# How Does IOS-Enabled Business Intelligence Enhance Supply Chain Performance?

Caleb Amankwaa Kumi, David Asamoah, Benjamin Agyei-Owusu

► To cite this version:

Caleb Amankwaa Kumi, David Asamoah, Benjamin Agyei-Owusu. How Does IOS-Enabled Business Intelligence Enhance Supply Chain Performance?. 20th Conference on e-Business, e-Services and e-Society (I3E), Sep 2021, Galway, Ireland. pp.441-453, 10.1007/978-3-030-85447-8_37. hal-03648109

HAL Id: hal-03648109

https://inria.hal.science/hal-03648109

Submitted on 21 Apr 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# How does IOS-enabled business intelligence enhance supply chain performance?

Caleb Amankwaa Kumi¹[0000-0003-4431-1241] David Asamoah²[0000-0002-8948-6697] and
Benjamin Agyei-Owusu³[0000-0001-7364-7858]

¹²³ Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
caleb.kumi3@gmail.com
dasamoah.ksb@knust.edu.gh
bagyei-owusu.ksb@knust.edu.gh

**Abstract.** This study examines the mechanisms through which IOS-enabled business intelligence enhances supply chain performance of organizations. The study proposes that IOS-enabled business intelligence first impacts information exchange, coordination and integration capabilities, which subsequently enhance supply chain responsiveness and supply chain performance. A research model examining the proposed relationships was developed and tested using data from retail organizations operating in an emerging African country. Data analysis through partial least squares structural equation modelling confirmed that IOS-enabled business intelligence allowed firms to obtain information exchange, coordination, integration and supply chain responsiveness capabilities. With the exception of integration capabilities, supply chain capabilities directly enhanced supply chain performance as well. Finally, supply chain responsiveness partially mediated the relationship between information exchange capabilities and supply chain performance and fully mediated the relationship between integration capabilities and supply chain performance. The implications of these findings are discussed.

**Keywords:** Business Intelligence, Inter-Organizational Information Systems, Supply Chain Capabilities.

## 1 Introduction

Inter-organizational information systems (IOS) are network-enabled information systems that allow organizations to effectively manage business operations and supply chain activities across several organizations (Asamoah et al., 2021a). Adoption and use of IOS have grown in recent decades, and have become common place in many sectors. Researchers have identified that adoption and use of IOS can be patterned to achieve three possible objectives, namely, to enable communication, to achieve integration, and to enable business intelligence (Zhang and Cao, 2018; Subramani, 2004). Deploying IOS for business intelligence is of particular relevance in the present era of big data where large volumes of business data is created on a daily basis. Exploring and understanding business data can help firms gain new insights into their operations, customers
and market, and this can serve as the foundation of higher performance. IOS-enabled
business intelligence refers to the extent to which the use of IOS facilitates learning and
knowledge creation between members of a supply chain network (Zhang and Cao,
2018). Applications for IOS-enabled business intelligence may take the form of shared
database and decision support systems, shared knowledge acquisition, and artificial in-
telligence (Mandal and Dubey, 2021).

Previous studies reveal that deploying IOS enhances outcomes such as firm perfor-
mance (Hartono et al., 2010), supply chain capabilities (Rajaguru and Matanda, 2013),
and supply chain performance (Cho et al., 2017; Asamoah et al., 2021a). The extant
literature in exploring outcomes of IOS however often lump different dimensions of
IOS use together, largely examining IOS use at the second order level, and failing to
examine how specific dimensions of IOS use may enhance performance of firms. This
means that there still exists a gap in our understanding on whether and how IOS-enabled
business intelligence enhances firm performance. There have been multiple calls by
researchers to examine the influence of different dimensions of IOS use on performance
(Asamoah et al., 2021a; Agbenyo et al., 2018). Further, there is very little understanding
into the mechanisms through IOS-enabled business intelligence enhances firm perfor-
mance. This study addresses these research gaps by examining the role of information
exchange, coordination, integration and supply chain responsiveness capabilities in ex-
plaining the outcomes of IOS-enabled business intelligence. The study proposes that
IOS-enabled business intelligence initially enables firms to obtain information ex-
change, coordination and integration capabilities. These supply chain capabilities are
then leveraged to achieve higher supply chain responsiveness, and ultimately, supply
chain performance. These assertions are tested using data obtained from firms operating
in an emerging economy.

