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Abstract

We develop and test a moderated mediation model to explain how management accounting systems impact supply chain integration and firm performance, and how perceived environmental uncertainty influences these relationships. Partial least squares structural equation modeling was used to analyze the survey data from 158 supply chain firms in Vietnam. Results indicate that the use of management accounting systems increases the extent of supply chain integration, which in turn enhances firm performance. Furthermore, perceived environmental uncertainty moderates the positive associations between the use of management accounting systems, supply chain integration, and firm performance. This study makes several contributions to existing literature. First, it elucidates the role of supply chain integration in promoting partner integration in the Vietnamese context. Second, it reveals the performance implications of the interaction between management accounting systems and supply chain integration. Our findings have important implications for supply chain management because they emphasize the need to develop effective management accounting systems and supply chain integration strategies in the context of environmental uncertainty.

Plain Language Summary

We looked at how Vietnamese companies can benefit from using management accounting systems and integrating their supply chains, especially when facing uncertain business environments. We surveyed 158 supply chain firms in Vietnam and used a statistical method called partial least squares structural equation modeling to analyze the data. Our results show that when companies use management accounting systems, it helps them integrate their supply chains better, which then improves their overall performance. We also found that the level of uncertainty in the business environment affects how these factors work together. This study is important because it sheds light on how supply chain integration can strengthen partnerships among companies in Vietnam and how combining management accounting systems with supply chain integration can boost performance. These findings suggest that companies in Vietnam should focus on developing effective management accounting systems and strategies for integrating their supply chains, especially when dealing with uncertain business conditions.

Keywords

supply chain integration management accounting systems perceived environmental uncertainty firm performance Vietnam

Introduction

The COVID-19 pandemic has profoundly impacted the economies of countries and international trade. Particularly, it has prompted countries to implement lockdown measures aimed at mitigating the transmission of the infection (Butt, 2022). These measures have significantly disrupted the domestic and global supply chains (Mahajan & Tomar, 2021). Economies have experienced supply chain stoppages across industries; some non-essential sectors are yet to fully recover (Mollenkopf et al., 2020; Papadopoulos et al., 2020). Pedroso et al. (2020) argue that businesses worldwide are increasingly facing major challenges, including the uncertainty of the external environment.

When businesses face high levels of environmental uncertainty, their management control systems must be adaptable and flexible to effectively coordinate operations during unforeseen events (Eldridge et al., 2013). The main contributors to environmental unpredictability are the emergence of the global economy, intensifying corporate competitiveness, and the shift toward delegating tasks to the supply chain instead of controlling every aspect of the internal value chain (Otley, 2016). Therefore, businesses must make proactive internal and external decisions (Petcharat et al., 2012) and respond quickly to market changes. An integration strategy among businesses, suppliers, and customers in the supply chain is crucial for responding to market changes (Kim, 2009; Velayutham et al., 2021). Business-supplier and customer relationships, in addition to information management and lean operations within the framework of supply chain management, positively affect management accounting systems (MAS) (Mohd-Jamal & Tayles, 2014). Consequently, businesses require integration to improve the usefulness of management accounting information and manage operations effectively by increasing perceived environmental uncertainty (PEU) (Velayutham et al., 2021).

Besides enhancing integration with supply chain partners, businesses must employ appropriate management accounting techniques to reduce supply chain management costs for a chain to function effectively (Pradhan et al., 2018). MAS provides appropriate techniques for calculating the cost of supply chain activities and crucial data to support inter-organizational cooperation, control, and decision-making to enhance efficiency in supply chain processes (Almatarneh et al., 2022; Hasyim & Jabid, 2019). Integrating MAS with the supply chain enables the timely delivery of dynamic financial and accounting information on supply chain activities (Velayutham et al., 2021). Therefore, MAS information is expected to support decision-making processes, control costs actively, promote linkages between partners in the supply chain, and enhance chain efficiency.

Additionally, some Western academics concluded that PEU positively influences the utilization of accounting information and supply chain strategies (e.g., Agbejule & Burrowes, 2007; Pradhan et al., 2018; Velayutham et al., 2021). Hasyim and Jabid (2019) demonstrate that enterprise size, product diversification, competition level in the business environment, and government regulatory obligations promote supply chain integration (SCI) with MAS. SCI is a collection of actions associated with the rigid organization and coordination of product movement within the supply chain framework (Som & Anyigba, 2022). The intersection of management accounting and supply chain has recently attracted the interest of several academics in emerging Asian economies such as Malaysia, Indonesia, and Vietnam. Doktoralina and Apollo (2019) examined the positive impacts of strategic MAS on supply chain practice outcomes. Le (2020) asserted that environmental MAS facilitates the implementation of sustainable supply chain management. Nartey et al. (2020) examine the moderating role of SCI in the association between MAS usage and supply chain performance in Ghanaian hospitals. However, Nartey et al. (2020) considered only the importance of SCI aspects in designing MAS following information exchange requirements, enhancing cohesion, and building trust among partners. Thus, previous studies have not discussed the practical benefits of the positive interaction between SCI and MAS information in an uncertain environment.

However, the COVID-19 pandemic has resulted in numerous risks and increased uncertainty in the business environment, as well as disrupted segments of purchasing, manufacturing, logistics, distribution, and exchange demand (Hongdao et al., 2022; Velayutham et al., 2021). Velayutham et al. (2021) also demonstrated that MAS information could assist managers in overcoming these limitations, thereby better preparing businesses for future disruptions. Under such circumstances, MAS assists managers in optimal planning, budgeting, and predicting the future (Massicotte & Henri, 2021). Reliable, sophisticated, and timely information on the externally uncertain environment compiled and integrated by the MAS, along with good SCI practices, can assist businesses in enhancing operational performance. There is a gap in the literature regarding the examination of the potential mediating effect of SCI on the pathway from MAS use to firm performance, along with the moderating effects of PEU on the relationships between MAS use, SCI, and firm performance. This gap is significant for enhancing our understanding of the mediating and moderating effects, particularly for supply chain businesses operating in emerging markets with SCI issues, such as limited technological capabilities and fragmented supplier and customer bases (Duong & Ha, 2021; Kumar, Chibuzo, et al., 2017). This understanding can aid firms in leveraging MAS to enhance performance within uncertain and disruptive environments, ultimately fostering greater resilience and success.

