Partnering Implementation in SMEs: The Role of Trust

Abstract

Resource Dependence Theory suggests that (a) power balance with resource interdependency, (b) formal/informal procedures for resource exchange, and (c) matching in goals and operational philosophies positively affect partnering implementation (information exchange and joint decision-making). Additionally, improved partnering implementation positive affects commitment fulfillment and dispute resolution. In a setting where SMEs supply to small local retailers, the SMEs do not suffer from low bargaining power and rely on informal contracts, and both firms are compatible. The small trading partners in this study predominantly have face-to-face and telephonic interactions with each other (possible due to the small number). Knowledge of one another and a simple transaction process reduces risk and uncertainty, and leads to trust. In this study, trust is a contextual factor, and we aim to determine if there is a positive effect of (a), (b), and (c) on partnering implementation, and if the effect strengthens with an increase in the level of trust. Survey data are used to calibrate and validate a structural equation model independently. Through empirical research, we aim to identify deviations in results, determine the cause of deviation in the study characteristics, and add explanatory power to research findings. Except for the influence of trust on the positive relationship between informal procedures and partnering implementation, the finding fits with the theoretical bases. With a high level of trust, clarity in time, accuracy, and relevance of information exchanged may be lacking, compromising decision-making and adding to the ambiguity of partnering implementation with an informal agreement.

1. Introduction

A typical supply chain comprises suppliers, manufacturers, warehouses, and retail entities. The entities must work together to supply the correct quantity of goods to the right place at the right time. Managing the supply chain is vital to meet the end customers’ requirements and ensure each entity’s profitability. The supply chain needs to achieve speed and affordability without sacrificing quality.

Food quality, including safety, is a significant concern facing today’s food industry [1]. The major challenge for the Agrifood supply chain is maintaining its product quality characteristics at each stage. Agrifood supply chain processes include, but are not limited to, (1) land cultivation, (2) crop production, (3) food processing, (4) food packaging, (5) inventory management, and (6) logistics planning [2,3]. Coordination among various entities involved in the Agrifood supply chain is critical for sustainable improvement.

Today, processed ready-to-eat and drink items are displacing home-cooked meals and artisanal foods. The NOVA system [4] classifies food items based on the amount and type of processing involved into four categories, of which three are discussed below.

Category 1 foods undergo little to no processing (e.g., eggs, milk). These techniques do not use salt, sugar, oils, or fats.

Category 2 foods include baking and cooking ingredients obtained naturally from food and the environment (e.g., oil, salt, sugar).

Category 3 foods are obtained by processing Category 1 foods with sugar, oil, salt, and other ingredients from Category 2 (e.g., biscuits, salted cashew nuts).

Category 4 foods are ultra-processed foods obtained by adding lots of additives (sugar, salt, saturated fat, trans fat, etc.) (e.g., packed cookies, drinks, chips, frozen meals).

The food-processing industry uses appealing packaging and aggressive multi-media marketing to push sales. Packaging is essential for protecting food as it travels through the supply chain to the consumer. Packaging techniques constantly evolve from simple preservation methods to active packaging innovations that extend product shelf-life and improve safety and sensory properties. Food packaging serves additional functions such as branding, promotion, and stating ingredients and calorie content. For our research, the unit of analysis is SMEs involved in the bakery business (which falls under the NOVA 3 category) and whose customers are small local retailers. That is the reason we briefly discuss the NOVA categorization.

1.1. Research Motivation

India has one of the largest agricultural sectors and is the world’s second-largest food producer after China. It is estimated that 30% of world food production is wasted [5]. In developing countries like India, food wastage is caused by supply chain inefficiencies (e.g., lack of cold chain, poor infrastructure, inadequate leverage of information technology potential). One of the primary factors contributing to supply chain inefficiency and food waste in India is insufficient food processing. Developed nations process up to 80% of their food, extending their shelf-life. In contrast, India processes about 10% [6], suggesting a predominance of unprocessed and minimally processed foods, which are more susceptible to spoilage. This reliance on unprocessed options contributes to the significant food waste issue in India, mainly due to supply chain inefficiencies.

India’s share of processed food exports has remained stable at around USD 3.2 billion. India has a high potential for growth in the processed food sector and its exports. The growth of the nation’s middle class is driving growth in the processed food industry, with India’s packaging market growing at 15% per year [7]. Many small and medium enterprises (SMEs) are involved in food processing and packaging. The SMEs, however, lack the resources and understanding to collaborate effectively with other entities in their supply chain.

Trust (where both parties believe the other will honor the terms of their business obligations) is fundamental in every business relationship and transaction (especially in continuous trading). Lack of trust increases transaction costs and requires complex contracts and governance systems. Without trust, interaction among community members (social networks of friends, friends of friends) is insufficient to sustain cooperation. Community-based informal institutions cannot be relied on to enforce contracts. The formal judiciary must then enforce the contracts, which is prohibitively time-consuming and costly (especially for resource-starved small enterprises) in a developing country such as India [8].

1.2. Research Objective and Approach

According to Resource Dependence Theory (RDT), a firm must engage with entities in its surroundings to get resources. Inter-organizational alliances are essential to achieving goals that would be difficult for a single entity to accomplish efficiently [9]. However, dependence on a partner for a large proportion of input or output grants the partner some control and power [10]. Otherwise, the firm’s dependency on a partnering entity gives the partner authority [11].

Power plays a crucial role in any relationship. Power distribution is usually uneven in inter-organizational relationships [12]. Addressing the power imbalance by understanding the interdependencies brings stability to the relationship. Additionally, appropriately designed contracts play their part. Supply chain entities usually prefer ties with partners with similar business cultures and leadership styles, which support joint planning and the implementation of goals. Collaborative decision-making requires clearly understanding and aligning both entities’ strategic and operational goals.

