorisation process will still be stimulated, but at low levels (see van Knippenberg et al., 2004). In contrast, since equity investments in alliance governance reflect the intention of firms to develop relations with allied partners, it is apparent that firms will exploit the core knowledge and resources gained from alliances and the information-elaboration process functions (see Li, 2013). Instead of broadly exploring supplementary and complementary resources and sources of knowledge, firms tend to attentively request that their allied partners demonstrate their commitment and deeply exploit their available knowledge and resources (cf. Chung et al., 2019). Accordingly, the positive effects of the information elaboration process generated at low-to-moderate levels of governance diversity are substantial, because firms and alliances are motivated by equity investments to contribute their core competences and resources for exploitative collaboration purposes (Chen and Lin, 2016). In a nutshell, due to relatively lower levels of governance diversity, stronger positive effects of the information elaboration process are predominant, whereas the negative effects of the social categorisation process are weak and minor, thus resulting in an overall positive effect on sustainability collaboration.

At higher levels of governance diversity, the early alliance management experience is not likely to be very useful or helpful because of the dissimilarity in relationship-building routines, managerial attention, and the unique resource commitments of different governance structures (Jiang et al., 2010). Despite a wide range of accessible information and resources, high governance diversity leads to complexity in the integration and utilisation of such information and resources, impinging the ability of firms to exploit the core knowledge, resources, and competence gained from their alliances (Chen and Lin, 2016). As a consequence, the negative effects of the social categorisation process dominate due to excessive dissimilarities regarding various levels of governance diversity, while the benefits of the information elaboration process are obscured (van Knippenberg et al., 2004). To wit, high levels of governance diversity generate more problems from a social categorisation standpoint and thus acquire less advantage from information elaboration, thereby undermining sustainability collaboration within the alliance group.

To summarise, at the outset, the increasing variance of the alliance governance structure positively affects the collaboration of firms and alliances regarding sustainability matters. However, beyond a certain point, this relationship will be subject to diminishing or even resulting in negative returns. This study therefore suggests that governance diversity has an inverted U-shaped relationship with sustainability collaboration.

H1b. There is an inverted U-shaped relationship between alliance governance diversity and the level of sustainability collaboration.

Functional diversity is the distribution of alliances who execute different functions, such as marketing, research and development, manufacturing, and logistics (Jiang et al., 2010). A high level of functional diversity in an alliance portfolio exploits the core competences of alliances, thus broadening the value creation of a supply chain (Jiang et al., 2010).

The purpose of joining functional alliances is to acquire valuable resources and capabilities that firms lack and that outside experts can offer (see Bruyaka and Durand, 2012). Having an alliance portfolio with diverse functional alliances signals that firms are prepared to establish relationships with partners with expertise to accomplish tasks of different business functions (Caner et al., 2018). Firms diversifying their alliance portfolio in terms of functions aim to further exploit, refine, and extend their existing knowledge, resources, skills, and competencies from such alliances (Jiang et al., 2010). Based on van Knippenberg et al. (2004), increasing levels of

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alliance portfolio diversity in terms of functions will stimulate the information elaboration process such as the exchange, processing, and integration of information and knowledge, which then allows firms and alliances to utilise supplementary knowledge and resources, thus facilitating collaboration for sustainability purposes. Consequently, the higher the level of functional diversity, the more the positive effects of collaboration performance gained from the information elaboration process. On the other hand, differences regarding functional diversity rarely trigger problems derived from the social categorisation process, as the complementary nature of diverse functions helps firms minimise the conflict and rivalry of redundancy and duplications among alliances (Cui and O’Connor, 2012). Therefore, the increasing information- elaboration process and the relatively weak effect of the social categorisation process generated by functional diversity will positively enhance collaboration performance in terms of sustainability purposes. In such situations, we delineate a positive effect of alliance portfolio functional diversity on sustainability collaboration.

H1c. There is a positive relationship between alliance functional diversity and the level of sustainability collaboration.

2.2 The mediating role of sustainability collaboration on the diversity–performance linkage The ultimate goal of managing diverse alliances in SSCM is to enable the progressive attainment of economic, environmental, and social sustainability performances (see Roy et al., 2018). These triple-bottom-line achievements can be manifested through firms’ engagement in and commitment to sustainability measures when involved in SSCM (Klassen and Verreecke, 2012). Among these three types of sustainability, economic sustainability refers to a firm’s efforts to reduce supply chain costs while balancing its business with other strategic and sustainability goals (Closs et al., 2011). Environmental sustainability deals with the reduction of environmental burdens such as pollution and greenhouse gas emissions and waste disposal (Abdul-Rashid et al., 2017). Finally, social sustainability places emphasis on social issues, in particular such matters as legislation, health, safety, and wages (see Closs et al., 2011; Paulraj, 2011). Therefore, we expect that alliance portfolio diversity would not only affect sustainability collaboration, but that it would further drive final SSCM performances. In the diversity literature, the significant mediating role of collaboration in the relationship between diversity and types of performance-related outcomes has been highlighted (e.g. Lee and Kim, 2020). In SCM, despite a lack of empirical research, scholars claim that synergetic collaboration provides an ideal platform for various forms of supply chain alliance diversity (e.g. partner types, governance structures, and functions) to transfer benefits such as sustainability knowledge, resource exchange, learning, and integration, and eventually generate value such as sustainability performance (e.g. Castro et al., 2016). Therefore, the linkage/nexus from alliance portfolio diversity to SSCM performance will occur via sustainability collaboration.