To address this gap in research, we have formulated and tested a model that combines the resource-based view (RBV) (Barney, 1991) and contingency theory (Otley, 1980). The objective of this model is twofold: (1) to examine the impact of MAS utilization on firm performance via SCI, and (2) to investigate how PEU moderates the relationships between MAS utilization, SCI, and firm performance. In doing so, our study adds to the existing knowledge by confirming the mediating role of SCI in the link between MAS utilization and firm performance, as well as the moderating role of PEU in Vietnamese supply chain companies. Specifically, we explore the effects of MAS utilization on SCI and firm performance both with and without the presence of an uncertain environment.

The structure of this paper is as follows: In the subsequent section, we outline the construction of a theoretical model that draws upon the foundational theories of RBV and contingency theory. This model focuses on the connections between MAS, SCI, and firm performance, specifically emphasizing the moderating influence of PEU. Next, we present the research methodology and highlight the significant findings. We then proceed to discuss the theoretical and practical implications of our study. Lastly, we conclude with a section that acknowledges the limitations of our study and proposes potential avenues for future research.

Model and Hypothesis Development

The Relationship Between MAS Use and SCI

MAS provides the information required internally by the company and at the inter-enterprise level (Taschner & Charifzadeh, 2020). Sharing information among partners in the supply chain is essential for fulfilling customer demand (Gunasekaran et al., 2008). MAS information satisfies four criteria–broad scope, timeliness, aggregation, and integration (Chenhall & Morris, 1986), enabling businesses to respond quickly to competitive pressure, enhance product/service quality, and increase customer value (Chenhall & Moers, 2015). Moreover, MAS information for businesses must meet stakeholder requirements regarding reliability and precision (Lassou et al., 2021), with SCI being no exception. The SCI process connects businesses by sharing information and resources from suppliers to businesses, and ultimately to customers (W. Yu et al., 2013). Accurate, compliant, and timely sharing of MAS information in the supply chain can reduce inventory, extend product life cycles, and enhance production flexibility (Flynn et al., 2016). In addition, timely and accurate MAS information can optimize the allocation of supply chain costs among customers, suppliers, and partners. This can enhance the efficiency and effectiveness of a supply chain (Dekker & Van Goor, 2000; Mohd-Jamal & Tayles, 2014). According to Ramos (2004), MAS information is an integral part of control mechanisms used to plan, measure, and assess the various activities within the supply chain.

Furthermore, MAS information assists supply chain partners in negotiating product or service characteristics to meet planned cost targets (Ramos, 2004). This information also facilitates the coordination of relationships, improvement of interactions, and trust development among supply chain partners (Pradhan et al., 2018). Therefore, MAS information is crucial for strategic decisions regarding supply chain network design and value creation (Velayutham et al., 2021). Increased use of MAS information improves supply chain performance by fostering cooperation between partners and facilitating supply chain activities (Pradhan et al., 2018). As the need for information supporting SCI grows, so does the use of MAS, and, consequently, the scope of SCI. Based on the preceding discussion, we put forward the following hypothesis:

H1. MAS use positively affects SCI.

The Mediating Role of SCI

MAS is a management system that provides managers with financial and non-financial data regarding internal business situations, markets, suppliers, customers, and competitors (Chenhall & Morris, 1986). It is based on four dimensions: (1) broad scope, (2) timeliness, (3) aggregation, and (4) integration (Chenhall & Morris, 1986). It helps businesses identify opportunities to increase customer value, thereby increasing the retention rate of existing customers and market share, and serving as a solid foundation for bringing in new customers and returning long-term profits. According to Cadez and Guilding (2012), businesses can improve performance by utilizing strategic MAS information. The increased use of MAS data directly affects the business performance of organizations (Hutahayan, 2020; Ismail et al., 2017; N. P. Nguyen, 2018; Sinaga et al., 2019; Tu and Nguyen, 2021).

From the perspective of RBV, we posit that MAS possesses the attributes of VRIN (i.e., valuable, rare, inimitable, and non-substitutable) as outlined by Barney (1991). More specifically, the functional components of the MAS are readily available and have the ability to convert the data provided by the MAS into tangible benefits, thereby demonstrating their “value.” The establishment and effective functioning of the MAS depend on various factors, including the business environment, competitive pressures, resource availability, organizational structure, and company size. Consequently, the information and output of MAS in firms are distinct, which is “rare.” Each firm develops an “inimitable” MAS tailored to its management requirements. MAS is deemed “non-substitutable” because it is virtually the only way for businesses to collect, store, process, distribute, and control information, directly influencing managers’ decision-making (Chenhall & Morris, 1986). Thus, the more frequently businesses use MAS information, the more likely they are to achieve positive business results (N. P. Nguyen, 2018; Tu and Nguyen, 2021). Based on the previous arguments supported by the RBV, we propose the following hypothesis:

H2. MAS use positively affects firm performance.

Many of the issues businesses face today, including limitations in the production-service phase, product quality, transportation, and high costs, stem from their inability to effectively integrate the internal and external SCs (Flynn & Flynn, 1999; Rosenzweig et al., 2003). In terms of cost, quality, and production cycle time, the integration of MAS between manufacturers and supply chain partners has produced favorable outcomes (Flynn et al., 2010; Kim, 2009; Maiga, 2017). Additionally, as supply chain members become more integrated, they will likely be compelled to share sales forecasting, production planning, order tracking and tracing, delivery status, and inventory volume data. Consequently, supply chain activities rely on long-term contracts, revenue, risk-sharing, and risk management (Duong & Ha, 2021; Shou et al., 2018). Based on the enterprise resource basis theory (Barney, 1991), this study argues that SCI fulfills VRIN requirements. Developing VRIN resources is essential for businesses seeking competitive advantage, as they must pay greater attention to VRIN conditions. Specifically, (1) through SCI, businesses can minimize weaknesses in the process of research, production, circulation, and distribution of products, thereby operating more efficiently; (2) rarity is reflected in differences in resources and capacity; thus, the input, information, and performance of SCI in each enterprise are unique; and (3) SCI is inimitable because each enterprise has an associated SCI. By expanding SCI, businesses can identify and eliminate non-value-added activities and improve product quality and delivery readiness, thus laying the groundwork for sales growth (Kim, 2009; Rosenzweig et al., 2003). Based on these arguments, we hypothesize the following:

H3. SCI positively affects firm performance.