Trust-based resource commitments are required in inter-firm relationships [13]. To connect and collaborate effectively, firm personnel must trust each other and their counterparts in partner organizations. Each entity perceives the association to be beneficial if it effectively improves profits, and the demands and expectations of the end-customer are met.

Consumers’ concern about tainted food has strained and eroded trust in the supply chain. Higher levels of trust are associated with a lower transaction cost and higher efficiency in the relationship between member firms. When members trust each other, the supply chain requires fewer contracts, a smaller number of disputes requiring less effort to resolve, and low information disparity [14]. Trust in the Agrifood supply chain is linked to food safety and quality at each stage of the supply chain.

A contextual factor refers to the environment in which a firm’s processes are developed and implemented [15], and in this study, trust is a contextual factor. It is hypothesized that (1) balance of power, (2) contractual governance, and (3) partner match are positively related to partnering implementation (information sharing and joint decision-making). It is proposed that trust strengthens each of these positive relationships. Partnering implementation is, in turn, positively associated with perceived relationship effectiveness.

Thus, this study aims to answer the following two research questions:

RQ1: Do ‘balance of power’, ‘contractual governance’, and ‘partner match’ positively affect ‘partnering implementation’, and does this effect strengthen with an increase in the level of ‘trust’?

RQ2: Does an improvement in ‘partnering implementation’ positively affect the ‘perceived relationship effectiveness’?

Though there is an indication that an entity gains external resources efficiently by (a) managing power imbalance, (b) designing appropriate contracts, and (c) matching work culture, it is yet to be validated empirically by identifying the context that ensures its effectiveness in terms of information sharing and joint decision-making. Through empirical research, we aim to identify deviations in results, determine the cause of deviation in the study characteristics, and add explanatory power to research findings [16].

The results of empirical studies often vary between (a) firms in the same industry (e.g., due to size differences, the difference in work culture, and leadership style), (b) firms in different industries, and (c) firms in different supply chains. It is to be understood if the benefit to SMEs by integrating externally with other entities is worth investing their limited resources in [17].

Most studies focus on the supply chain of large firms in the manufacturing sector (e.g., automotive, electronics, aerospace) and that of a developed country [18]. Usually, the relationship between the focal firm and its supplier(s) is examined. We, however, study the relationship between the focal firm (SME) and its customer(s) in a developing nation. An empirical study of the focal firm, its supplier(s), and customer(s) presents difficulty in collecting data due to informal and fragmented supply chains across multiple entities in the same network [19]. Thus, a self-administered survey method is used.

Data are collected from SMEs (bakeries with less than 50 employees) processing food to obtain NOVA Category 3 items and packing them before supplying them to small local retailers (the retail environment in India is unorganized and dominated by small retailers). The instrument is validated using confirmatory factor analysis. The hypothesized relationships between the constructs are tested using covariance-based structural equation modeling.

1.3. Research Contributions

The main contributions of this study are as follows:

• Most research focuses on issues when SMEs supply to larger firms. By studying SMEs supplying to small local retailers, we examine a relationship (i) with interdependencies and without domination, (ii) governed by informal contracts, and (iii) without a requirement for either partner to change business practices;
• Small enterprises have limited resources, and we determine the above conditions to promote their effective usage for the exchange of relevant information (as well as its timeliness and accuracy) and joint decision-making. These, in turn, sustain a productive and successful collaboration;
• Small trading partners with face-to-face and telephonic interactions (possible due to the small number) have knowledge of one another and in-between trust. We determine trust to improve the effectiveness of resource usage in the case of (i) and (iii) above, but not (ii). With a high level of trust, the time at which information exchange occurs and the information type and detail tend to be loosely defined.

The remainder of the paper is organized as follows. Section 2 provides the theoretical background, discussing the Resource Dependence Theory. Section 3 provides the proposed model and research hypotheses. Section 4 details the methods. Section 5 discusses the findings. Section 6 concludes with recommendations for future research.

2. Resource Dependence Theory (RDT)

Resource Dependence Theory (RDT) posits that organizations rely on external resources for survival and growth, creating power dynamics [10]. Organizations with critical resources govern those depending on them. Therefore, firms often use measures to lessen their dependence on others and increase their influence. The present study bases the framework on Resource Dependence Theory (RDT) and studies the interrelationship between power balance, contractual governance, partner match, partnering implementation, perceived relationship effectiveness, and trust for the analysis and development of coordination between firms. Partnering implementation aims to improve inter-firm decision-making at the strategic and operational levels. The RDT offers an effective thematic for research. By empirical study, the practical applications of the model can be determined.

2.1. Balance of Power

Firms with comparable resources may be considered the closest rivals in a given market. Understanding the resource capabilities of its closest rivals allows the firm to assess its bargaining power within the supply chain ecosystem. Such firms constantly compete to gain a marketing edge [20]. Inter-organizational links must be synergistic and mutualistic to flourish, with asymmetrical interactions likely to result in conflict and instability [21,22]. Two entities enter a transaction owing to interdependencies, following which they are in a position to facilitate or hinder each other’s outcome and performance. One entity’s dominance over the other will likely negate the relationship’s success. Understanding and accepting power imbalance is crucial to successful relationship-building [22]. Because of the resource’s economic and operational importance for a firm, the partner with resource availability is granted power in situations with a lack of options for obtaining the resource. RDT still offers a comprehensive understanding of this aspect [23].

2.2. Contractual Governance

Inter-organization coordination is achieved through contractual governance, which features formality, exit barriers, and exclusivity. Formality represents how well the conditions are defined in the mutual agreement. Exit barriers represent the financial and technological barriers that prevent either party from dissolving the relationship. Exclusivity implies that the resource is not shared with other entities, reducing the likelihood of the relationship’s contention. The underlying resource endowment may improve depending on how the partnering entities manage these aspects.