As noted by Niesten et al. (2017), firms interact with alliances through sustainability collaboration activities to develop collaborative sustainable solutions, legitimise existing sustainable technologies, lobby for environmental/social improvements, and co-provide environmental/social services, all while pursuing their own economic survival. However, depending on various levels and facets of alliance portfolio, different effects can be imposed on sustainability collaboration outcomes. In particular, the dissimilarity in terms of partner types, governance structures, and functions among alliances provides different levels of benefits to collaboration outcomes through the elaboration of knowledge and resources (van Knippenberg et al., 2004). In contrast, the divergence of alliances creates challenges to collaboration due to dissimilarity (van Knippenberg et al., 2004).

Through collaboration in sustainability practices, firms can further achieve sustainability performance, including economic, environmental, and social sustainability performances in

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SSCM (e.g. Blome et al., 2014; Pakdeechoho and Sukhotu, 2018). Within sustainability collaboration between firms and alliances, with the resultant access to a variety of resources, non-redundant and distinct experiences/skills/knowledge will be gained (see Blome et al., 2014). Moreover, industrywide codes of conduct that shape the guidance and requirements in terms of firm operations with a more economical approach, plus environmental and social responsibilities, can be compiled (Closs et al., 2011). From the perspective of economics, together with more transparent collaboration, which in turn reduces opportunism and monitoring costs (Cao and Zhang, 2011), and individual expertise specialisation in the collaborative supply chain, economies of scale are formed and competitiveness can be achieved (Blome et al., 2014). Eventually, higher revenue is generated (Cao and Zhang, 2011) and economic sustainability performance is enhanced. Regarding environmental impact, since sustainability collaboration will increase investment in pollution prevention technologies (Vachon and Klassen, 2008), such proactive environmental capital spending in conjunction with the variety of knowledge and better generated ideas through collaboration will enhance the capability of environmental management. Ultimately, the supply chain will provide higher environmental sustainability performance (ibid). As for the social aspect, sustainability collaborationwill facilitate supportive social actions andmitigate social risks among alliances, thus attenuating potential social hazards such as legislation for worker safety and welfare (Klassen and Vereecke, 2012). Those social standards integrated into alliance evaluation and selection can contribute to corporate reputation and image (Paulraj, 2011) and lead to better social sustainability performance of SSCM (Pakdeechoho and Sukhotu, 2018).

In the literature, the interveningmechanism of the collaboration-related concept in the link between alliance portfolio diversity and inter-firm performance has been explored. Examples include alliance portfolio capital in the global public works industry (Castro et al., 2016) and complementarity in the airline industry (Cobe~na et al., 2017), while their diversities differ from the concern of the current study and their outcome variables use a single general performance term. From the above theoretical reasoning and empirical evidence, we attempt to fill the SSCM literature gap regarding the role of collaboration as a mediator within the studied context. This study therefore proposes that sustainability collaboration serves as amediating role, transforming the effects of the three facets of alliance portfolio diversity into the three triple-bottom-line performances in SSCM.

H2. Sustainability collaboration mediates the relationship between alliance portfolio diversity (including: partner, governance, and functional) and sustainability performance (including: economic, environmental, and social).

3. Research methodology 3.1 Measurement and questionnaire design Existing scales were applied to measure the constructs of interest, with slight modification to fit the studied context (scale items can be provided on request). For the three antecedents, all addressing alliance portfolio diversity, the seven categories of allied partners, adapted from Oerlemans et al. (2013), were used to measure partner diversity, while the five and six categories taken from Jiang et al. (2010) were used to measure functional and governance diversity, respectively. Also, Blau’s heterogeneity index (1977) was applied to calculate and obtain the heterogeneity level of each diversity indicator. For any given indicator, its degree of diversity was calculated using equation D 5 1 � P

pi2, where p refers to the proportion belonging to a given category and i represents the number of different categories. The degree ranges from 0 (a group with perfect homogeneity) to 1 (a group of perfect heterogeneity, with alliance members spread evenly among all categories).

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For the rest of the studied construct scales, the mediator—i.e. sustainability collaboration—was assessed using Blome et al.’s (2014) six-item scale. Regarding the measurements of the outcome variables, i.e. SSCM performance in terms of economic, environmental, and social sustainability, the five-item scale drawn from Closs et al. (2011), the seven-item scale suggested by both Ortas et al. (2014) and Jeble et al. (2018), and the five-item scale employed by Paulraj (2011) were used, respectively.