The study is significant in a number of ways. This study is the first to empirically
examine how IOS-enabled business intelligence drives supply chain performance, to
the best of our knowledge. The study provides new insight into the business intelli-
gence-supply chain performance link by proposing and empirically confirming that
IOS-enabled business intelligence first creates information exchange, coordination and
integration capabilities, which are leveraged for greater supply chain responsiveness
and supply chain performance. The study additionally provides insights on the out-
comes of business intelligence deployment within an African country context, a context
that has been very little studied. The study also provides insights for owners and man-
agers of firms by creating a clear understanding of how IOS-enabled business intelli-
gence may enhance their supply chain performance.

The rest of the paper is organized as follows. The research model and hypotheses is
presented next, followed by the methodology of the study. This is immediately followed
by a discussion of the results of the study. The study then concludes by discussing the
implications of the study for research and practice.
## 2 Research model and hypotheses

This study is underpinned by the resource-based view theory. The resource-based view theory argues that for organizations to gain competitive advantage, they must possess resources that are rare, valuable, inimitable, non-substitutable and can be deployed in manner that is difficult for competitor to duplicate (Barney, 1991; Peteraf, 1993). In this study, we argue that IOS-enabled business intelligence is an important intangible resource, that can be leveraged to obtain supply chain capabilities such as information exchange, integration, coordination and responsiveness, which ultimately help organizations to gain superior performance in the form of improved supply chain performance. The research model is presented in Figure 1 below. The hypotheses of the study are discussed next.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Research model</figcaption></figure>

### 2.1 IOS-enabled business intelligence and supply chain capabilities

IOS enabled business intelligence allows supply chain partners to work collaboratively to attain mutual benefits (Nissen et al., 2014). Additionally, the shared knowledge enabled by IOS allows effective and efficient synchronization of the supply chain processes, and joint planning and execution (Ayoub et al., 2017). Mandal and Dubey (2021) observed that using IOS for intelligence enhances IOS use for integration, collaboration, and coordination. IOS-enabled business intelligence allows organizations to jointly plan with their supply chain partners to address the needs customers in a responsive manner (Mandal and Dubey, 2021; Yu et al., 2018). Through the use of IOS, firms can derive useful insights towards improving their supply chain capabilities (Asamoah et al., 2021a; Asamoah et al., 2019). From the purview of the resource-based view
theory and the discussions above, we argue that the IOS-enabled business intelligence
is a valuable, rare and inimitable organizational resource that can be exploited by or-
ganizations to develop supply chain capabilities such as information exchange, coordi-
nation, integration, and responsiveness. Drawing from the discussion above we propose
the following hypotheses:

H1a: Higher levels of IOS-enabled business intelligence will lead to higher levels of information exchange capabilities.

H1b: Higher levels of IOS-enabled business intelligence will lead to higher levels of coordination capabilities.

H1c: Higher levels of IOS-enabled business intelligence will lead to higher levels of integration capabilities.

H2: Higher levels of IOS-enabled business intelligence will lead to higher levels of supply chain responsiveness capabilities

## 2.2 Information exchange capabilities, supply chain responsiveness and supply chain performance

Information exchange capabilities refers to the ability of an organization to share relevant information with its supply chain partners effectively and efficiently (Wu et al., 2006; Yeniyurt et al., 2019). Information exchange helps to enhance interorganizational integration and coordination between supply chain partners and gain useful insight about the business environment (Kim et al., 2018; Yu et al., 2018). Effective and efficient supply chain information exchange leads to higher levels of supply chain responsiveness (Liu et al., 2013; Yu et al., 2018). Yu et al. (2018) affirms that through information exchange, organizations are able to improve their performance. Following the discussion above, we propose that:

H3a: Information exchange capabilities is positively related to supply chain responsiveness.

H3b: Information exchange capabilities is positively related to supply chain performance.