Hypotheses H1, H2, and H3 suggest that SCI plays a mediating role in the connection between the utilization of MAS and firm performance, as outlined below.

H4. SCI mediates the relationship between MAS use and firm performance.

The Moderating Role of PEU

To survive and thrive in a competitive environment, businesses must constantly adapt to challenging realities (Pedroso et al., 2020). In order to maintain competitiveness and generate satisfactory financial returns for the business and its shareholders, managers must prioritize their capacity to adapt to ongoing and unforeseen changes. Competitiveness and success are defined as the capacity to respond quickly and effectively to customer needs (W. Yu et al., 2013). In this context, MAS data can assist businesses in navigating the challenges of competitive markets (Bromwich, 1990; Pasch, 2019). MAS information focuses on enhancing value added by companies to outperform rivals and assist managers in monitoring activities in this volatile market (Hariyati et al., 2019).

Conversely, a dynamic business environment makes it highly challenging for managers to identify threats and opportunities (C. Yu et al., 2016). From that point onward, identifying competitors becomes more difficult, time- and cost-intensive. The inability to identify competitors can create competitive blind spots, allowing key competitors to enter and dominate the company’s market, thereby reducing revenue (C. Yu et al., 2016). According to contingency theory (Otley, 1980), the impact of environmental factors on businesses may result from differences in MAS data utilization. As PEU rises, managers utilize MAS more frequently to address issues related to competition and fluctuating market demand (Agbejule, 2005; Gul, 1991).

Moreover, the composition of the accounting system must be closely related to the enterprise’s structure and operating context (Otley, 1980), and firm performance depends on its compatibility with contextual factors, such as the environment of business, strategy, scale, technology, and culture (Chenhall & Langfield-Smith, 2007). From the context theory perspective, this study argues that businesses must organize and customize their MAS to suit their structure and operating context. Accurate, consistent, and timely MAS information assists managers in making supply chain-related decisions (Doktoralina & Apollo, 2019). Therefore, increasing the MAS in uncertain contexts will aid administrators in developing optimal performance-enhancing solutions (Alaeddin et al., 2019). Based on previous arguments driven by the contingency theory, we propose the following hypothesis:

H5. PEU positively moderates the effect of MAS use on firm performance.

In uncertain environments, companies greatly value the quality and efficacy of supply chain relationships based on interaction, cooperation, commitment, and trust (Flynn et al., 2016; Wong et al., 2011). Uncertainty has compelled managers to establish favorable conditions for successfully implementing governance systems and adapting supply chain strategies. The increased use of MAS assists businesses in establishing trust and strengthening relationships with suppliers, thereby enhancing the supply chain management process (Agbejule, 2005). Additionally, the degree of uncertainty encountered by SCs varies (Flynn et al., 2016). Therefore, businesses that succeed in SCI must effectively utilize MAS information, which will increase their competitive advantage and adapt them to changing customer demands and new development trends. Information from MAS aids businesses in constructing more robust and resilient SCs, allowing them to recover from significant disruptions more quickly and improve supply chain value creation (Velayutham et al., 2021).

Based on contingency theory, we argue that a successful SCI strategy depends on the active sharing of information and resources among supply chain partners and the environmental compatibility of the business. When the PEU is high, MAS information can assist businesses in identifying key supply chain factors and facilitating alignment (Pradhan et al., 2018). Moreover, in an uncertain environment, MAS information helps managers quickly identify problems in the supply chain, enabling them to provide optimal operational solutions and enhance environmental compatibility (Velayutham et al., 2021). Strengthening SCI is predicated on sharing information, vision, strategies, and activities among member enterprises in the same direction (Hult et al., 2004), thereby assisting them (Flynn et al., 2016). This demonstrates that managers with a greater perception of environmental instability tend to increase the use of MAS information to maintain beneficial cooperation with supply chain partners, thereby consolidating the scope of SCI. Consequently, we propose the following hypothesis:

H6. PEU positively moderates the effect of MAS use on the SCI.

In a dynamic and uncertain environment, managers are frequently under pressure to maintain the effectiveness of their business operations, including all supply chain operations. To cope with PEU and increase SCI intensity, managers must promote positive interactions with supply chain partners (Bae, 2017; Flynn et al., 2010). As SCI is essential for effective information exchange and the development of end-to-end business processes, managers must pay increasing attention to supply chain-related activities as PEU increases (Chavez et al., 2017). However, according to Otley (2016), businesses operating in competitive environments find it difficult to accurately predict when competitors’ actions and environmental changes occur. Hence, these organizations rely on suppliers and clients. Consequently, SCI must be more adaptable (Datta, 2017), and supply chain activities must be adapted to deal with potentially hazardous situations in an uncertain environment. Flynn et al. (2016) stated that when customers need to change frequently, and market competition is high, businesses must strengthen their internal integration and supplier support to address these issues in product development, quality, cost, and service.

Additionally, enhanced SCI can enhance cost, quality, variety, and service-level performance (Narasimhan et al., 2008; Wong et al., 2011). Under the increasing pressure of PEU, enterprises strive to integrate internally and externally through coordinated information and resource sharing, with sufficient capacity to maintain operational efficiency and increase performance if they integrate only internally. Consequently, these problems can be resolved if businesses increase their integration with suppliers and customers, which accelerates product development, marketing, procurement, manufacturing, and logistics, reduces inventory and costs, and ultimately increases productivity (Marodin et al., 2017; Wong et al., 2011). Greater PEU puts more pressure on businesses to leverage robust SCI to maintain operational stability and enhance performance. Therefore, we propose the following hypothesis:

H7. PEU positively moderates the effect of SCI on firm performance.

Figure 1 illustrates the proposed model and hypotheses.

Figure 1.

Proposed model and hypotheses.