To mitigate against potential systematic errors, competitive intensity—i.e. the degree of competition in an industry (Tsai and Yang, 2013)—was included as a control variable, with its scale consisting of four items taken from Jaworski andKohli (1993). Firm profiles were also introduced as control variables, including firm size (the number of employees) and firm age (the number of years a firm has been in operation). All these variables were posited in the literature to affect firm performance (Marshall et al., 2015; Qi et al., 2013).

All scales were originally taken from literature in the English language. As the data collection was carried out in Vietnam, a non-English speaking environment, a back- translation procedure was applied to translate the scale items into Vietnamese to ensure equivalency.

The questionnaire was pretested with five practitioners with more than 10 years’ experience each in SCM. They were asked to read through, answer, and critique the questionnaire on such facets as readability, clarity, and appropriateness to practical contexts. Changes were made according to their comments/suggestions.

3.2 Data collection The field studywas conducted in the Vietnamesemanufacturing sector. Since 2000, there has been an increasing trend in global offshoring and the relocation of several manufacturing firms from developed countries/areas such as the USA, the European Union, Japan, Korea, and Taiwan to emerging markets in South-East Asian countries (Phan et al., 2019). Being one of the most attractive countries in South-East Asia for foreign investment, Vietnam has gradually become an Asian manufacturing hotspot due to its relatively low labour costs, large workforce, high economic growth rate, geographical location, and open trade policies, among other factors (see VietnamBriefing, 2018). Major global players such asMicrosoft and Intel from the USA, Unilever and Bosch from Europe, Honda from Japan, Samsung from South Korea, and Foxconn from Taiwan have moved their manufacturing activities to Vietnam, which has raised the foreign direct investment capital inflow of Vietnamese manufacturing industries to 44% (cf. Vietnam Briefing, 2018). These foreign investors have enabled the procurement of local products and services, thus bringing local manufacturers into their global production networks and simultaneously nourishing the demand, development, and management of domestic supply chains in Vietnam. Furthermore, according to Vietnam Briefing (2018), the Vietnamese manufacturing sector consists of a large range of SCM functions while diversity partnership has gradually emerged as a focused businessmodel (see VietnamNews, 2020).Moreover, in Vietnam, the concept of sustainability has been widely considered by the government with the approval of the Sustainability Development initiative by the Prime Minister of Vietnam in 2012 (Government Portal, 2019). Since then, an increasing number of firms in Vietnam, being aware of the significance of sustainability, have started to design and implement sustainability strategies to align with government guidance. According to Newman et al. (2018), Vietnamese manufacturers have already promoted and even expanded environmental and social responsibilities into their policies and operations.

Data were collected in manufacturing firms mainly in industrial zones located in Binh- Duong province, the province neighbouring Ho-Chi-Minh City. According to Thong Tin Thong Ke Tinh Binh Duong (2020), Ho-Chi-Minh City and its four neighbouring provinces,

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including Binh-Duong province, comprise the main economically developing area, which accounts for 40% of the national GDP. This area is the main manufacturing centre in Vietnam, with Binh-Duong province containing around 30 industrial zones and attracting investors from more than 30 countries. The Binh-Duong authority has integrated sustainability into its provincial policies, thus motivating its firms to introduce the sustainability concept into operations. Firms in Binh-Duong, compared to other provinces, are thus more familiar with sustainability issues.

The questionnaire was distributed by trained interviewers to the managers of 500 firms. These managers were eligible to participate in our field study and to answer questions regarding their firms’ sustainability practices, as their jobs were related to manufacturing operations such as production managers and procurement managers. In total, 327 questionnaires were collected. However, 6 of them included missing data and were excluded accordingly. Eventually, 321 were considered to be valid (response rate of 64.2 percent) and were then used for analysis. Since the statistical power of the collected questionnaires was above the threshold of 0.80, the sample size was large enough for analysis. The demographics of the participating firms will be provided on request.

4. Data analyses and results 4.1 Bias countermeasures Three potential types of bias, namely sample selection bias, non-response bias, and common method bias (CMB), were analysed for determining the validity of the survey procedure and questionnaire.

Sample selection bias.Tomitigate against possible sample selection bias, several remedies were applied during the data collection procedure, such as collecting data in different groups/ locations (also see Gonz�alez-Santos et al., 2020; Wang et al., 2020a, b). Moreover, when analysing the data, Heckman’s (1979) two-stage model based on a firm’s export experience with and without exporting was performed to address whether or not sample selection was a concern. At the first stage of the ascertainment, we distinguished between organisations’ export experience, followed by implementing Heckman’s probit model to predict this binary, dichotomous variable-export experience. In this model, we also included control variables and firm type as instrumental variables. At Stage II, the InverseMills Ratio (obtained at Stage I) was incorporated into the structural models as a covariate to correct the sample selection bias to predict the three dimensions of SSCM performance. The results suggested that the coefficients of the Inverse Mills Ratio were not supported in the regression models. Based on these, selection bias was not likely to impose limitations in our study.