2.3. Partner Match

Compatibility is a significant factor to be considered when partnering with an entity. Entities with similar domains (having the same areas of expertise or operation) and goal congruence make ideal partners. Prior knowledge of its and potential partners’ objectives is required before formalizing any agreement. Collaboration between entities with common goals, objectives, operational philosophies, and corporate cultures is more likely to succeed [24]. Long-term social, economic, service and technical ties are the foundation of successful collaboration. An alliance’s past and anticipated future continuity are inextricably linked [25]. A successful relationship is based on a history of cooperation, mutual trust, and the ability to resolve conflicts.

2.4. Trust

Partnering entities with trust improves supply chain performance by improving access to rare resources, surpassing competitors, and gaining market position [26]. Entities with a relationship founded on faith, mutual respect, and trust are more accepting of one another’s behavior and more willing to resolve issues amicably through discretion, skill, and tact. Market competitiveness improves, and transaction costs are reduced, when entities form such a relationship [27]. Trust between partners may thus be one of the crucial factors that play a decisive role in the success of inter-organizational relations.

2.5. Partnering Implementation

The inter-organizational relationship is based on interdependence, with members expected to share timely and accurate information and financial and manufacturing resources. Strengthening the relationships requires consistent engagement and information exchange at all management levels. Entities will likely share crucial information only with solid ties [28]. At the strategic level, the agreement is required on long-term plans, such as developing new products and increasing production capacity [29]. Joint decision-making shall improve demand forecast, production, and inventory plans at the operational level. Partnering implementation features include technology access, information sharing, and joint program assessment.

2.6. Perceived Relationship Effectiveness

The perceived effectiveness of a relationship is based on each partner’s ability to fulfill its commitments and its regard for the other party. In this study, effectiveness is a subjective measure of the extent to which the collaborative effort is productive and successful. Studies demonstrate an association between subjective evaluations and objective outcomes [30]. Partners who feel satisfied, experience low conflict and respect each other’s reputation are more likely to maintain a productive and successful collaboration in the long run. We considered three dimensions of perceived effectiveness: (1) perceived satisfaction, (2) perceived conflict, and (3) partner’s reputation. Perceived satisfaction is a sense of contentment the partnering entity has with the relationship outcome. Perceived conflict is believing the partnering entity’s actions are not in the firm’s best interest. Reputation reflects the partner’s supply performance in terms of quality, delivery time, and cost [31].

3. Hypothesis Development

As per the RDT model, the following hypotheses were tested.

The balance of power (BP) positively affects partnering implementation (PI), with higher BP relating to higher PI. Contractual governance (CG) positively affects PI, with a higher level of CG relating to a higher level of PI. The partner match (PM) positively affects PI, with better PM relating to an improved level of PI. An improved PI positively affects perceived relationship effectiveness (PRE), with higher levels of PI relating to higher levels of PRE.

It is proposed that trust positively moderates the relationship between BP and PI. That is, the trust strengthens the positive relationship between BP and PI. The trust similarly moderates and strengthens the positive relationship between CG and PI (and PM and PI). The following section discusses the reasons for the theoretical relationship. The primary focus of this study is PI, which links BP, CG, and PM with the PRE, as shown in Figure 1.

3.1. Hypothesis H1: The Relationship between Power Balance and Partnering Implementation

A firm’s power is its ability to influence the actions of partnering entities through its decisions [32]. However, a firm’s dominance over others would affect the prospect of its future relationship in terms of the exchange of timely and accurate information on, for example, inventory status, market demand, and customer needs, which aid in partnering implementation.

Managers must understand the power structure in their supply chain while incorporating or recommending any strategies and operational practices that impact the remaining members [33,34]. An improved understanding of power balance shall aid in improving partnering implementation by promoting cooperation to identify and fulfill customer needs, compete effectively, and utilize resources efficiently.

The above arguments lead to the following hypothesis.

H1: 

Balance of power positively influencse partnering implementation.

3.2. Hypotheses H2: The Relationship between Contractual Governance and Partnering Implementation

Contractual governance refers to agreements (both written and oral) formed by parties to mitigate risk and ambiguity in resource exchange. A firm connects with entities in the external environment in the interest of its profitability and continuity [10]. A collection of formal or informal procedures governs the resource and knowledge exchange via this connection to benefit both organizations [35]. Contractual governance positively influences operational performance [36], suggesting an improved level of partnering implementation.

The above arguments lead to the following hypothesis:

H2: 

Contractual governance positively influences partnering implementation.

3.3. Hypothesis H3: The Relationship between Partner Match and Partnering Implementation

Firms with similar management styles, organizational fit, and a history of successful collaboration have better partner matches [37]. Such firms have similar goals and objectives and compatible operating practices, essential for successful relationship-based strategies. A successful partnership requires active collaboration and information exchange at all management levels to achieve a common goal, which is more likely if the partners are a good match [38]. Partnering implementation can succeed only when both parties share the same guiding principles and draw on similar backgrounds and experiences.

The above arguments lead to the following hypothesis.

H3: 

Partner match positively influences partnering implementation.

3.4. Hypothesis H4: The Relationship between Partnering Implementation and Perceived Relationship Effectiveness

Partnering implementation is characterized by information sharing and joint decision-making, and partners exert influence over one another’s behavior to strengthen this aspect. Joint decision-making sets clear strategic and operational guidelines for both firms to operate. It clarifies the expectation of supply performance, reduces chances of conflict, and improves the reputation in each other’s perspective. It leads to synergy, where both firms benefit [39].

The above arguments lead to the following hypothesis.

H4: 

Partnering implementation positively influences perceived relationship effectiveness.

