Analyzing the Interaction of Industry 4.0 and Sustainable Global Marketing Channel Development with Necessary Condition Analysis: The Role of Inter-Organizational Trust

Abstract

The purpose of this study is to examine the interaction between Industry 4.0 technologies, inter-organizational trust, sustainable distributor channel development, and marketing channel operational performance. The research employed a quantitative approach, collecting data from 131 respondents in Canadian and U.S. global firms with over 400 employees. The analysis utilized partial least squares structural equation modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The study revealed that inter-organizational trust is both a significant determinant and a necessary condition for marketing channel operational performance. While Industry 4.0 technologies emerged as a significant determinant, they were not identified as a “must-have” necessary condition. Notably, distributor sustainability development proved to be an insignificant determinant, but still a “must-have” necessary condition for marketing channel operational performance. This study uniquely contributes to understanding Industry 4.0 and marketing channel dynamics by integrating inter-organizational trust analysis with NCA methodology. By identifying trust as a significant determinant and a “must-have necessary condition”, the research provides practical guidance for managers navigating technological adoption in global marketing channels. The findings challenge conventional assumptions about sustainable development while emphasizing trust’s crucial role in the digital age, offering valuable insights for achieving high marketing channel operational performance during the transformation to Industry 4.0.

1. Introduction

The convergence of Industry 4.0 technologies and global marketing channel development represents a critical frontier in contemporary business research, with inter-organizational trust emerging as a pivotal factor in this dynamic landscape. The emergence of Industry 4.0 marks a transformative shift in business processes, driven by the integration of advanced digital technologies such as Big Data (BD), the Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing (CC) into operational workflows [1]. As industries increasingly integrate advanced technologies such as the IoT, AI, CC, and BDA, the dynamics of marketing channels are undergoing profound transformations. This evolution necessitates a deeper understanding of how these technological advancements influence inter-organizational relationships and trust, which are vital for successful collaboration in global distribution channels. The significance of this transformation lies in its potential to enhance competitive advantage, customer satisfaction, and sustainability—factors that are paramount in today’s complex market ecosystem [2,3]. Industry 4.0, characterized by the digital transformation of manufacturing and production processes, fundamentally alters how businesses interact with each other and their customers. While integrating digital technologies into marketing strategies enables firms to respond more effectively to market demands and improve their resilience [4,5], it also introduces new data privacy and security challenges. In this context, trust emerges as a crucial facilitator of smoother communication, collaboration, and knowledge sharing, essential for navigating global marketing channels’ complexities [6]. Understanding how trust can mitigate these concerns is vital for fostering long-term relationships in the digital age [7].

In the increasingly competitive global marketplace, inter-organizational trust becomes even more pronounced, particularly as companies seek to build strategic alliances to enhance their market positioning. Trust influences organizations’ willingness to share information and resources, significantly impacting their ability to innovate collaboratively [8]. This dynamic is especially critical for small and medium-sized enterprises (SMEs) which often lack the resources to compete independently in global markets. These organizations can enhance their capabilities through trust-based partnerships and access new markets [9,10], creating a more resilient and inclusive global distribution network. The COVID-19 pandemic has further accelerated the shift toward digital marketing and highlighted the need for robust, adaptive marketing strategies. Organizations with trust-based relationships have demonstrated a superior ability to pivot quickly and effectively, positioning themselves to navigate challenges better and seize emerging opportunities [11]. However, despite the recognized importance of trust in digital transformation, there remains a significant gap in understanding the specific mechanisms through which Industry 4.0 technologies influence trust formation and maintenance in global marketing channels.

This study’s primary research questions are as follows: What is the impact of inter-organizational trust and the use of Industry 4.0 technologies on distributor sustainability development and marketing channel operational performance when aiming to develop sustainable global marketing channels? Also, does inter-organizational trust play a role when aiming to take advantage of Industry 4.0 technologies? These questions are essential, as the role of sustainability in all areas of business and society significantly impacts the survivability of companies as we know them today [12]. Consequently, the research objectives of this paper are, first, the examination of the interaction between the key constructs of the study, and second, the identification of exogenous constructs that are “must-have” necessary conditions and those that are just sufficient conditions for the marketing channel operational performance.

The remainder of this paper is structured as follows: First, we present a comprehensive literature review covering Industry 4.0, sustainable global marketing channels, inter-organizational trust, and marketing channel operational performance. Next, we outline our methodology, including sample characteristics and measurement development. We then present our data analysis and results and discuss our findings. Finally, we conclude with implications for theory and practice and suggestions for future research.