Non-response bias. Primarily in following Lucianetti et al. (2019) and Wang and Tarn (2017), we deployed two separate non-response assessment procedures. The first procedure was based on time responses following Armstrong and Overton (1977); we pairwise compared the first (n5 80) and the last (n5 80) quarters of the sample (referring to early and late respondent groups, respectively) on firm type and export intensity. Their results for group similarity indicated that no statistically significant difference was found. In the same vein, we tried to rule out the difference between early and late groups based on all the measurement items of the focal study constructs, a more rigorous homogeneity test than difference assessment based on constructs (Kwak et al., 2018; Onofrei et al., 2019; Yang et al., 2017). Our checks revealed no significant difference along 15 of the 17 scale items. The second procedure then reviewed whether there was difference in the mean scores of these 17 scale items in relation to two groups of firm characteristics, size and capital. The results also yielded non-significant differences among 15 out of the 17 items. All results illustrated and reinforced an overall absence of significant non-response bias in this study (see Guo et al., 2017).

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Common method bias. Both instrumental design procedures and statistical methods were used to check for possible CMB. On the questionnaire and during the data collection, usage of procedures such as pre-validated scales, pre-testing, and participation anonymity were employed and implemented. During the data analysis, three statistical analyses were applied to evaluate the CMB level. First of all, a Harman’s single-factor test for exploratory factor analysis (EFA) was applied, resulting in seven distinct factors with eigenvalues above 1.0; these factors together explained 71.71 percent of the total variance, and the first factor explained only 31.84 percent of the total variance, significantly less than 50 percent. A confirmatory factor analysis (CFA) approach was then used to perform Harman’s single- factor test. Unacceptable model fit indices, including χ2/df5 8.217, CFI5 0.511, IFI5 0.514, TLI5 0.472, and RMSEA5 0.15 suggested that no single or general factor emerged. Finally, based on the stringent post hocmarker variable test suggested by Lindell andWhitney (2001) and the procedure proposed by Hultman et al. (2009), the second smallest positive correlation of 0.025was isolated from the other correlations to compute the CMB-adjusted. The difference between the original and the CMB-adjusted correlations was around 0.03, indicating no change in the significance of all the correlations. Moreover, we re-estimated our measurement model using the CMB-adjusted correlations, while the chi-square difference test between the original and the CMB-adjusted models indicated insignificant deterioration fit (Δχ2 ≤ 0.33). Overall, these bias checks suggested that CMB was not a significant problem in this study.

4.2 Scale accuracy analysis The accuracy of the multi-item measurement scales was evaluated through CFA (see results in Table 1). First of all, the measurement model was assessed, and the result yielded an acceptable model fit, with χ2/df5 2.322, RMSEA5 0.064, SRMR5 0.0575 (which were below their respective thresholds of 3.0, 0.07, and 0.08) and IFI5 0.916, TLI5 0.903, CFI5 0.916 (all above the cut-off point at 0.9). Then, for testing reliability, the following indexes were used, including a Cronbach’s alpha coefficient of 0.7, a composite reliability (CR) index of 0.7, and an average variance extracted (AVE) value of 0.5. As observed in Table 1, all indexeswere above their respective thresholds. To test convergent validity, all the items were checked as to whether they would be loaded onto their corresponding variables without a factor loading below 0.5. After removing three items with a low factor loading (pertaining to environmental sustainability, social sustainability, and competitive intensity), all the remaining loadings came in above the threshold. To test discriminant validity, three criteria were applied, i.e. correlation values, AVE values, and Heterotrait-Monotrait (HTMT) ratios (Tables 1 and 2).

Constructs No. (scale items) Meana

(SD) α CRb AVEc Item loadingsOriginal Final

Sustainable collaboration 6 6 5.03 (1.09) 0.89 0.98 0.59 0.81; 0.94; 0.80; 0.66; 0.70; 0.64

Economic sustainability performance

5 5 5.45 (0.97) 0.78 0.97 0.54 0.72; 0.70; 0.90; 0.60; 0.72

Environmental sustainability performance

7 6 5.06 (1.16) 0.87 0.97 0.55 0.71; 0.77; 0.80; 0.67; 0.70; 0.75

Social sustainability performance

5 4 5.60 (0.97) 0.87 0.98 0.65 0.90; 0.76; 0.83; 0.72

Information processing capability

4 4 5.15 (1.21) 0.85 0.96 0.59 0.72; 0.75; 0.83; 0.78

Competitive intensity 4 3 5.05 (1.10) 0.79 0.94 0.56 0.71; 0.86; 0.70

Note(s): abased on a seven-point Likert scale; bComposite Reliability; cAverage Variance Extracted

Table 1. Measurement accuracy assessment

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All correlation values were less than the conservative cut-off value of 0.8, the square root of the AVE value was higher than all of the construct’s shared variances with other constructs, and the HTMT ratios were all below the threshold of 0.9 (Henseler et al., 2015).

4.3 Endogeneity The Durbin–Wu–Hausman test (Davidson and MacKinnon, 1993) was adopted to address potential endogeneity bias. First of all, we regressed sustainability collaboration on all control variables and firm capital, which was chosen as an instrumental variable. The residual generated from this regression was then used as an additional regressor in hypothesised equations. The parameter estimated for the residual was not significant in all hypothesised regression models. This indicated that sustainability collaboration was consistent with its conceptualisation and not endogenous in our setting.