3.5. Hypotheses H5, H6, and H7: Moderating Effect of Trust on the Relationship between (a) Balance of Power, (b) Contractual Governance, (c) Partner Match, and Partnering Implementation

Partnering entities are more committed to collaboration effectiveness and efficiency when power is balanced, and they trust the other party shall further their interest [40]. Trust between the two entities implies that each will not behave opportunistically and misuse power [41]. Contractual governance measures are less critical in a trust-based relationship where opportunistic behavior is expected to be minimal [42]. Studies have shown that trust is beneficial to achieving performance [43] and aids in resolving conflicts. Trust is essential to overcome compatibility issues between partners and sustain relationships in the long term [44,45]. Thus, in our research, we position trust as a moderating factor. We are interested in how it influences the relationships between key factors (i.e., balance of power, contractual governance and partner match) and perceived effectiveness. We believe exploring these moderating effects provides a nuanced understanding of how various factors interact to influence perceived relationship effectiveness.

The above arguments lead to the following hypotheses.

H5: 

Trust will moderate the relationship between the balance of power and partnering implementation. That is, the relationship between the balance of power and partnering implementation will be stronger when there is a high level of trust between the partners.

H6: 

Trust will moderate the relationship between contractual governance and partnering implementation. That is, the relationship between contractual governance and partnering implementation will be stronger when the partners have a high level of trust.

H7: 

Trust will moderate the relationship between partner match and partnering implementation. That is, the relationship between partner match and partnering implementation will be stronger when there is a high level of trust between the partners.

4. Methods and Analysis

In the first step, we prepare a questionnaire for data collection. Self-administered questionnaires were used to collect the research’s data. For large-scale research, this is commonly regarded as one of the best methodologies [46]. Most researchers favor this approach because of its secrecy, freedom of response, and anonymity [47]. This data-gathering method was adopted in our study for two reasons: cost-effectiveness and efficiency in data collection. A self-administered survey method helps ensure that the data collected from each respondent is consistent.

The survey instrument is applied for large-scale data collection only after a pre-test and a pilot study [48], as detailed in Section 4.1. Data collected on a large scale validate the instrument using confirmatory factor analysis (CFA), and we test the hypothesized relationships between the constructs using a structural model, as detailed in Section 4.2. The measurement (CFA) and structural models comprise covariance-based structural equation modeling (SEM).

4.1. Questionnaire Preparation

Six latent first-order constructs are included in the theoretical framework: (1) balance of power, (2) contractual governance, (3) partner match, (4) partnering implementation, (5) perceived relationship effectiveness, and (6) trust. The constructs must be measured to test the proposed hypotheses.

4.1.1. Item Generation

Articles dealing with supply chain management were reviewed. A tentative list of items to measure each construct was adopted from the literature.

4.1.2. Pre-Test and Structured Interview

A thorough literature assessment and interviews with academics and practitioners ensure the instrument’s content validity. Content validity is “the degree to which items in an instrument represent the content to which the tool will be customized” [49]. Their recommendations resulted in the modification and removal of unnecessary and unclear items. The details of the remaining items are provided in

Table 1. Items for constructs.

Code	Items	Modified from
Balance of power (BP)		[21,24,37]
BP1	We sell to several customers.
BP2	We are dependent on our customers to promote our product.
BP3	We provide other value-added services to our customers.
BP4	Our customers do not have a viable alternative to us.
BP5	Our customers do not contribute to our firm performance.
Contractual Governance (CG)		[24,37]
CG1	Our customers and we understand the roles, actions, and schedules.
CG2	Our customers and we have detailed operating guidelines.
CG3	The partnership with customers is based on sound understanding.
CG4	Customer firms and we have the right to withdraw from the relationship without prior notice or penalty.
CG5	The customer firms and us could have a similar understanding with our competitors.
CG6	Our firm could have a similar understanding with the customer firm’s competitors.
Partner Match (PM)		[24,37]
PM1	Our firm has continuously been in business relations with our customers for several years.
PM2	Our firm’s goals and objectives are consistent with our customers.
PM3	Our firm and our customer firms have similar operating philosophies.
PM4	The relationship between our firm and customers has been stable and enduring.
PM5	Our management and that of customers have a good working relationship.
Trust (TR)		[39]
TR1	Promises made to our customers by us are reliable.
TR2	Our firm does not make false claims to our customers.
TR3	Our customer sometimes does not keep promises made to our firm.
TR4	When we share our problems with our customers, they respond with understanding.
TR5	We can count on our customers to consider the impact of their behavior and decisions on our firm.
TR6	We believe our customers will support and assist us despite changing circumstances.
Partnering Implementation (PI)		[39,50,51]
PI1	Our firm, along with customers, jointly identifies end-customer needs.
PI2	Our customers do not assist our firm in forecasting demand.
PI3	Our customers assist our firm in managing inventory.
PI4	Our customers assist our firm with pricing decisions.
PI5	Our firm and customers exchange timely information.
PI6	Our firm and customers exchange accurate information.
Perceived Relationship Effectiveness (PRE)		[43,52]
PRE1	We are happy with our working relationship with most customers.
PRE2	Our firm has had an unhappy relationship with some of our customers.
PRE3	Our firm has had major disagreements with few customers on specific issues.
PRE4	Our customers often do not meet their commitments.
PRE5	Our customers have a good reputation in the market.

.

4.1.3. Pilot Study

The pilot study helps improve the scale in preparation for data collection. The self-administered pilot survey (between February 2023 and March 2023) included 91 SMEs (specifically, bakeries) processing food to obtain NOVA Category-3 items (e.g., cakes, biscuits) and packing them before supplying them to customers who are small local retailers. These SMEs had fewer than 50 employees. Participants were asked to mark their responses to the items in Table 1 on a Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7).

Result and Analysis

Certain items were dropped using the corrected item-total correlation (CITC) test in the scale purification process. Eight items (BP3, CG1, CG2, PM2, TR6, PI1, PI2, and PRE1) having CITC values less than or equal to 0.5 were dropped [53].

Table 2. Result of pilot study.