## 2.3 Coordination capabilities, supply chain responsiveness and supply chain performance

Coordination capabilities refers to the ability of organizations to effectively and efficiently coordinate business activities with their supply chain partners (Wu et al., 2006). Supply chain coordination has been identified as a key antecedent of organizational performance (Yeniyurt et al., 2019). Kumar and Singh (2017) assert that effective supply chain coordination helps to manage interdependencies and reduce uncertainties. Improved supply chain coordination leads to benefits such as reduced transaction costs and improved operational efficiency among supply chain partners (Wu et al., 2006). A healthy supply chain coordination fosters the entire supply chain to be more responsive (Kim and Lee, 2010). Thus, it is expected that coordination capabilities will result in enhanced supply chain responsiveness and supply chain performance. Accordingly, we propose the following hypothesis:
*H4a: Coordination capabilities is positively related to supply chain responsiveness.*

*H4b: Coordination capabilities is positively related to supply chain performance.*

**2.4 Integration capabilities, supply chain responsiveness and supply chain performance**

Integration capabilities refers to the ability of organization efficiently integrate their internal and external supply chain activities. Wong et al. (2011) argues that activity integration helps to create opportunities for leveraging the knowledge embedded in collaborative processes to enhance value creation, delivery performance and reduce cost. A number of studies have emphasized that supply chain integration helps practicing firms to improve their efficiency and effectiveness (Escorcia-Caballero et al., 2019). Similarly, Danese et al. (2013) maintain that supply chain integration helps improve responsiveness. Based on the discussions above, we propose the following hypotheses:

*H5a: Integration capabilities is positively related to supply chain responsiveness.*

*H5b: Integration capabilities is positively related to supply chain performance.*

**2.5 Supply chain responsiveness and supply chain performance**

Supply chain responsiveness capabilities refers to extent to which members of a supply chain are able to cooperatively respond to changes presented by the environment (Wu et al., 2006; Yeniyurt et al., 2019). In today's market place plagued with several levels of uncertainties and competition, supply chain responsiveness becomes a very useful organizational capability (Asamoah et al., 2021b). Yu et al. (2018) found that supply chain responsiveness has a positive effect on firm financial performance. Similarly, the study of Kim and Lee (2010) found that supply chain responsiveness positively influences market performance. Drawing from the discussions above, it is conceivable that supply chain responsiveness capabilities will result in supply chain performance. Accordingly, we propose that:

*H6: Supply chain responsiveness capabilities is positively related to supply chain performance*

**2.6 Mediating role of supply chain responsiveness**

Drawing on the discussions in the previous sections, this study hypothesizes that supply chain responsiveness intervenes the direct effects of integration capabilities, coordination capabilities and information exchange capabilities on supply chain performance. Specifically, we argue that supply chain capabilities such as coordination, supply chain information exchange, and integration drives supply chain responsiveness which in turn influences firm performance. Accordingly, we postulate that:

*H7a: Supply chain responsiveness capabilities mediates the relationship between integration capabilities and supply chain performance.*

*H7b: Supply chain responsiveness capabilities mediates the relationship between information exchange capabilities and supply chain performance.*
H7c: Supply chain responsiveness capabilities mediates the relationship between coordination capabilities and supply chain performance.

# 3 Methodology

## 3.1 Measurement items development

Measurement items for the research items were sourced from existing literature with IOS-driven business intelligence sourced from Asamoah et al. (2021a) and Zhang and Cao (2018). Measures for information exchange capabilities, coordination capabilities, integration capabilities and supply chain responsiveness capabilities were sourced from Wu et al. (2006) and Asamoah et al. (2021a). The measures of supply chain performance were sourced from Koçoğlu et al. (2011) and Won Lee et al., (2007). All constructs were conceptualized at the first level, with the exception of supply chain performance, which was conceptualized as a second level construct with efficiency, flexibility and reliability as sub-dimensions. The measures were refined by three professors specializing in business intelligence and three top practitioner business intelligence experts. The measures were also subjected to pilot testing for further refinement.