Methods

Research Site

We chose Vietnam as the research site for several reasons. First, Vietnam has become a significant player in global supply chains owing to favorable government policies, low-cost labor, its strategic location as a bridge between Northeast and Southeast Asia, and other contributing factors (Do, 2022). Second, studying the supply chain practices of Vietnamese companies offers valuable insights into the unique challenges and opportunities faced by emerging markets (H. T. K. Nguyen et al., 2019). Finally, the COVID-19 pandemic has exposed the vulnerabilities of global supply chains, leading to production and transportation disruptions, and affecting Vietnam’s economy. A report by the General Statistics Office of Vietnam (GSO) showed that 85.7% of businesses were negatively impacted (GSO, 2020). The pandemic also affected Vietnam’s supply chain, national consumption, investment, and economic cooperation with international partners (Chong et al., 2021). Businesses experienced material shortages, and domestic and international markets contracted, leading to production stagnation and insolvency (Delteil et al., 2020). To address these issues, Vietnamese businesses must use MAS information and the SCI to improve their performance. These factors indicate that Vietnam is a favorable site for researching supply chain firms and their utilization of MAS to enhance performance under the boundary condition of PEU.

Measures of Constructs

Our study’s primary constructs were measured using well-established scales. Specifically, the extent of MAS use was measured using the Chenhall and Morris (1986) scale modified by Agbejule (2005). The extent of MAS use was a multidimensional scale comprising four dimensions: (1) broad scope, (2) timeliness, (3) aggregation, and (4) integration, all of which were measured as reflective scales associated with the 15 observed variables. Observed variables were measured using a seven-point Likert scale. Respondents were asked to rate the extent of MAS use on four dimensions ranging from 0 = “Not at all” to 7 = “Very High” when completing the survey.

The scale used to measure SCI is adopted from Kim’s (2009) work. It considers SCI as a second-order construct comprising three dimensions: (1) supplier integration, (2) internal integration, and (3) customer integration, all of which are related to the 21 observed variables. The component variable measured was a reflective scale. This scale is determined by respondents’ evaluations of the degree of integration between their supply chain and internal departments, suppliers, and customers. Observed variables are measured with a seven-point Likert scale ranging from 1 = “Very low” to 7 = “Very high.”

The PEU variable is measured by managers’ perceptions of the business environment’s five aspects of change: the degree to which (1) products and services become obsolete, (2) marketing methods change, (3) customer preferences and tastes change, (4) competitor actions and (5) the company itself changes. This scale is determined by managers’ perceptions of business environment changes and has been adopted in recent studies (e.g., Agbejule, 2005). Observed variables are measured with a seven-point Likert scale ranging from 1 = “Very slow” to 7 = “Very fast.” This scale was selected because, according to Agbejule (2005), PEU is expressed through the five aforementioned aspects that most managers in most fields encounter in their business environments.

Firm performance is a dependent variable measured by comparing managers’ perceptions of their business performance to those of competitors over the past 3 years using five observed variables. To increase the objectivity of this variable’s measurement, the authors combined two scales inherited from Jaworski and Kohli (1993) and Calantone et al. (2002) regarding financial performance. Specifically, the scale evaluates a business’s performance based on return on assets (ROA), return on sales (ROS), return on investment (ROI), business speed, and profitability. On a 5-point Likert scale, growth in total revenue and profit ranges from 1 = “Much worse” to 5 = “Much better.” Consistent with previous research (e.g., Adomako & Nguyen, 2020; Dardati & Saygili, 2020), we incorporated firm size (measured by assets and full-time equivalent employees), firm age, and ownership structure as control variables in order to account for their potential influence on firms’ environmental strategies and performance.

Sampling

To collect data, we obtained a list of potential respondents’ email addresses from LinkedIn, which is a professional network with many advantages, such as being relatively easy to access, permitting the collection of large volumes of reliable data (Gorbacheva et al., 2016; Stokes et al., 2019), requiring low costs for data collection, and facilitating direct interaction. We sent 7,000 emails containing survey links to potential respondents between early October 2020 and mid-April 2021, using SurveyMonkey. To enhance the response rate, we sent reminder letters 3 weeks later to those who did not respond. After the survey was completed, we received 226 responses, of which 68 were invalid. The final sample of 158 valid responses comprised managers who met the following criteria: (1) being middle-level managers and above who work for a company with an established supply chain; (2) having at least 1 year of experience in two areas of MAS and supply chain; and (3) being in charge of the following departments: marketing, finance/accounting, research and development, sales, and production, because these departments utilize management accounting information and are directly associated with the supply chain. These requirements are the bare minimum to ensure that survey respondents are knowledgeable about the research issues and to enhance the reliability and objectivity of the responses. Given that the unit of analysis was at the firm level, we scanned the responses to ensure that each firm had only one response and no exceptions were noted.

As the response rate for the total number of invitations to participate in the survey was less than 10%, it was necessary to test for non-response bias following the procedure suggested by Armstrong and Overton (1977). The authors compared the values of the model variables between the group with 25% of the earliest respondents and the group with 25% of the most recent respondents. The results indicated no difference between the two groups in terms of the main variables. Therefore, it can be concluded that there was low-risk non-response bias in our study.

Table 1 presents the demographic information of the respondents and their firms. The number of senior managers did not significantly exceed that of middle managers, accounting for 58.2% and 42.8% of the sample, respectively. The majority of working years are in the group of more than 9 years, which accounts for the most significant proportion (57%), and a large proportion hold a bachelor’s degree (60%). Most respondents were between 30 and 50 years of age (78.5%). Participants were predominantly men (86.1 %). Regarding the business sector, the industry groups in this survey sample did not differ significantly, except for multi-sector business groups, which comprised 26.6% of the sample. The processing and manufacturing industries had the highest percentage (12%), followed by transportation and warehousing (8.8%). The highest proportion of total assets and capital was greater than 1 trillion dollars (28.5%). Additionally, the number of small-scale enterprises with a revenue of 10 billion or more accounts for a relatively high percentage (17.1%), while the remaining groups account for the majority, ranging from 10.1% to 14.5%.

Table 1.