4.4 Hypothesis testing The processes suggested by Squire et al. (2009) and Haans et al. (2016) were applied to examine the direct effects, while Zhao et al.’s (2010) procedure was followed to analyse the mediating effects. All examined predictors were mean-centred to reduce the potential multi- collinearity problem (Aiken and West, 1991). The research framework and hypotheses are presented in Figure 1 (see start of Section 2) while the results are shown in Table 3.

Direct effects (H1a–H1c). The hypothesised relationships were tested using a hierarchical regression approach (Squire et al., 2009). A baseline model, i.e. Model 1, containing the linear effects of the three alliance portfolio facets on sustainability collaboration, was first tested. Next, Model 2, which included the linear effects of the independent variables and their quadratic terms, was formulated to examine the non-linear effects of partner diversity and governance diversity on sustainability collaboration (H1a and H1b, respectively). From the results of Model 2, we found evidence supporting a U-shaped relationship between partner diversity and sustainability collaboration (H1a) with a significant (positive) quadratic effect (β5 0.252, p< 0.001). The results also supported H1b with a significant (negative) non-linear effect of governance diversity related to sustainability collaboration (β 5 �0.225, p < 0.05). The R2 difference betweenModel 1 andModel 2 was 0.043, significant at p< 0.001, indicating that the quadratic effects ofModel 2 explained the outcome variable better thanModel 1, thus providing additional evidence to support H1a and H1b.

According to Haans et al. (2016), the significance of the quadratic coefficient was not sufficient to conclude the existence of a quadratic relationship. Therefore, we conducted a further step to examine the slopes at the low-end and high-end (XL and XH) of the data range, as well as the turning point of each researched non-linear effect. The results are shown in Table 4, and the curvilinear relationships of H1a and H1b are plotted in Figure 2.

For H1c, the linear positive effect of functional diversity on sustainability collaboration was confirmed with the significant beta at 0.345 (p < 0.001) in Model 1, while this linear effect

Construct scales SC EcS EnS SoS IPC CI

Sustainability collaboration (SC) 0.767 0.220 0.589 0.318 0.575 0.386 Economic sustainability performance (EcS) 0.218 0.740 0.539 0.666 0.422 0.343 Environmental sustainability performance (EnS) 0.590 0.555 0.736 0.572 0.527 0.323 Social sustainability performance (SoS) 0.321 0.651 0.567 0.806 0.507 0.274 Information processing capability (IPC) 0.581 0.413 0.523 0.506 0.768 0.309 Competitive intensity (CI) 0.388 0.335 0.319 0.273 0.307 0.750

Note(s):Bivariate correlations and HTMT ratios are at the lower and upper part of the diagonal, respectively, while the diagonal elements are the square root of AVE (highlighted in Italics)

Table 2. Measurement accuracy analysis - discriminant

validity assessment

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also remained significant in Model 2 (β 5 0.246, p < 0.001). Regarding the control variables, the analysis results of the three control variables showed they had different impacts on the three types of SSCM performance (see Models 3, 4, and 5).

Mediating effects (H2).We employed the widely accepted mediation testing procedures in the pathmodelling framework of Zhao et al. (2010) and followed prior research examining the mediating effect involving curvilinear relationships (e.g. Lawrence et al., 2019) to evaluate the mediating effects. We added the direct paths from sustainability collaboration and the control variables to the three types of SSCM performance in Models 3a–3c. The results in Models 3a–3c revealed that while all the proposed effects of alliance portfolio diversity aspects on sustainability collaboration were supported in Model 2, the effects of sustainability collaboration on economic, environmental, and social performance were also significant in Models 3a–3c (β 5 0.257, p < 0.001; β 5 0.464, p < 0.001; β 5 0.347, p < 0.001, respectively). Based on these results, the mediating role of sustainability collaboration was confirmed, thus supporting H2. In addition, we considered direct paths from the three alliance portfolio diversity facets to the three SSCM performances in Models 4a–4c to determine their mediating types. The summary of the mediation types is presented in Table 5.

Hypothesis The slope at low-end

(β) The slope at low-end

(β) Turning point Results

H1: PD – SC �3.282c 6.358c 0.353* Supported positive U-shaped H2: GD – SC 2.971c �1.908a 0.652* Supported negative U-shaped

Note(s): a p < 0.05, p < 0.01, c p < 0.001 *well-located within the data range PD: Partner Diversity; GD: Governance Diversity; SC: Sustainability

Variables Model 1 Model 2 Model 3 Model 4

SC SC a. EcS b. EnS c. SoS a. EcS b. EnS c. SoS

Controls CI 0.225c 0.129a 0.141a 0.208c 0.126a 0.136a

Firm size �0.276c 0.055 �0.281c �0.291c 0.040 �0.279c

Firm age 0.019 �0.034 0.098 0.048 �0.021 0.122

Direct effects PD 0.014 0.003 �0.199a �0.074 �0.125 GD 0.070 0.227a �0.099 �0.009 �0.125 FD 0.345c 0.246c �0.001 0.018 �0.054 (PD)2 �0.225a 0.302c 0.114 0.115 (GD)2 0.246c 0.070 �0.028 0.012