## 3.2 Data collection

Data for the study was obtained from a survey of firms involved in retail trade in Ghana that use IOS. IOS, particularly vendor-managed inventory, has seen increasing adoption in the retail operations in Ghana, particularly for fast-moving consumer goods (Asamoah et al., 2021a). Five hundred firms directly involved in the wholesale and retail of fast-moving consumer goods were randomly selected from a database and targeted for data collection. Questionnaires were delivered to these organizations, together with a cover letter explaining the purpose of the study. In all, 161 usable responses were received from firms that indicated they were into wholesale and retail of fast-moving consumer goods.

# 4 Results

## 4.1 Demographic results

The demographic results revealed that largest categories of firms have been in operation for over 20 years (39.1%), had high revenues larger than 10 million Ghana cedis (31.7%), and had employee size larger than 500 (36%). This suggests IOS-enabled business intelligence is used more by larger firms, relative to smaller firms. The full demographic data is presented in Table 1 below.

<table><thead><tr><td>Years of operation</td><td>Frequency</td><td>P</td></tr></thead><tbody><tr><td>1 to 5 years</td><td>33</td><td>2</td></tr><tr><td>6</td><td>2</td><td>15.5</td></tr></tbody></table>
<table><thead><tr><th>11 to 15 years</th><th>20</th><th>12.4</th></tr></thead><tbody><tr><td>10 to 50</td><td>42</td><td>2</td></tr><tr><td>51 to 500</td><td>55</td><td>3</td></tr><tr><td>More than 500</td><td>58</td><td>3</td></tr><tr><td>Total</td><td>161</td><td>1</td></tr></tbody></table>

## 4.2 Measurement model results

Measurement model results were assessed by following the guidelines of Hair et al (2019). Indicator loadings were found to be less than 0.708 as required, indicating good item reliability. Composite reliability values were greater than 0.7, indicating internal consistency reliability of the research constructs. The average variance extracted (AVE) values were larger than 0.5, indicating acceptable convergent validity of the model. These results are summarized in Table 2 below.

<table><caption>**Table 2.** Psychometric properties of research constructs</caption><thead><tr><th>Constructs</th><th>Composite Reliability</th><th>AVE</th></tr></thead><tbody><tr><td>Coordination Capabilities (CORD)</td><td>0.916</td><td>0.685</td></tr><tr><td>Efficiency (EFF)</td><td>0.930</td><td>0.767</td></tr><tr><td>Flexibility (FLEX)</td><td>0.925</td><td>0.712</td></tr><tr><td>Information Exchange Capabilities (INFX)</td><td>0.930</td><td>0.768</td></tr><tr><td>Integration Capabilities (INTG)</td><td>0.931</td><td>0.773</td></tr><tr><td>IOS-enabled business intelligence (INTL)</td><td>0.914</td><td>0.726</td></tr><tr><td>Reliability (REL)</td><td>0.927</td><td>0.718</td></tr><tr><td>Supply Chain Responsiveness (RESP)</td><td>0.905</td><td>0.703</td></tr></tbody></table>

In establishing discriminant validity, we used the Fornell-Larcker criterion. Comparing the square root of the AVE of each construct with the correlation between constructs and confirming if the former is larger than the latter establishes discriminant validity (Hair et al., 2019). In Table 3 below, the diagonal values in bold which represent the square root of the AVE of constructs are larger than the off-diagonal values which are correlations among constructs, establishing discriminant validity.

<table><caption>**Table 3.** Fornell-Larcker test results</caption><thead><tr><th></th><th>CORD</th><th>EFF</th><th>FLEX</th><th>INFX</th><th>INTG</th><th>INTL</th><th>REL</th><th>RESP</th></tr></thead><tbody><tr><th>CORD</th><td><strong>0.828</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>EFF</th><td>0.672</td><td><strong>0.876</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>FLEX</th><td>0.614</td><td>0.708</td><td><strong>0.844</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>INFX</th><td>0.781</td><td>0.664</td><td>0.656</td><td><strong>0.876</strong></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>INTG</td><td>0.638</td><td>0.530</td><td>0.492</td><td>0.615</td><td><strong>0.879</strong></td><td></td><td></td></tr><tr><td>INTL</td><td>0.634</td><td>0.636</td><td>0.645</td><td>0.672</td><td>0.594</td><td><strong>0.852</strong></td><td></td></tr><tr><td>REL</td><td>0.668</td><td>0.702</td><td>0.701</td><td>0.707</td><td>0.627</td><td>0.664</td><td><strong>0.848</strong></td></tr><tr><td>RESP</td><td>0.717</td><td>0.663</td><td>0.697</td><td>0.765</td><td>0.712</td><td>0.766</td><td>0.726</td><td><strong>0.839</strong></td></tr></table>