2. Literature Review

2.1. Industry 4.0 and Global Marketing Channels

The advent of Industry 4.0 represents a transformative era in business processes, characterized by the integration of advanced digital technologies (e.g., Big Data (BD), Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing (CC)) in business operations. This digital revolution fundamentally reshapes how businesses operate and interact with their stakeholders. Ejsmont et al. [1] conducted a comprehensive bibliometric analysis demonstrating how Industry 4.0 technologies optimize resource utilization and minimize waste throughout product lifecycles, marking a momentous evolution in business operations, which, if done correctly, can positively influence corporate sustainability. This technological transformation is particularly impactful for micro, small, and medium enterprises (MSMEs) in emerging economies, where Industry 4.0 can mobilize development and competitiveness [13,14]. Arromba et al. [15] further elucidate how integrating advanced technologies enables firms to respond swiftly to evolving consumer preferences and sustainability demands, creating more agile marketing strategies, e.g., forming global distribution channels. Business sustainability concerns are often difficult to manage due to their complex nature, so they must be comprehensively addressed via sustainable business models [16].

The integration of Industry 4.0 technologies is profoundly transforming marketing channel development and the concept of sustainable marketing. Kowalska [17] argues for rediscovering the traditional marketing mix to incorporate sustainability-focused strategies driven by growing environmental consciousness and demands for corporate transparency. Supporting this transformation, Hussain et al. [18] provide empirical evidence of the competitive advantage gained by firms that successfully integrate sustainable practices into their marketing strategies, noting that consumers increasingly support brands aligned with environmental and social responsibility values. Building on these insights, Kumar et al. [19] demonstrate how Industry 4.0 technologies facilitate the implementation of circular economy practices, leading to more sustainable product lifecycles and marketing approaches. Thus, there is increasing support for stakeholders requiring businesses to behave sustainably, especially regarding Industry 4.0 and how we improve the processes in place.

The impact of Industry 4.0 on distribution networks and supply chains is particularly significant. Furstenau et al. [20] highlight how these technologies enhance sustainability by precisely tracking materials and products, significantly reducing environmental impact in complex global networks. This increased visibility and efficiency reduce waste, optimize logistics, and support more sustainable decision-making across global supply chains [21,22]. Mustafa et al. [23] further emphasize that companies leveraging Industry 4.0 technologies can create more transparent and sustainable supply chains, enhancing brand reputation and customer loyalty through ethical and sustainable business practices.

Intelligent manufacturing and production systems, enabled by Industry 4.0 technologies, contribute substantially to sustainability through various mechanisms. Predictive maintenance powered by IoT sensors and AI extends equipment life and reduces downtime, while digital twins and simulation technologies enable process optimization before physical implementation [24]. Industry 4.0 also facilitates the adoption of circular economy principles in global distribution channels, with IoT connectivity enabling product-as-a-service business models and improved tracking supporting remanufacturing initiatives [25]. IoT sensors and AI significantly enhance environmental monitoring capabilities, enabling real-time emissions monitoring and optimizing energy efficiency across global operations [26]. Overall, these efforts improve environmental impact monitoring and management systems, increasing reporting and transparency and enhancing businesses’ trust, reputation, and legitimacy [27].

However, integrating Industry 4.0 and sustainability in global distribution channels presents significant challenges that organizations must address. These include substantial investment requirements in technology and infrastructure, potential workforce displacement, cybersecurity and data privacy concerns, and ensuring equitable access to these technologies, particularly for SMEs and firms in developing economies [28]. Despite these challenges, Industry 4.0’s transformative potential in creating more sustainable and efficient distribution channels continues to drive its adoption across global business ecosystems.

2.2. Inter-Organizational Trust

The digitalization of business processes has elevated the importance of inter-organizational trust as a critical facilitator of collaboration and knowledge sharing between firms adopting Industry 4.0 technologies. Zaheer et al. [29] define inter-organizational trust as “the extent of trust placed in the partner organization by the members of a focal organization”. This collective trust orientation represents a distinct construct from interpersonal trust between boundary spanners, though the two are interrelated and mutually reinforcing in organizational relationships [29,30].

The conceptualization of inter-organizational trust in the digital age encompasses multiple dimensions. Zaheer et al. [29] identify reliability, predictability, and fairness as crucial components in organizational dealings. Building on this framework, Sako [31] delineates trust into three essential types: contractual trust (expectation of promise fulfilment), competence trust (confidence in partner capabilities), and goodwill trust (belief in the partner’s positive intentions). In the context of Industry 4.0 and global marketing channels, these trust dimensions become particularly vital as organizations navigate complex digital transformations and collaborative initiatives.