Mediator SC 0.257c 0.464c 0.347c 0.218c 0.446c 0.347c

R2 0.145 0.188 0.161 0.279 0.166 0.229 0.288 0.205 R2_change (ΔR2) 0.043c

F 17.88c 12.08c 15.21c 30.55c 15.75c 10.28c 14.00c 8.90c

Note(s): PD: Partner Diversity; GD: Governance Diversity; FD: Functional Diversity EcS: Economic Sustainability; EnS: Environmental Sustainability; SoS: Social Sustainability CI: Competitive Intensity; SC: Sustainability Collaboration; IPC: Information Processing Capability a p < 0.05, b p < 0.01, p < 0.001

Table 4. Additional curvilinear effect tests

Table 3. Hierarchical regressions and multiple regression test results

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5. Discussion In this section, several theoretical and managerial implications of the findings are discussed.

5.1 Theoretical implications In recent years, sustainability has been introduced in SCM to form a SSCM system (Shafiq et al., 2017) in which SSCM practices can eventually enhance overall firm performance (Lee and Tang, 2018). In the literature, strategic alliances are emphasised and found to be a significant influencer of SSCM performance-related outcomes (e.g. Liu et al., 2020), while existing studies explored it from the perspective of particular alliances such as suppliers, buyers, or customers (for a review, see Roy et al., 2018). Despite the fact that a more comprehensive approach that covers these alliances together as a whole—i.e. the alliance portfolio—can provide a more complete picture and better insights than those that only consider individual alliances (e.g. Niesten et al., 2017), such an integrated view has not received much attention. This study therefore investigates the impacts of various aspects of alliance portfolio diversity (i.e. partner, governance, and functional diversities) on SSCM outcomes—sustainability collaboration—which acts as a mediator and can lead to the three triple-bottom-line performances in SSCM (e.g. Blome et al., 2014; Pakdeechoho and Sukhotu, 2018). By doing so, we advance our knowledge, contribute to this ignored topic in the SSCM

Mediating effects Mediating types

H4a: PD-SC-EcS Partial mediation H4b: GD-SC-EcS Full mediation H4c: FD-SC-EcS Full mediation H5a: PD-SC-EnS Full mediation H5b: GD-SC-EnS Full mediation H5c: FD-SC-EnS Full mediation H6a: PD-SC-SoS Full mediation H6b: GD-SC-SoS Full mediation H6c: FD-SC-SoS Full mediation

Note(s): PD: Partner Diversity; GD: Governance Diversity; FD: Functional Diversity EcS: Economic Sustainability; EnS: Environmental Sustainability; SoS: Social Sustainability CI: Competitive Intensity; SC: Sustainability Collaboration; IPC: Information Processing Capability

Figure 2. Curvilinear effects of

H1a and H1b

Table 5. Mediating type

summary

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literature, and respond to Carter et al.’s (2019) call for more research into the subject of supply chain diversity.

To interpret the varying effects of alliance portfolio diversity on inter-firm performance, a number of theoretical backgrounds have been introduced, such as Transaction Cost Theory, Real Options Theory, and Learning Theory (for an overview, see Penney and Combs, 2020). These theories are applied to explain the varied effects of different alliance portfolio diversity facets from one particular side of the effects, i.e. either positive (e.g. Jiang et al., 2010) or negative (Penney and Combs, 2020), on firm/inter-firm performances. However, the diversity of supply chain partners brings about both benefits (e.g. diverse knowledge sharing) and problems (information overload and conflicts), which can facilitate or hinder supply chain actors’ collaboration (see Niesten et al., 2017), thereby resulting in both positive and negative effects. By providing a fresh perspective from the diversity literature, namely CEM, our study explains both the positive and the negative effects of alliance portfolio diversity on performance-related outcomes through the interaction of two mechanisms, social categorisation and information elaboration. The findings support our arguments on the curvilinear or linear effects of different aspects of alliance portfolio diversity on sustainability collaboration. As such, we extended the applicability of CEM in the studied SSCM issue.

Notably, according to CEM, the two processes, social categorisation and information elaboration, produce diverse (positive and negative) effects on collaboration practices. While an overriding process is generated and outweighs the other process, the linear effect of diversity on collaboration practices will be produced. However, beyond a certain threshold/ cut-off point, that overriding process might fade and be overcome by the other process, thus triggering and turning to a reversed effect, as such leading to a curvilinear type of effect. Based on these arguments, this research systematically and deeply develops, investigates, and finds theU-shaped, invertedU-shaped, and linear effects of the different facets of alliance portfolio diversity—partner, governance, and functional, respectively—on sustainability collaboration. We thus contribute to both the existing alliance portfolio diversity and the SSCM literature.