The discriminant validity of the construct was also assessed using the heterotrait-monotrait (HTMT) ratio of the correlations test, which indicates that HTMT values should be less than 0.9 (Henseler et al., 2015). The HTMT values were within this range, confirming the discriminant validity of the model. The HTMT results are presented in Table 4 below.

<table><caption>**Table 4.** HTMT results</caption><thead><tr><th></th><th>CORD</th><th>EFF</th><th>FLEX</th><th>INFX</th><th>INTG</th><th>INTL</th><th>REL</th><th>RESP</th></tr></thead><tbody><tr><th>CORD</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>EFF</th><td>0.748</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>FLEX</th><td>0.678</td><td>0.786</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>INFX</th><td>0.867</td><td>0.738</td><td>0.725</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>INTG</th><td>0.709</td><td>0.587</td><td>0.545</td><td>0.682</td><td></td><td></td><td></td><td></td></tr><tr><th>INTL</th><td>0.717</td><td>0.716</td><td>0.725</td><td>0.757</td><td>0.669</td><td></td><td></td><td></td></tr><tr><th>REL</th><td>0.741</td><td>0.782</td><td>0.776</td><td>0.786</td><td>0.696</td><td>0.747</td><td></td><td></td></tr><tr><th>RESP</th><td>0.816</td><td>0.753</td><td>0.791</td><td>0.868</td><td>0.810</td><td>0.883</td><td>0.825</td><td></td></tr></tbody></table>

## 4.3 Structural model results

Having established the validity of the measurement model, the structural model was assessed. The model's in-sample explanatory power was assessed by examining the R² of the endogenous variables, which ranged from a moderate level of 0.353 for integration capabilities to a high level of 0.749 for supply chain responsiveness. The R² values are presented inside the endogenous constructs in Figure 2 below. The predictive relevance of the model was also established since Q² values ranged from 0.260 to 0.596, which are significantly larger than zero (Hair et al., 2019).

To ascertain whether the direct effects were supported, the path co-efficient and p-values of the hypothesized direct paths were examined. The mediation paths were assessed by following the procedure outlined by Nitzl et al. (2016). The results of the hypotheses tests are summarized in Figure 2 below.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Structural model results</figcaption></figure>

## 4.4 Discussions

The study set out to examine whether and how IOS-enabled business intelligence en-
hances the supply chain performance of firms. The findings of the study confirmed that
IOS-enabled business intelligence first allowed organizations to obtain information ex-
change, coordination and integration capabilities as hypothesized. Additionally, IOS-
enabled business intelligence enhanced the supply chain responsiveness capabilities of
firms. These findings are in line with the results of previous studies that observed that
use of IOS in general enhances supply chain management capabilities in general
(Agbenyo et al. 2018; Asamoah et al., 2019; Asamoah et al., 2021a). Whilst the specific
effect of IOS-enabled business intelligence on these capabilities have not been studied
in previous studies, the study of Mandal and Dubey (2021) can serve as a useful refer-
ence. They observed that IOS use for intelligence will occasion higher levels of IOS
use for integration, collaboration, and coordination. Beyond these findings, the present
study explores the interrelationship between supply chain management capabilities. It
was revealed that information exchange capabilities and integration capabilities can be
leveraged to achieve higher supply chain responsiveness. This suggests that IOS-
enabled business intelligence occasions a staggered improvement in supply chain man-
agement capabilities. Coordination capabilities however were not associated with
higher supply chain responsiveness.