Demographic Information (n = 158).

	n	%
Position
Top manager	92	58.2
Mid-level managers	66	41.8
Tenure
1 to <5 years	37	23.4
5–9 years	31	19.6
>9 years	90	57.0
Firm size (employees)
≤50	57	36.1
51–100	14	8.9
101–300	23	14.5
301–1,000	30	19.0
1,001–5,000	19	12.0
5,001–10,000	9	5.7
>10,000	6	3.8
Firm size (total assets (VND billion)
≤10	27	17.1
11–50	21	13.3
51–100	16	10.1
101–200	9	5.7
201–500	23	14.5
501–1,000	17	10.8
≥1,000	45	28.5
Ownership structure
Without foreign capital	85	53.8
With foreign capital	73	46.2
Type of organization
100% foreign-owned enterprise	56	35.4
State-owned enterprise (≥51% state capital)	76	48.1
Private company	5	3.2
Joint venture with international partner	4	2.5
Joint venture with local partner	15	9.5
Others	2	1.3
Sector
Agriculture, forestry, fisheries, mining	7	4.4
Processing industry, manufacturing	19	12
Electricity, water, and waste disposal	2	1.3
Construction	5	3.2
Wholesale and retail	10	6.3
Transport, warehouse	14	8.8
Hotel, restaurant	10	6.3
Information and communication	8	5.1
Financial, banking, insurance, real estate	9	5.7
Consulting (e.g., architecture, law, accounting)	6	3.8
Education and training	2	1.3
Health and social aid	6	3.8
Multi-field business	42	26.6
Other activities	18	11.4

Regarding the number of full-time employees, the largest proportion (44.9%) comprised enterprises with 100 or fewer employees, which also reflects the proportion of small- and medium-sized businesses, accounting for 97% of Vietnam’s total number of businesses (Ministry of Planning and Investment, 2019). Generally, the percentages of enterprises with 100% foreign capital and private enterprises were relatively high at 35.4% and 48.1%, respectively. This proportion demonstrates the premium that these companies place on building and operating supply chains. Conversely, state-owned enterprises, joint ventures with domestic partners, joint ventures with foreign partners, and other business groups account for a negligible share of the market (ranging from 1.3% to 9.3%).

Results

Reliability and Validity Analyses

According to Table 2, the outer loadings of the observed variables are between 0.72 and 0.88, with t-values of 9.79 or greater. Thus, the scale items of the variables have a high degree of reliability. Subsequently, the reliability of the scales was evaluated using indicators such as the average variance extracted (AVE) and composite reliability (CR). Table 2 demonstrates that all research variable scales had CR indices ranging from 0.88 to 0.92, higher than the cut-off value of 0.70, and AVE indices ranging from 0.60 to 0.76, higher than the cut-off value 0.50. Consequently, all the scales in the model can be considered reliable.

Table 2.

Reliability Analysis.

	Outer loading	t-Value
Use of management accounting systems
Scope (AVE = 0.63; CR = 0.89)
Information that relates to possible future events (if historical information is most useful for your needs, mark the lower end of the scale)	0.79	20.44
Non-financial information that relates to production and market information such as growth share, etc. (If you find that financial information is most useful for needs, please mark the lower end of the scale.)	0.81	21.68
Non-economic information, such as customer references, relations, attitudes of government and consumer bodies, competitive threats, etc.	0.74	16.69
Information on broad factors external to your organization, such as economic conditions, population growth, technological developments, etc.	0.83	27.69
Timeliness (AVE = 0.64; CR = 0.88)
Requested information arrives immediately upon request	0.78	21.42
Information supplied to you automatically upon its receipt into information systems or as soon as processing is completed	0.84	25.69
There is no delay between an event occurring and the relevant information being reported to you	0.81	21.53
Requested information arrives immediately upon request	0.79	20.24
Aggregation (AVE = 0.76; CR = 0.91)
Information in forms, which enables you to conduct what-if analysis	0.88	46.30
Information on the effects of events on particular time periods (e.g., monthly/quarterly/annual summaries, trends, comparisons, etc.	0.87	38.42
Information in formats suitable for input into decision models (such as discounted cash flow analysis or incremental marginal analysis)	0.86	40.43
Integration (AVE = 0.69; CR = 0.90)
Cost and price information of departments of your business unit	0.81	26.45
Presence of precise targets for each activity performed in all sections within your department	0.81	27.53
Information that relates to the impact that your decisions have on the performance of other departments	0.85	34.66
Information on the impact of your decisions throughout your business unit and the influence of other individual’s decisions on your area of responsibility	0.86	37.40
Supply chain integration
Company’s integration with suppliers (AVE = 0.64; CR = 0.91)
Information exchange with suppliers through information technology	0.81	32.05
The level of the strategic partnership with suppliers	0.82	33.50
The participation level of suppliers in the design stage	0.78	28.28
The participation level of suppliers in the process of procurement and production	0.79	28.96
The establishment of quick ordering systems	0.79	29.08
Stable procurement through networks	0.79	27.85
Company’s integration with customers (AVE = 0.60; CR = 0.91)
Follow-up with customers for feedback	0.77	23.90
The level of computerization for customer orders	0.78	24.63
The level of organic linkage with customers by network	0.77	23.39
The level of sharing of market information	0.76	25.29
The agility of the ordering process	0.75	22.60
The frequency of periodical contact with customers	0.78	28.59
The level of communication with customers	0.80	30.34
Cross functional integration within a company (AVE = 0.60; CR = 0.92)
Data integration among internal functions through networks	0.77	26.53
Systematic information systems integration among internal functions	0.77	25.74
Real-time searching of the level of inventory	0.74	22.34
Real-time searching of logistics-related operating data	0.78	27.98
Data integration in the production process	0.78	27.84
Integrative inventory management	0.76	24.12
Systematic interaction system between production and sales	0.82	32.09
Periodic interdepartmental meetings among internal function	0.78	26.61
Perceived environmental uncertainty (AVE = 0.61; CR = 0.89)
The rate at which products and services become outdated	0.75	14.09
The rate at which marketing practices are changing	0.72	9.79
The tastes and preferences of customers in your industry	0.80	15.71
Actions of competitors	0.82	24.03
The rate of change in product/service technology	0.80	21.01
Firm performance (AVE = 0.64, CR = CR = 0.90)
Return on investments	0.81	21.38
Return on sales	0.76	16.15
Sales growth	0.80	18.95
Return on assets	0.84	27.22
Overall profitability	0.80	19.21

Discriminant Validity

To assess the discriminant validity of the variables, we relied on the criteria proposed by Fornell and Larcker (1981) and compared the square root of the average variance extracted (AVE) of each variable with its highest correlation value with the other variables. Table 3 shows that the values of the square root AVE, which were in the range of 0.68 to 0.80, are higher than the correlation value between variables (ranging between 0.03 and 0.39). This result indicates that the scales of the research variables in the model reached discriminant values. We also applied the Heterotrait-Monotrait (HTMT) ratios suggested by Henseler et al. (2015) to further evaluate discriminant validity. The ratios ranged from 0.12 to 0.42 and were all below 0.85, indicating that the scales of the research variables in the model ensure discriminant validity. Additionally, while the correlations between the main variables were all below the threshold of 0.70 and statistically significant (p < .05), the risk of multicollinearity was low.