Finally, we find support for the mediating effects of sustainability collaboration on the relationships between the alliance portfolio diversity facets and the SSCMperformances—i.e. economic, environmental, and social. The mediating effect of collaboration in the diversity– performance link has been examined in the context of group diversity (e.g. Lee and Kim, 2020). Also, scholars claim that diversity among supply chain alliances can generate sustainability outcomes through a synergetic collaboration platform (e.g. Niesten et al., 2017). However, there is a lack of empirical studies regarding the mediating effect of sustainability collaboration on the alliance portfolio diversity–sustainability performance link. We therefore fill this literature gap while further developing a three-layer framework that emphasises the critical role of alliance portfolio diversity in the ultimate goal of SSCM: triple- bottom-line performances.

5.2 Managerial implications The concrete evidence of the divergent effects of the three facets of alliance portfolio diversity on SSCM outcomes raises supply chain managers’ awareness of the types of benefits and sets of challenges according to various levels of partner, governance, and functional diversities. Thus, appropriate/relevant practices and guidelines based upon the relevant circumstances are recommended for firms to effectively manage their alliance portfolio to improve sustainability collaboration and ultimately enhance the triple-bottom-line performances/goals.

TheU-shaped effect of partner diversity on alliance sustainable collaboration implies that firms at low-to-moderate levels of partner diversity should pay attention to a variety of potential conflicts triggered by the various goals and backgrounds of firms and their alliances, such as economic-vs. sustainability-oriented, manufacturing vs. service, and

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capital-vs. labour-intensive. Asymmetry of information is a major source of conflicts that inhibit mutual understanding between alliances and impedes joint efforts for sustainable purposes in the supply chain (Simatupang and Sridharan, 2002). Thus, managers should set up a platform for frequent communication to proactively exchange and update information relating to sustainability collaboration as well as provide prompt and relevant support to their firms’ partners, as such solving miscommunication, sustainability goal misalignment, and failure to obtain resources from each other (Cai et al., 2017). Such an approach can not merely reduce potential conflicts but can even turn destructive conflicts into constructive ones (see Shahzad et al., 2020). As suggested by Vlaisavljevic et al. (2016), firms and partners should agree on the use of a formal language to capture and codify the sustainability knowledge or sustainable codes of conduct such as utilising manuals, processes, and software. Problems of dissimilarity in operations and decision-making processes that are exposed by the differences between partnering alliances can thus be reduced, while firms can accumulate necessary alliance partnermanagement experience formanaging higher levels of partner diversity better. When partner diversity is at high levels, the investment in more comprehensive information processing systems with the digitisation of supply chain processes should be considered to facilitate the handling of increasing loads of information and materials, thereby more effectively utilising available resources from diverse partners. According to prior literature (e.g. Seyedghorban et al., 2020), several advanced technologies have been introduced to augment supply chain collaboration functions, such as SRM (Supplier Relationship Management), CRM (Customer Relationship Management), EERP (ExtendedEnterprise Resource Planning), andAIoT (smart Internet of Things powered byAI (artificial intelligence) using cloud computing and big data analytics). Via the applications of these technologies, firms and alliances can standardise their sustainability decision-making processes and accelerate the exchange of plentiful knowledge and resources, which in turn enhance sustainability collaboration and SSCM performance.

In terms of governance diversity, our findings reveal an inverted U-shaped effect on sustainability collaboration. That is, at lower levels of diversity, governance structure diversity can benefit sustainability collaboration in that more equity investments from different alliances will encourage the commitment and devotion of alliances to sustainability goals. Firms can thus exploit partners’ resources at different and more profound levels (Chung et al., 2019). Despite this, alliances may hesitate to devote more domain knowledge and expertise to the collaboration due to a concern of core competence protection (Manhart andThalmann, 2015). It is suggested that firms should promote common benefits of the entire sustainability supply chain, as such urging alliances to devote resources rather than simply equity-based ownerships. When the diversity level of governance structure increases to a certain high level, firms should take a different approach and consider the management complexity involved. Therefore, there is a trade-off between governance diversity and firms’ capabilities in dealing with the resultant problems to achieve the optimal diversity level of governance structures for the best collaboration outcomes (see Chen and Lin, 2016). Frequent alliance evaluation is also crucial for the elimination of ineffective alliances within a complicated governance structure. A comprehensive performance assessment mechanism with clearly defined sustainability objectives is required to effectively evaluate alliances’ contributions to sustainability collaboration outcomes (e.g. Maestrini et al., 2018). Dedicated staff or functions can be assigned or set up to secure relevant resources and routines for managing alliance portfolios, thus controlling the portfolio diversity as optimised for increased performance in a timely manner (Marhold et al., 2017).

Regarding functional diversity, our finding suggests a positive linear relationship with sustainability collaboration. Therefore, firms can findways to increase the number of alliances with complementary and more specific functions. For example, firms can select their alliances not just with logistics expertise in mind, but even more specific aspects, such as order

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processing, materials handling, and warehousing. Firms can further design a proactive alliance selection scheme to frequently scan and monitor the market (e.g. visiting industry conventions and trade shows) to identify new alliance opportunities ahead of their rivals (Degener et al., 2018). Also, a clear alliance policy is necessary to align the new alliances with the existing alliance portfolio. As suggested byHoffmann (2005), alliance policies should cover general principles of alliance management, general and sustainability requirements for partners, rules on areas and situations for sustainability collaboration, and rules on sustainability cooperationmanners. If firms are dealingwith an enormous number of alliances, the re-assessment of partners in each function is necessary to identify unsuitable ones, such as those with function duplication, while special units to synthesise the functions provided by individual alliances are required to facilitate collaboration for sustainability purposes.