Regarding the effect of supply chain capabilities, it was observed that information
exchange, coordination and supply chain responsiveness capabilities all resulted in
higher supply chain performance. Integration capabilities however did not enhance
supply chain performance. This mirrors the findings of Yu et al. (2018) that observed
that data driven supply chains enabled information exchange, coordination, activity in-
tegration and responsiveness capabilities, and that all these supply chain capabilities
with the exception of activity integration enhanced financial performance. The different
ways in which supply chain capabilities impact on supply chain performance highlight
the need for researchers to explore into detail how the individual dimensions of supply
chain capabilities are influenced by IOS and they in turn influence supply chain perfor-
mance.

Examining the mediation role of supply chain responsiveness in the effect of infor-
mation exchange, coordination and integration capabilities on supply chain perfor-
mance revealed interesting findings. A partial mediation role of supply chain respon-
siveness was observed for the effect of information exchange capabilities on supply
chain performance. This means that information exchange capabilities partly enhance
supply chain performance directly, and partly enhance it through enhanced supply chain
responsiveness. The effect of integration capabilities on supply chain performance on
the other hand was fully mediated through supply chain responsiveness, since the direct
effect of integration capabilities on supply chain performance was not significant, but
the mediation effect was positive and significant. Finally, examining the mediation role
of supply chain responsiveness on how coordination capabilities enhance supply chain
performance reveals that there was no mediating role of supply chain responsiveness.
This indicates that supply chain responsiveness does not intervene the relationship be-
tween coordination capabilities and supply chain performance.

5 Conclusion

The study examined into detail the mechanisms through which IOS-enabled business
intelligence enhances the supply chain performance of firms by proposing that supply
chain capabilities play an important, individual and staggered role in explaining this
relationship. The findings of the study largely support the assertions of the study, with
IOS-enabled business intelligence enhancing all four identified supply chain manage-
ment capabilities, and three of the capabilities having a significant effect on supply
chain responsiveness and performance.

There are some implications of the study. The study provides a detailed understand-
ing of how IOS-enabled business intelligence enhances supply chain performance, by
bringing to light the role of that supply chain management capabilities in enhancing
supply chain performance. The study also foments an understanding in the interrela-
tionships between supply chain capabilities that can guide future researchers who are
studying supply chain capabilities. The study also adds up to the nascent studies on
business intelligence within the African context. By way of implications for practice,
the findings of the study confirm that positive outcomes in terms of improved supply
chain management capabilities and improved supply chain performance can be ob-
tained from IOS-enabled business intelligence initiatives. Business owners and manag-
ers can therefore invest in IOS to enhance their performance outcomes. The study also
highlights the important role of supply chain management capabilities in achieving suc-
cessful supply chain performance outcomes from IOS-enabled business intelligence.

There are some limitations of the study. Data was obtained from firms in the retail
sector of Ghana, an emerging African country, and it is possible that the findings may
not apply to firms in other industries or in other geographic contexts. Future research
can consider expanding the scope of the study to examine other industries, examine
similar relationships in other contexts, and explore possible constructs enhance the ef-
fect of IOS-enabled business intelligence on performance outcomes.

References

Agbenyo, L., Asamoah, D., & Agyei-Owusu, B. (2018). Drivers and effects of Inter-Organizational Systems (IOS) use in a developing country. AMCIS 2018 Proceedings.

Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2019). Effect of Inter-Organizational Systems use on supply chain capabilities and performance. In Y. Dwivedi, E. Ayaburi, R. Boateng, & J. Effah (Eds.). ICT unbounded, social impact of bright ICT adoption (pp. 293–308). Springer International Publishing.

Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2021a). Inter-organizational systems use and supply chain performance: Mediating role of supply chain management capabilities. *International Journal of Information Management*, 58, 102195.

Asamoah, D., Nuertey, D., Agyei-Owusu, B., & Akyeh, J. (2021b). The effect of supply chain responsiveness on customer development. *The International Journal of Logistics Management*.

Ayoub, H. F., Abdallah, A. B., & Suifan, T. S. (2017). The effect of supply chain integration on technical innovation in Jordan. *Benchmarking: An International Journal*.