Code	I0	IF	Ai	IFL	FFL	α
Balance of Power (BP)
BP1	0.849	0.868	0.919	0.926	0.926	0.941
BP2	0.858	0.857	0.923	0.899	0.906
BP3	0.346	--	--	0.629	--
BP4	0.869	0.881	0.915	0.913	0.922
BP5	0.805	0.828	0.932	0.888	0.893
Contractual Governance (CG)
CG1	0.331	--	--	0.530	--	0.937
CG2	0.235	--	--	0.734	--
CG3	0.771	0.797	0.934	0.799	0.841
CG4	0.841	0.868	0.912	0.855	0.890
CG5	0.843	0.880	0.907	0.895	0.904
CG6	0.830	0.862	0.913	0.834	0.873
Partner Match (PM)
PM1	0.851	0.865	0.859	0.880	0.900	0.909
PM2	0.236	--	--	0.608	--
PM3	0.773	0.784	0.887	0.880	0.879
PM4	0.781	0.815	0.875	0.858	0.869
PM5	0.705	0.727	0.909	0.816	0.811
Trust (TR)
TR1	0.672	0.701	0.844	0.797	0.783	0.872
TR2	0.748	0.781	0.825	0.818	0.814
TR3	0.661	0.692	0.847	0.757	0.779
TR4	0.600	0.651	0.857	0.761	0.774
TR5	0.706	0.678	0.850	0.797	0.822
TR6	0.273	--	--	0.406	--
Partnering Implementation (PI)
PI1	0.117	--	--	0.702	--	0.880
PI2	0.245	--	--	0.572	--
PI3	0.710	0.748	0.845	0.785	0.801
PI4	0.700	0.796	0.827	0.839	0.872
PI5	0.671	0.704	0.860	0.773	0.776
PI6	0.598	0.737	0.854	0.821	0.827
Perceived relationship effectiveness (PRE)
PRE1	0.354	--	--	0.676	--	0.813
PRE2	0.705	0.666	0.759	0.735	0.764
PRE3	0.756	0.753	0.710	0.838	0.858
PRE4	0.579	0.574	0.792	0.690	0.720
PRE5	0.539	0.585	0.802	0.795	0.778

I₀: 1st iteration for CITC. I_F: Final iteration for CITC. A_i: Alpha if deleted. I_FL: Initial loading. F_FL: Final loading. α: Cronbach’s Alpha.

shows the items’ initial CITC value (I_O) before they were dropped. After dropping the items, the final values of CITC (I_F), alpha if deleted (A_i), and Cronbach’s alpha (α) of the purified scale are provided. The alpha value and the CITC value suggest scale reliability. Each construct has a Cronbach’s alpha value greater than 0.8, suggesting internal consistency and a reliable scale [54,55].

Exploratory factor analysis (EFA) was also conducted. The initial factor loadings (I_FL) prior to item deletion and the final factor loadings (F_FL) after scale purification are provided in Table 2. The final factor loading of each item in the purified scale is more than 0.7. The items’ high loading on the underlying construct supports convergent validity, and a low loading of less than 0.4 on the rest supports discriminant validity. Thus, the pilot survey resulted in 25 items. Construct BP, CG, PM, PI and PRE were measured by four items, while five items were used to measure TR.

4.2. Large-Scale Data Collection

The data were collected in a cross-sectional field survey using the questionnaire finalized in the pilot study. The questionnaire was self-administered (between May 2023 and November 2023), and 261 valid responses from SMEs were collected. Participants were asked to mark their responses to the items on a Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7). Doubts, principally due to a poor grasp of English, were clarified in the local Hindi language. As before, the SMEs were bakeries processing food to obtain NOVA Category 3 items and packing them before supplying them to small local retailers (customers).

4.2.1. Sample Size

A sample size greater than 150 is recommended in SEM for a satisfactory result, provided at least three items measure each construct [56,57]. The 261 participants in the study constitute a sufficient sample size.

4.2.2. Normality Test

Before evaluating the structural model, we confirm that the data corresponding to each measurement item are normally distributed. High-skewness and high-kurtosis data indicate non-normality, which impacts estimates in covariance-based SEM. The normality test is performed using Statistical Package for Social Sciences (SPSS, Version 23) software. The skewness and kurtosis values were within the recommended range of ±3 and ±10 [58].

4.2.3. Common Method Bias Test

Cross-sectional surveys are susceptible to common method bias (CMB) because they use a common procedure. Harman’s single-factor test is used to detect Common Method Bias (CMB). We have used SPSS version 23 to obtain the values. We find that a single factor extracted is 31.41% of the total variance, which is less than 50% of the variance [59]. Thus, we conclude there is no threat of common method bias, as a single factor did not account for a significant portion of the EFA variance [60].

4.2.4. Structural Equation Modeling (SEM)

The structure of the causal model is measured and tested using covariance-based structural equation modeling (SEM). The SEM comprises a measurement model and a structural model. Measurement using CFA reveals the degree to which the measurement items represent the latent construct. Scale reliability and validity are assessed using CFA.

The structural model is used to study the proposed relationship between the constructs. A model fit per recommended values is desired when comparing estimated and observed covariance matrices in SEM. Metrics are used to assess the theoretical model’s suitability for use. The explicit accounting of measurement error is possible with covariance-based SEM. Analysis of Moment Structures (AMOS, Version 21) software, an add-on to SPSS, was performed for this purpose.

Confirmatory Factor Analysis (CFA)

The instrument’s validity and reliability are tested using CFA. The observable variables are regressed in CFA on the corresponding latent construct to obtain the regression coefficients (factor loading). The average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha obtained for each construct in CFA are provided in

Table 3. CFA results.