Finally, it also deserves noting that the mediation analyses show different magnitudes of the effects on the triple-bottom-line performances in SSCM. In relation to others, environmental performance is most strongly driven by alliance portfolio diversity through sustainability collaboration, while these precursors least strongly influence economic performance. These insights can help firms adjust their SSCM alliance portfolio strategies for specific sustainability dimension purposes.

6. Conclusions In this study, an attempt is made to contribute to the research stream on the role of strategic alliances in SSCM performance (e.g. Meinlschmidt et al., 2018; Liu et al., 2020) from a more critical, but primarily ignored, concept in the SSCM literature—namely, alliance portfolio diversity. Borrowing from CEM (van Knippenberg et al., 2004) as its scholarly grounding, we explain how partner, governance, and functional diversities separately and distinctively drive sustainability collaboration based on various shapes of effects (non-linear U- and inverted U-shaped, as well as linear, respectively). Collaboration, acting as a mediator in the proposed framework, in turn leads to the three sustainable SSCM performances: economic, environmental, and social. Based on these findings, a list of applicable managerial implications with evidence is provided for practitioners and supply chain managers.

In spite of the theoretical and managerial contributions in this research, there remains room for improvement in this study, thus providing stimuli for future work. Firstly, additional research can be conducted for validating, enhancing, and generalising the findings. For example, as sustainability issues can be dynamic (Abdul-Rashid et al., 2017) and our field study is based on a cross-sectional basis, we suggest applying a longitudinal data collection approach to examining the proposed study framework.Moreover, aligningwith the emerging usage of business metrics in an SCM context (Jeble et al., 2018), we recommend that researchers test the proposed relationships with the assistance of big data analytics to enhance the robustness and accuracy of the findings. Secondly, despite our research determining alliance portfolio diversity to have three aspects consisting of partners, functional purposes, and governance structures of the alliances, there are also some other diversity domains (e.g. technology and organisation) and types (e.g. separation, variety, and disparity) relating to alliance portfolios (Lee et al., 2017). Future research can include these domains and types for investigation. Thirdly, the relationship between alliance portfolio diversity and SSCM performance is mainly explained based on collaboration and communication regarding resource and knowledge-sharing between alliances. According to Manhart and Thalmann (2015), the knowledge flow within the over-diversified alliance portfolio may lead to the risk of leaking competitive knowledge, which would eventually harm performance. This raises an interesting topic relating to knowledge management and knowledge protection among alliances in SSCM, in particular. Further research can thus examine how alliance portfolio diversity affects firms’ knowledge management and

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protection practices in SSCM. Fourthly, being the focal studied precursor, diversity might be associatedwith other influencers which have a final impact on sustainable performance, such as required communication among partnering firms and the social structures occurring between them. Therefore, related theories, such as the information processing view (Galbraith, 1974) and the institutional theory (DiMaggio and Powell, 1983), can be introduced and implemented together with CEM to complementarily interpret the current issue, thereby providing a more complete picture of this study topic and better insights as a consequence. Finally, the relative power held by the participating firms and their roles in SCM, such as orchestrators and executors, may affect their attitudes and behaviours in practice (see Johnston et al., 2018). The power and/or their roles can be contingent conditions for accessing the relationship between alliance portfolio diversity and SSCM performance.

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About the authors Angelina Nhat Hanh Le is Senior Lecturer of Marketing in Management School, University of Economics HCM City, Vietnam, where she teaches marketing management, consumer behaviour, and research methodology. Her current research interests include consumer behaviour, Internet marketing, brand management, marketing channels, green marketing, and meta-analysis. Her research has been published in Journal of the Academy of Marketing Science, Journal of International Marketing, International Journal of Advertising, Management Decision, Asia Pacific Journal of Marketing and Logistics, and so on.

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Tessa Tien Nguyen is a PhD student in the Business Administration Department, National Central University, Taiwan, R.O.C. She is also a research assistant in School of Management, University of Economics Ho Chi Minh City, Vietnam. Her current research interests include supply chain management, business analytics, and sustainability marketing.

Julian Ming-Sung Cheng is Professor of Marketing in the Business Administration Department, National Central University, Taiwan, R.O.C, where he teaches international branding, marketing channels, and sustainability marketing. His current research interests include digital marketing, marketing channels, branding, glocal marketing, green marketing, neuro marketing, marketing analytics, andmeta-analysis. His research has been published in various journals, such as: Journal of the Academy of Marketing Science, Industrial Marketing Management, Journal of International Marketing, Journal of Advertising Research, and so on. JulianMing-Sung Cheng is the corresponding author and can be contacted at: [email protected]

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