Barney, J. (1991). Firm resources and sustained competitive advantage. *Journal of management*, 17(1), 99-120.

Cho, B., Ryoo, S. Y., & Kim, K. K. (2017). Interorganizational dependence, information transparency in interorganizational information systems, and supply chain performance. *European Journal of Information Systems*, 26(2), 185-205.

Danese, P., Romano, P., & Formentini, M. (2013). The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network. *Transportation Research Part E: Logistics and Transportation Review*, 49(1), 125-140.

Escorcia-Caballero, J. P., Moreno-Luzon, M. D., & Chams-Anturi, O. (2019). Supply chain integration capability: An organizational routine perspective, 8(5), 39-47

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. *European Business Review*, 31(1), 2–24

Hartono, E., Li, X., Na, K. S., & Simpson, J. T. (2010). The role of the quality of shared information in interorganizational systems use. *International Journal of Information Management*, 30(5), 399-407.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. *Journal of the Academy of Marketing Science, 43*(1), 115–135

Kim, D., & Lee, R. P. (2010). Systems collaboration and strategic collaboration: their impacts on supply chain responsiveness and market performance. *Decision Sciences, 41*(4), 955-981.

Kim, D., Jean, R. J. B., & Sinkovics, R. R. (2018). Drivers of virtual inter-firm integration and its impact on performance in international customer-supplier relationships. *Management International Review, 58*(3), 495–522

Kocoğlu, İ., İmamoğlu, S. Z., İnce, H., & Keskin, H. (2011). The effect of supply chain integration on information sharing: Enhancing the supply chain performance. *Procedia - Social and Behavioral Sciences, 24*, 1630–1649

Kumar, R., & Singh, R. K. (2017). Coordination and responsiveness issues in SME supply chains: a review. *Benchmarking: An International Journal*.

Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. *Decision support systems, 54*(3), 1452-1462.

Mandal, S., & Dubey, R. K. (2021). Effect of inter-organizational systems appropri-
ation in agility and resilience development: an empirical investigation. *Benchmarking:
An International Journal*.

Nissen, H. A., Evald, M. R., & Clarke, A. H. (2014). Knowledge sharing in hetero-
geneous teams through collaboration and cooperation: Exemplified through Public-
Private-Innovation partnerships. *Industrial Marketing Management, 43*(3), 473-482

Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling. *Industrial Management & Data Systems, 116*(9), 1849–1864.

Peteraf, M. A. (1993). The cornerstones of competitive advantage: a resource-based view. *Strategic Management Journal, 14*(3), 179-191.

Rajaguru, R., & Matanda, M. J. (2013). Effects of inter-organizational compatibility on supply chain capabilities: Exploring the mediating role of inter-organizational information systems (IOIS) integration. *Industrial Marketing Management, 42*(4), 620–632.

Subramani, M., 2004. How do suppliers benefit from information technology use in
supply chain relationships? MIS Q. 28 (1), 45–73.

Won Lee, C., Kwon, I. G., & Severance, D. (2007). Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer. *Supply Chain Management: An International Journal, 12*(6), 444–452.

Wong, C. Y., Boon-Itt, S., & Wong, C. W. (2011). The contingency effects of envi-
ronmental uncertainty on the relationship between supply chain integration and opera-
tional performance. *Journal of Operations Management, 29*(6), 604-615.

Wu, F., Yeniyurt, S., Kim, D., & Cavusgil, S. T. (2006). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. *Industrial Marketing Management, 35*(4), 493-504.

Yeniyurt, S., Wu, F., Kim, D., & Cavusgil, S. T. (2019). Information technology resources, innovativeness, and supply chain capabilities as drivers of business performance: A retrospective and future research directions. *Industrial Marketing Management, 79*, 46-52.
Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. *Transportation Research Part E: logistics and transportation review, 114*, 371-385.

Zhang, Q., & Cao, M. (2018). Exploring antecedents of supply chain collaboration: Effects of culture and interorganizational system appropriation. *International journal of Production economics, 195*, 146-157.