Code	FL	SMC	CR	AVE	Cronbach’s Alpha
Balance of Power (BP)			0.886	0.664	0.882
BP1	0.883	0.779
BP2	0.945	0.894
BP4	0.665	0.442
BP5	0.735	0.540
Contractual Governance (CG)			0.811	0.519	0.808
CG3	0.651	0.424
CG4	0.670	0.449
CG5	0.778	0.605
CG6	0.773	0.598
Partner Match (PM)			0.829	0.550	0.825
PM1	0.621	0.386
PM3	0.774	0.599
PM4	0.764	0.583
PM5	0.795	0.631
Trust (TR)			0.848	0.527	0.847
TR1	0.737	0.543
TR2	0.693	0.480
TR3	0.690	0.476
TR4	0.760	0.577
TR5	0.747	0.558
Partnering Implementation (PI)			0.945	0.812	0.945
PI3	0.904	0.817
PI4	0.928	0.860
PI5	0.912	0.833
PI6	0.860	0.740
Perceived relationship effectiveness (PRE)			0.915	0.730	0.914
PRE2	0.872	0.760
PRE3	0.855	0.732
PRE4	0.804	0.647
PRE5	0.884	0.782

Model: Chi-Square: χ2(df) = 1.647; GFI = 0.883; AGFI = 0.853; CFI = 0.957; NFI = 0.899. SMC: Square multiple correlations. FL: Factor loading. CR: composite reliability. AVE: Average variance extracted.

.

Result and Analysis

It is seen that the CR and Cronbach’s alpha values are higher than 0.5 and 0.7 for each construct, suggesting their reliability [61].

The factor loadings exceed 0.6, suggesting each item’s convergent validity. Moreover, the high factor loading, with a small standard error, suggests that the items adequately measure the underlying construct [57,62].

The square root of each construct’s AVE (diagonal entries in bold in

Table 4. Discriminant validity using Hetrotrait–Monotrait Ratio (HTMT) test.

	PI	TR	PRE	BP	PM	CG
PI	0.901
TR	0.409 ***	0.726
PRE	0.500 ***	0.474 ***	0.854
BP	0.377 ***	0.201 **	0.445 ***	0.815
PM	0.349 ***	0.304 ***	0.321 ***	0.203 **	0.742
CG	0.363 ***	0.297 ***	0.309 ***	0.258 ***	0.250 **	0.720

Note: Significance at: *** p < 0.01, ** p < 0.05.

) is greater than the correlations between constructs, suggesting discriminant validity using the Hetrotrait–Monotrait Ratio (HTMT) test.

Structural Model

The structural model is used to study the proposed relationship between the constructs. AMOS uses the covariance technique to estimate the standardized-β coefficients’ direction, strength, and significance level. The analysis is conducted in three separate steps allowing us to calculate the amount of variance each additional variable explains.

Step 1: We study the direct effects of balance of power (H1), contractual governance (H2), and partner match (H3) on partnering implementation (PI). Also, we study the direct effect of PI on perceived relationship effectiveness (H4).

Step 2: The moderator variable, trust, is introduced into the model.

Step 3: The interaction terms between trust and each construct (BP, CG, and PM) are introduced into the model. To reduce the chances of multicollinearity, independent and moderator variables are mean-centered (to obtain BP_A, CG_A, PM_A, and TR_A) before creating the interaction terms BPxTR, CGxTR, and PMxTR [63].

The variance inflation factor (VIF) and tolerance corresponding to each regression coefficient were tested in SPSS using the independent variables BP_A, CG_A, and PM_A, the three interaction terms BPxTR, CGxTR, and PMxTR, and the dependent variable PI_A, where PI_A is the mean-centered value of partnering implementation (PI).

Result and Analysis

As shown in

Table 5. Collinearity statistics.

	Tolerance	VIF
BP_A	0.699	1.431
CG_A	0.675	1.482
PM_A	0.714	1.401
BPxTR	0.280	3.569
CGxTR	0.288	3.468
PMxTR	0.302	3.317
Dependent Variable: PI_A

, the VIF for each regression coefficient was within the prescribed limit of 5, and the tolerance was above 0.25 [64], suggesting no multicollinearity issue.

Table 6. Results of hypotheses testing.

Independent Variables			Dependent Variables			Hypothesis
			β Coefficients			Results
			Model 1	Model 2	Model 3
Step 1: Direct Relationships
BP	→PI		0.302 ***	0.282 ***	0.163 **	Support (H1)
CG	→PI		0.254 ***	0.208 **	0.218 ***	Support (H2)
PM	→PI		0.259 ***	0.208 **	0.132 *	Support (H3)
PI	→PRE		0.495 ***	0.490 ***	0.488 ***	Support (H4)
Step 2: Moderating Variable
TR	→PI			0.290 ***	0.190 ***
Step 3: Interaction Terms
BPxTR	→PI				0.235 **	Support (H5)
CGxTR	→PI				−0.115	No Support (H6)
PMxTR	→PI				0.168 *	Support (H7)
R2 (PI)			0.223	0.250	0.254

Note: Significance at *** p < 0.01, ** p < 0.05, * p < 0.1.

summarizes the results of the structural model. The predictiveness of this study’s model is indicated by (1) factor loading exceeding 0.6, i.e., each measure accounts adequately for the variance of the underlying latent variable, and (2) standardized beta coefficients, which exceed 0.132 and are statistically significant [65]. In Step 2, the regression model’s R² values increase, suggesting an improvement in the model’s prediction capability. In Step 3, the regression model’s R² values increase further, suggesting more improvement in the model’s prediction capability.

Table 7. Fit statistics for validation and model fitness.