Table 3.

Discriminant Validity Analysis.

	1_____	2_____	3_____	4_____
1. Use of management accounting systems	0.68
2. Supply chain integration	0.24*	0.75
2. Supply chain integration	0.26
3. Perceived environmental uncertainty	0.35*	0.39 *	0.78
3. Perceived environmental uncertainty	0.41	0.42
4. Firm performance	0.20*	0.33*	0.03*	0.80
4. Firm performance	0.23	0.36	0.12

Notes. 1st value = Correlation between variables (off diagonal); 2nd value (italic) = HTMT ratio; Square root of average variance extracted (bold diagonal).

Correlation significant at the 5% level.

Common Method Bias and Multicollinearity Issues

Given that we used a self-reported and key informant approach to collect survey data, the potential issue of common process bias must be addressed (Podsakoff et al., 2003). The Harman single-factor test indicated that no single factor accounted for the majority of the variance (the first factor accounted for 37.50% of the total explained variance at 71.48%). Due to the extremely conservative nature of Harman’s test for detecting biases (Malhotra et al., 2006), we employed the marker-variable method (Lindell & Whitney, 2001). The marker variable was the questionnaire item “Do you prefer to visit Ha Long Bay during the upcoming national holiday this year?” When the effects of rM were eliminated from the correlations between the primary constructs (rU–rA), the average change was 0.03. All preceding tests indicated that the risk of common method bias was low in this study. We also investigated the possibility of multicollinearity. As all the inner variance inflation (VIF) values were considerably lower than the “rule of thumb” threshold of 10 (O’Brien, 2007), multicollinearity is considered negligible.

Hypothesis Testing Results

The authors use partial least squares structural equation modeling (PLS-SEM) to analyze the acquired data to test the hypotheses and models. PLS-SEM is highly suitable for complicated models with moderation and mediation effects (Hair et al., 2021) and handles second-order constructs, which are often used to capture complex higher-order relationships between latent variables (Henseler et al., 2015). We tested these by evaluating each path’s magnitude and statistical significance in the original structural model based on indicators obtained from data processing and 500 bootstrap runs, such as the β coefficient, t-value, and adjusted R² values for the mediating and dependent variables. Considering that the adjusted R² values (ranging between 0.10 and 0.22) are not lower than the minimum requirement 0.10 (Falk and Miller, 1992), we may infer that the research model fits the obtained data rather well. Additionally, we utilized the standardized root mean square residual (SRMR) value using SmartPLS3 software for data processing. The SRMR value of the model is 0.08, which is not greater than the cut-off value of 0.08; therefore, it can be accepted as proposed by Henseler et al. (2016). From the findings of this study, it can be concluded that the research model fits the collected data relatively well.

Table 4 presents the hypothesis testing results. This hypothesis is validated by comparing the impact of MAS usage on SCI, MAS usage, and SCI on firm performance with and without PEU throttling. Specifically, H1 stated that the extent of MAS use positively influences SCI. At the 0.05 level of statistical significance, Table 4 shows that the path coefficient from the extent of MAS use to the degree of SCI has a value of 0.13 (t-value = 2.52). This value supports H1.

Table 4.

Hypothesis Testing Results.

	Model 1: without SCI as the mediating variable	Model 2: with SCI as the mediating variable		Hypothesis
	FP	SCI	FP	Hypothesis
Independent variable
MAS	0.20* (2.39)	0.13* (2.52)	0.09 (1.07)	H1, H2
SCI			0.33** (4.39)	H3
PEU × MAS		0.25** (3.77)	0.05 (0.77)	H4, H5
PEU × SCI			0.22** (3.81)	H6
Control variable
Ownership structure	0.26** (3.98)		0.21** (3.25)
Size (assets)	0.04 (0.37)		0.00 (0.02)
Size (employees)	0.07 (0.69)		0.11 (1.04)
Adjusted R²	0.10	0.13	0.22

Notes. MAS = extent of MAS use; PEU = perceived environmental uncertainty; SCI = extent of supply chain integration; FP = firm performance; PEU × MAS = the interaction between PEU and MAS; PEU × SCI = the interaction between PEU and SCI; numbers in brackets: t-values;

, **denotes significance at 5% and 1%, respectively (two-tailed t-test).

The level of MAS beneficially affects performance, according to H2. Two hierarchical models were used to test this hypothesis. Model 1 shows the direct influence of MAS usage on firm performance, whereas Model 2 refers to the impact of these two variables in the presence of the mediating role of SCI in the relationship between them. Table 4 shows that MAS use positively affects firm performance (β = .20, t-value = 2.39). Our study also found that the extent of SCI positively affects firm performance (β = .33; t-value = 4.39), supporting H3. When the mediating variable SCI was included in the association between the extent of MAS use and performance, the influence of MAS use on performance became statistically insignificant (β = .09; t-value = 1.07). This result indicates that SCI fully mediates the relationship between MAS use and firm performance. Thus, H4 is supported.

H5 stated that PEU positively moderates the association between MAS use and firm performance. To test this hypothesis, we generated the interaction term PEU × MAS after centering the two interacting variables (PEU and MAS) to avoid multicollinearity (Aiken et al., 1991). Table 4 indicates that H5 is not supported (β = .06; t-value = 0.77). H6 posits that PEU positively moderates the influence of SCI on firm performance. We created the interaction term PEU × SCI after mean-centering PEU and SCI to reduce multicollinearity. Specifically, the path coefficient from the interaction term PEU × SCI to firm performance was significant (β = .33; t-value = 4.39). According to H7, PEU positively moderates the relationship between MAS use and SCI. Similar to H5 and H6, in H7, PEU moderated the relationship between the extent of MAS use and CI. Table 4 demonstrates that H7 was supported; specifically, the path from the interaction term PEU × MAS to SCI was statistically significant (β = .25; t-value = 3.77).