Measures	Recommended Level	Research Model
Absolute fit measures
Chi-square/d.f.	1 to 3	1.99
Goodness of fit index (GFI)	>0.8	0.85
Root mean square error of approximate (RMSEA)	<0.08	0.06
Incremental fit measures
Comparative fit index (CFI)	>0.9	0.93
Incremental fit index (IFI)	>0.9	0.93
Trucker Lewis Index (TLI)	>0.9	0.92
Parsimonious fit measures
Adjusted goodness of fit index (AGFI)	>0.8	0.85
Parsimony goodness of fit index (PGFI)	Low value	0.7

summarizes the fit measurements and demonstrates that the proposed structural model is well-fit per [61] the cut-off criteria.

Balance of power (H1), contractual governance (H2), and partner match (H3) positively and significantly influence partnering implementation, supporting hypotheses H1, H2, and H3. Partnering implementation positively and significantly influences perceived relationship effectiveness, supporting hypothesis H4.

Trust significantly moderates the positive relationship between (1) the balance of power and partnering implementation and (2) the partner match and partnering implementation, thereby supporting hypotheses H5 and H7. However, we could not find evidence of trust strengthening the positive relationship between contractual governance and partnering implementation; thereby, hypothesis H6 is not supported.

5. Discussion and Implication

This study examines the role of trust, a behavioral construct, in the relationship between the focal firm (SME) and its customers (small local retailers). Sharing information to facilitate joint decision-making led to an improved understanding of end-customers’ requirements and the match between supply and demand [50].

The R² value of 0.254 (partnering implementation) indicates that the balance of power, contractual governance, and partner match, and the moderating effects of trust, explain an acceptable amount of variance. Figure 2 provides the model’s results.

Hypothesis H1 is supported. Balance of power has a positive and significant influence on partnering implementation. The firms surveyed are small in terms of the number of employees, and their typical customers. It is not a situation of low bargaining power in which SMEs often find themselves supplying large firms [34]. This oversight can significantly impact the effectiveness of resource allocation strategies and ultimately influence the success of maintaining strong customer relationships [66]. Balance of power is a basic tenet of inter-organizational relationships and enables effective partnering implementation with minimal resource utilization.

Hypothesis H2 is supported. Contractual governance positively and significantly influences partnering implementation. The small firms in this study rely on informal contracts, rather than formal contracts, to govern their relationship with the small retailers. Formal contracts must be drawn up professionally, with compliance ensured by tracking the other party’s actions, and suitable corrective action should be taken as needed [35]. These add to the cost burden of small firms, which suffer from resource limitations. Informal contracts, too, can clarify to an extent the type and detail of information exchanged and decision-making responsibilities, removing some ambiguity in partnering implementation. With informal contracts, the small firms in this study partner efficiently and share information to decide on crucial issues effectively.

Hypothesis H3 is supported. Partner match positively and significantly influences partnering implementation. Small firms that seek partner matches with larger retailers (in terms of resources) benefit in terms of sales and market position, but may have to change their business practices to achieve goals congruent with those of the more prominent firm [41]. Small firms aim to improve short-term financial measures such as cash flow, return on investment, and profit margin on the sale. Operational incompatibility might occur when partnering with large retailers who aim to improve market performance (sales growth and market share) and achieve continuity (customer intimacy, operational excellence, and product leadership).

Hypothesis H4 is supported. Improved partnering implementation positively and significantly influences perceived relationship effectiveness. The departmental structure of large firms can impede timely and accurate information exchange internally and with their customers [42]. The efficient management of purchases and sales might necessitate investment in expensive information systems to bolster operational-level activities, such as tracking sales and inventory status and placing and tracking orders. When supplying to a large retailer, the small firm may be classified as a ‘vendor’, ‘preferred supplier’, ‘exclusive supplier’, or ‘partner’ to manage the relationship efficiently via their purchase section [25]. Suppliers’ relationships with prominent retailers can be volatile due to periodic changes in purchasing staff, making establishing trust and efficient communication challenging. Relationship effectiveness would be lacking, with each entity independently pursuing its goals and objectives.

Hypothesis H5 is supported. Trust significantly moderates (strengthens) the positive relationship between balance of power and partnering implementation. Trust in this study is the belief that the customers shall act in the firm’s best interest regardless of its ability to monitor their behavior. Balance of power is unlikely with a small firm partnering with a large retailer if the sale of the firm’s products does not contribute significantly to the large firm’s sales volume, and the small firm relies on the large firm significantly for product promotion and sales volume [32]. The balance of power shall be tilted in favor of the large retailers.

This study consists of small firms supplying small local retailers with interdependencies; thereby, a balance of power is more likely. The effectiveness of balance of power in partnering implementation has to be understood in the context of trust. The timely exchange of information indicates a healthy relationship based on trust. Trust results from customers aiding the firm in decision-making by sharing relevant information.

Hypothesis H7 is supported. Trust significantly moderates (strengthens) the positive relationship between partner match and partnering implementation. The small firm may be considered another ‘vendor’ when supplied to a large retailer. A prominent retailer with several suppliers may not consider dedicating enough resources to build and maintain relationships with small firms [22]. Even with trust, the major incompatibility in strategic and operational orientation may prove challenging to overcome.

The small local retailers, the firms’ customers in this study, will likely understand its resource constraints. The role of partner matches in effectively collaborating has to be understood in the context of trust. The length of the relationship with the customers contributes to the trust level and extension of the relationship [43]. It thus helps sustain a healthy relationship in the long term, to each other’s benefit.

Hypothesis H6 is not supported. We could not find evidence of trust significantly moderating (strengthening) the positive relationship between contractual governance and partnering implementation. In this study, there is no formal written agreement of the firm with the customers in the form of (1) detailed roles, actions, and schedules and (2) standard operating procedures (policies, guidelines, and forms). The lack of a formal contract suggests a trust-based relationship with the belief that the customer shall not behave opportunistically. The effectiveness of informal contracts in partnering implementation has to be understood in the context of trust. With a high level of trust, the time at which information exchange occurs and the information type and detail tend to be loosely defined due to a lax attitude. Further, the decision-making roles and responsibilities may not be clear. These aspects add to the ambiguity of partnering implementation without any formal agreement.

Implications

Hypothesis H1 is supported. It is essential to recognize and address the role of power dynamics in shaping resource allocation decisions within organizations. Resource commitment is not significant when partnering firms are interdependent. This is unlike a situation where a firm has few customers responsible for a significant portion of the sales volume without contributing significantly to the customer firm’s sales volume.

Being close to the end-customer with few intermediaries is advantageous. With low reliance on any one customer (retailer) and with opinions and views from different sources, the firms can identify end-customer needs and wants, decide on new products, and price the product to compete better. The firm depends on the customers to promote its product over the competitors, and its success ultimately relies on a product that is so desirable that customers become promoters [32]. This highlights the level of interdependency.

Hypothesis H2 is supported. A formal contract with detailed roles, actions, and schedules is needed when partnering to sell a new product, especially in the early phases of the partnership [67]. When the risk involved in new product development is low (e.g., if the product is not a high-technology offering), the cost of product introduction is low (e.g., when the product is sold locally without aggressive multi-media marketing), and both parties have knowledge of one another, the contract need not be complex.

Hypothesis H3 is supported. Partnering with firms with similar resource constraints, objectives, operating characteristics, and work culture requires little change in business practices [41]. A relationship (i) with interdependencies and without domination, (ii) governed by informal contracts, and (iii) without a requirement for either partner to change business practices promotes the effective usage of limited resources for information exchange and collaborative decision-making. It enables the partners to improve the service level without compromising cost. For example, at the operation level, it promotes timely and accurate point-of-sales data to improve forecasting and planning production and inventory, which is crucial, especially for perishable products.

Hypothesis H4 is supported. Ideally, partnering firms should have reduced decision hierarchy and communication barriers between departments. Face-to-face and telephonic interaction between trading partners (possible in the case of low numbers) results in personal relationships and understandings, and effectiveness in the business relationship. Information exchange and collaborative decision-making sustain a productive and successful collaboration [50].

Hypothesis H5 and H7 are supported, while H6 is not. Power balance with interdependencies makes it likely that a firm shall devote resources to gain its partner’s trust. With trust, the firms shall respond with understanding to each other needs, such as exchanging information crucial for strategic and operational planning. Trust is also more likely with goal congruence [43]. Trust can overcome minor incompatibilities in objectives, operating characteristics, and work culture to improve partnering implementation. Though trust reduces the occurrence of conflicts and increases the likelihood of amicable resolution, a formal contract (without complexities) is recommended to ensure the exchange of relevant information (as well as its timeliness and accuracy) and determine the scope of joint decision-making.

6. Conclusions and Direction for Future Research

This paper provides a theoretical basis for analyzing the positive effect of trust on partnering implementation. Trust implies that the customers shall act in the firm’s best interest regardless of its ability to monitor their behavior [27]. Partnering implementation refers to information exchange and joint decision-making.

We study the relationships between the following:

(a) A balance of power (between partnering entities with resource interdependency);

(b) A collection of formal and informal procedures to govern the resource exchange;

(c) A partner match (regarding goals, objectives, operational philosophies, and work cultures) and partnering implementation.

We also examine the relationship between partnering implementation and perceived relationship effectiveness. Perceived relationship effectiveness indicates that each party fulfills its commitments, has regard for the other party, and amicably resolves disagreements.

It is proposed that partnering implementation has a positive influence on perceived relationship effectiveness, and trust strengthens the positive influence of (a) balance of power, (b) contractual governance, and (c) partner match on partnering implementation. By examining the effectiveness of (a), (b), and (c) (as defined above) within the context of trust, we gain further understanding of how context influences the relationship between (a), (b), and (c) and partnering implementation. The instrument for measuring (a), (b), (c), trust, partnering implementation, and perceived relationship effectiveness is developed for the setting in which SMEs (bakeries) supply to small local retailers from whom end-customers purchase.

The following should be noted:

(1) The situation is not that of low bargaining power in which SMEs often find themselves when supplying large firms;

(2) The small firms in this study rely on informal contracts rather than formal contracts to govern their relationship with the small retailers;

(3) Small local retailers with similar resource constraints are likely compatible regarding objectives, operating characteristics, and work culture.

Data are collected from the bakeries, and the instrument is validated using CFA. The hypothesized relationships between the constructs are tested using covariance-based SEM. In line with RDT, a higher level of (a) balance of power, (b) contractual governance, and (c) partner match promotes partnering implementation through the timely and accurate exchange of information, which facilitates decision-making and improves the perception of relationship effectiveness.

The small trading partners in this study predominantly have face-to-face and telephonic interactions with each other. The traditional trading method is possible due to the small number of partners. The IT components used (for ordering and payments) are low-priced internet and mobile technology [14]. Knowledge of one another and a simple transaction process reduces risk and uncertainty and leads to trust with responsiveness in problem-solving. The power balance (with interdependencies) and goal congruence increase the likelihood of sustained cooperation [23]. The positive relationship between balance of power and partnering implementation, as well as that between partner match and partnering implementation, are stronger with a higher level of trust than with a lower level.

Empirical evidence failed to suggest that the positive relationship between Contractual governance and partnering implementation is stronger for a higher level of trust than for a lower level. Though lack of trust results in complex contracts and governance systems [35], with a high level of trust and in the absence of formal written agreements of the firm with the customers, clarity in terms of time, accuracy, and relevance of information exchanged may be lacking, compromising decision-making and resulting in poor outcomes. A high level of trust may add to the ambiguity of partnering implementation with an informal agreement.

The objective of management is to improve relationship effectiveness, as indicated by relationship measures, through effective partnering implementation. The existing research has some limitations, as the data were collected from the supplier’s perspective. Future work could also broaden the scope by considering the customer’s perspective.