Additionally, we examined the effect of the control variables (i.e., ownership structure, firm size in terms of assets, and full-time equivalent employees) on firm performance. While firm size does not significantly impact firm performance, ownership structure has a significant impact (Model 2: = 0.21; t-value = 3.25), indicating that foreign-invested firms operate more efficiently than domestic firms. Certainly, these organizations focus on constructing and efficiently operating MAS and supply chains.

Endogeneity Test: A Robustness Check of Research Results

To enhance the methodological rigor of our study, we performed robustness checks using PLS-SEM following the recommendations of Becker et al. (2022) and Hair et al. (2021), with a test for endogeneity. Becker et al. (2022) address the potential concern of endogeneity that arises when exogenous constructs show significant correlations with the error term of the endogenous constructs in their proposed model in SmartPLS4. To tackle this issue, we employed Gaussian copula analysis. We found that none of the Gaussian copula terms for the relationships between the exogenous constructs (i.e., MAS use, SCI, PEU, PEU × MAS, and PEU × SCI) and endogenous constructs (i.e., SCI and firm performance) is statistically significant, as indicated by p-values exceeding .05. Thus, it can be concluded that endogeneity did not pose a significant issue in our study.

Discussion of Research Results

This study provides empirical evidence of the positive effect of MAS use on firm performance via SCI and tests the positive moderating roles of PEU on the relationships between MAS, SCI, and firm performance. First, this study finds that MAS use increases SCI, which in turn improves firm performance, supporting H1, H2, and H3. This illustrates the significance of MAS information in supply networks. The SCI will expand if the scope of MAS use in all four aspects corresponds to the information requirements of the supply chain partners. This result also underlines the significance of sharing information in the supply chain, particularly MAS information, as stated in the SCI definitions by Flynn et al. (2010) and W. Yu et al. (2013), to foster the integration of supply chain partners. Moreover, this result demonstrates, through the lens of the uncertainty theories of Otley (1980) and the RBV theory of Barney (1991), that an enterprise-based supply chain system is well designed. SCI is facilitated by an enterprise and its compatibility with the external environment, which enhances firm performance. This result is compatible with the findings of prior investigations, such as studies in Taiwan by Lii and Kuo (2016), Kim (2009) in Korea, and Flynn et al. (2010) in China.

Second, our study shows that PEU does not positively moderate the relationship between MAS use and performance, thus rejecting H5. This indicates that Vietnamese enterprises have not explored the efficient use of MAS information to deal with unpredictable environments and improve their performance. By contrast, prior studies, such as Agbejule (2005) in Finland and Gul (1991) in Australia, confirm a positive association among PEU, MAS, and firm performance. According to Gul (1991) and Agbejule (2005), comprehensive economic and non-economic information from outside the firm is necessary to evaluate competitive behavior and uncertain market demand. Timely and regular information is essential for effective response when the external environment changes rapidly. Concurrently, integrated and aggregated data are necessary to manage highly volatile scenarios. Gul (1991) also asserted that businesses could achieve remarkable performance if they used MAS data efficiently and in accordance with PEU. MAS information is acknowledged as an appropriate reaction to PEU to construct a buffer against environmental unpredictability (Chenhall & Morris, 1986). This study does not overcome the limitations of Gul (1991) and Agbejule (2005) by demonstrating the positive moderating effect of the PEU context variable on the link between MAS information utilization and firm performance.

Third, this study finds that PEU positively moderates the effect of SCI on firm performance, accepting H6. This result demonstrates that the interaction between PEU and SCI can significantly affect supply chain firms’ ability to achieve remarkable performance in Vietnam’s evolving economic climate. A combination of Otley’s (1980) uncertainty theory and Barney’s (1991) RBV demonstrates that an uncertain environment stimulates SCI to create a competitive advantage for firms in the supply chain. SCI is a resource that enables businesses to change rapidly and become more alert to changes in an uncertain environment. This hypothesis also demonstrates that SCI satisfies the difficult-to-replicate criterion for a resource with VRIN conditions and enables businesses to react to changes in the external environment more rapidly than their competitors. However, acquiring such resources is costly. Hence, a company with a high SCI has an undeniably lasting competitive advantage (Barney et al., 2001).

Finally, this study revealed the moderating role of PEU in the relationship between the extent of MAS use and SCI, thus supporting H7. This finding demonstrates the significance of MAS use and the level of information sharing in the supply chain in a high-uncertainty environment. This has fostered integration among supply chain partners to combat the problem of increasingly severe environmental changes, thereby assisting businesses in remaining stable. Through the lens of uncertainty and RBV theories, it was demonstrated that the level of use of resources with VRIN characteristics—high MAS information in an uncertain environment—facilitated the development of another resource with VRIN characteristics (SCI). Businesses that possess these unique resources enjoy sustainable competitive advantages that are difficult for rivals to replicate. Competitive advantage is demonstrated by increasing the capability to integrate partners in the supply chain, thereby meeting businesses’ expectations to provide customers with the highest value at the lowest cost and the greatest speed (Flynn et al., 2010).

Implications, Limitations, and Future Research Directions

Theoretical Implications

This study has several theoretical implications. First, this study has helped close the gaps at the intersection of MAS and the supply chain, specifically through the extent of MAS use and the extent of SCI in enterprises that have constructed a supply chain but are still quite limited in their use of MAS information to assist in the formulation of strategies, planning, and organization of the chain’s operations. This is the first study to examine the mediating role of SCI severity in the relationship between MAS use and performance and test the interaction between MAS information and SCI in enhancing performance in environments with and without PEU regulations for Vietnamese supply chain enterprises.

Second, this study highlights the importance of the effective and efficient use of MAS in four aspects: broad scope, timeliness, aggregation, and integration (Chenhall & Morris, 1986) in the supply chains of built enterprises, which comprise various fields. As mentioned in previous studies, SCI for customer, internal, and supplier integration is proportional to MAS use. Thus, this study expands the research on the role of MAS information in the supply chain, as Seal et al. (1999) mentioned in the context of integration in supply chains.

Third, the findings validate the position of contingency theory (Otley, 1980, 2016) in consistently developing MAS and SCI across resources and internal capacity, harmoniously integrating an enterprise’s internal resources with its external environment. Similarly, through the RBV (Barney, 1991; Barney et al., 2001), businesses can utilize the available resources to build and develop two VRIN-characteristic resources. These two resources enable firms to integrate with suppliers and customers by effectively utilizing MAS information. Effectively combining MAS and SCI information sharing increases competitive advantage, achieves outstanding performance, and creates opportunities for business success (Taschner & Charifzadeh, 2020).

Finally, while previous studies only examined the need to share MAS information for improved and effective coordination of supply chain activities (e.g., Flynn et al., 2016; Gunasekaran et al., 2008; Ramos, 2004), this study revealed the moderating effects of PEU on two relationships (i.e., the extent of MAS use and SCI and the extent of SCI and firm performance). This study contributes significantly to the accounting and supply chain literature by demonstrating the significance of MAS information in the SCI.

Managerial Implications

This study has several managerial implications. First, the mediating role of the extent of SCI in the relationship between MAS use and performance demonstrates that SCI is a crucial catalyst for transforming MAS use into exceptional firm performance. MAS information helps firms achieve their strategic objectives by integrating supply chain partners and establishing a strong network of internal and external links. This assists companies in enhancing the competitive advantage and operational efficiency of their supply chains, thereby enhancing their performance. Therefore, businesses must promote the integration and aggregation of MAS and supply chain systems to enhance performance.

Second, the positive interaction between MAS information and SCI in enhancing performance in environments with and without PEU regulations suggests that Vietnamese businesses can enhance their performance when multiple factors cause uncertainty. In other words, enterprises should focus on MAS and SCI to increase the efficiency of supply chain operations and improve their performance in complex and uncertain environments. Our study shows that understanding the interactions between supply chain partners and sharing the available information with the aid of an efficient MAS is essential for business success. Additionally, this study demonstrated that extensive MAS use and SCI can aid in overcoming PEU. If firms know how to harmonize these two resources, they will be able to perceive an ever-changing environment with greater accuracy, thereby enhancing their performance.

Third, the influence of PEU compelled managers to effectively apply all aspects of MAS information based on the integration of parts and functions within the enterprise to increase the level of integration with suppliers and customers. Thus, businesses can identify changing trends (e.g., sudden changes in customer needs or competitors’ lowest-cost strategies) to assist managers in making timely decisions. Therefore, it is necessary to adjust the SCI strategy appropriately. Based on enhanced SCI, agile PEU assists managers in making appropriate decisions, meeting customer requirements, enhancing competitive advantage, and positively influencing performance.

Limitations and Future Research Directions

This study has some limitations. First, the data collected were cross-sectional. Given that the collection of MAS data and the pursuit of an SCI strategy require significant time to implement, longitudinal data are required for more precise research results. Thus, both the practical and theoretical effects of the variables in the proposed model can be demonstrated objectively (Shou et al., 2018).

Secondly, environmental factors such as policy changes, national culture, and physical conditions, as well as industry regulations, conflicts, and pandemics, have not been thoroughly explored. Therefore, future studies should also consider these factors. In the context of globalization, businesses must strive for SCI through the integration of information technology because technology can assist them in creating value in the virtual world for supply chain management (Ford & Greer, 2020). Although information technology integration is mentioned in Kim’s (2009) SCI scale, the level of integration between enterprises and upstream and downstream supply chain partners remains minimal. This study suggests that SCI should be closely integrated with information technology integration to promote global supply chain cooperation by exchanging information among partners in a timely, efficient, and convenient manner, thereby aiding managers in making sound decisions (Kumar, Verma, et al., 2017). Additionally, integrating information technology facilitates the agility of the supply chain (Sabet et al., 2017); therefore, it should be considered separately.

Third, our study does not examine the decision-making variables that may mediate the relationships between MAS use, SCI, and firm performance, such as decision-making quality, which encompasses attributes such as accuracy, commitment, timeliness, and understanding of decisions (Franke et al., 2023; Oyewo et al., 2019) made by supply chain managers with the assistance of MAS. Thus, future studies should investigate these variables to comprehensively understand the interplay between MAS use, SCI, and firm performance.

Finally, because this study was conducted in Vietnam, the results may not be applicable to other countries for various reasons, such as differences in national culture, leadership styles, and disapproval levels. Therefore, future studies can circumvent these limitations by researching on a transnational scale or examining the above uncertainties to better understand and evaluate performance improvements more objectively and clearly. Additionally, this study focuses on businesses that have constructed supply chains in these fields. Future research should be conducted for each distinct business area to evaluate the model from multiple perspectives.

Conclusion

Drawing upon the RBV and contingency theory, this study hypothesizes that the degree to which MAS is employed affects SCI and firm performance and that these effects are influenced by PEU. By constructing and testing a moderated mediation model based on data from 158 businesses in Vietnam, this study makes a noteworthy contribution to the supply chain literature, as it is one of the first to demonstrate that SCI fueled by MAS and PEU can enhance firm performance. Firms with high MAS usage in their supply chain operations are likely to enjoy favorable conditions that promote SCI and competitive advantage. Moreover, heightened PEU increases the pressure on firms to bolster the efficacy of MAS in supporting SCI and firm performance. Our research findings encourage further exploration of how MAS can be leveraged in the context of a high level of business uncertainty to aid firms in emerging economies to enhance their SCI and firm performance.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the University of Economics Ho Chi Minh City (Grant Number: 2022-09-03-1138).

Ethical Approval

This study was approved by the Ethics Committee of the University of Economics Ho Chi Minh City (Committee Approval Number: 4545/QD-DHKTQLKH).

ORCID iD

Nguyen Phong Nguyen

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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How Can Vietnamese Companies Benefit from Management Accounting Systems and Supply Chain Integration Under Business Environment Uncertainty?

Abstract

Plain Language Summary

Keywords

Introduction

Model and Hypothesis Development

The Relationship Between MAS Use and SCI

The Mediating Role of SCI

The Moderating Role of PEU

Methods

Research Site

Measures of Constructs

Sampling

Results

Reliability and Validity Analyses

Discriminant Validity

Common Method Bias and Multicollinearity Issues

Hypothesis Testing Results

Endogeneity Test: A Robustness Check of Research Results

Discussion of Research Results

Implications, Limitations, and Future Research Directions

Theoretical Implications

Managerial Implications

Limitations and Future Research Directions

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

References