Trust’s role in marketing channel relationships has evolved significantly with digital advancements. Through repeated interactions, trust becomes institutionalized in organizational routines and practices [32], creating stable patterns of engagement that persist even as personnel changes. This institutionalization process is critical in global marketing channels, where trust-based routines provide continuity across geographic and cultural boundaries. High levels of inter-organizational trust yield tangible benefits, including reduced negotiation costs, decreased conflict, and improved exchange performance between firms [33].

The adoption of Industry 4.0 technologies has introduced new dimensions to trust-building processes. In this rapidly evolving landscape, trust facilitates critical activities such as information sharing about new technologies, coordinated investments in digital infrastructure, and collaborative innovation between channel partners. Organizations with solid trust relationships demonstrate a greater willingness to share sensitive information and experience reduced fears of opportunism, enabling tighter integration of operations and data flows across organizational boundaries [34]. Industry 4.0 technologies can enhance inter-organizational trust because of the legitimacy created via improved processes that improve sustainability.

However, building and maintaining inter-organizational trust in the digital age presents unique challenges. Geographic, cultural, and institutional differences between firms can complicate trust development [35], while the increasing interconnectedness through digital technologies introduces new vulnerabilities. Bachmann and Inkpen (2011) [36] emphasize the importance of institution-based trust in international business relationships, highlighting the need for carefully developed shared norms, aligned incentives, and robust conflict resolution mechanisms.

The relationship between trust and technology adoption has become increasingly symbiotic. As Kwon and Suh [37] observe, supply chain integration requires high levels of trust to overcome perceived risks in information sharing and collaborative planning. Simultaneously, emerging Industry 4.0 technologies, such as blockchain and IoT, are becoming instrumental in building and maintaining trust through their inherent characteristics of transparency and immutability [38]. This technological enhancement of trust mechanisms suggests a positive feedback loop between trust and technology adoption.

2.3. Industry 4.0 Technologies

The convergence of Industry 4.0 technologies and sustainability initiatives transforms distribution networks and supply chain operations. Advanced technologies like the Internet of Things (IoT) and Cyber–Physical Systems (CPS) enable seamless vertical integration and information sharing between supply chain partners [39,40], fundamentally changing how organizations approach sustainable development. As crucial intermediaries between manufacturers and end consumers, distributors play a vital role in reducing industries’ environmental and social impacts through adopting sustainable practices [41].

Implementing Industry 4.0 technologies drives significant improvements in sustainable distribution practices through multiple channels. IoT sensors and predictive analytics enable more precise inventory management and route planning, minimizing unnecessary transportation and storage [42]. Big Data analytics facilitate accurate demand forecasting, optimize inventory management, and reduce overproduction [43]. Digital twins and simulation capabilities allow distributors to test and optimize operations virtually, supporting more sustainable practices before physical implementation [44].

Empirical evidence strongly supports this relationship between Industry 4.0 and sustainability performance. Studies in various sectors demonstrate significant improvements in sustainability metrics following the implementation of Industry 4.0. For instance, Ghaithan [45] found direct positive correlations between technology adoption and enhanced sustainability outcomes in the petrochemical sector. Similarly, research in the automotive industry shows that integrating Industry 4.0 technologies with sustainable supply chain management practices leads to improved sustainability performance while meeting growing consumer and regulatory demands [46]. The benefits extend beyond environmental considerations to encompass social and economic dimensions, with improved working conditions through automation [47] and enhanced economic viability through streamlined operations [48,49]. These theoretical and empirical insights lead to the first hypothesis.

Hypothesis 1. 

Inter-organizational trust positively and significantly impacts Industry 4.0 technologies in global sustainable marketing channels.

2.4. Distributor Sustainability Development

The relationship between trust and sustainable development represents another crucial dimension in distribution networks. Research demonstrates that inter-organizational trust fosters collaboration, knowledge sharing, and long-term commitment, essential for achieving sustainable outcomes [50]. When distributors and partners establish solid trust-based relationships, they are more likely to engage in joint sustainability initiatives and share resources to improve environmental and social performance [51]. Trust facilitates open communication and transparency, enabling a better understanding and addressing sustainability challenges throughout the marketing channel [52].

Trust-based relationships reduce transaction costs and opportunistic behaviour, allowing distributors to invest more resources in sustainable practices and technologies [50,52]. Empirical studies have consistently shown that higher levels of trust between distributors and suppliers lead to improved environmental management practices, reduced waste, and more efficient resource utilization [52,53]. This relationship is further strengthened by effectively implementing sustainability standards and certifications across distribution networks [51]. Wang [54] found that inter-organizational trust influences the effects of structural holes in distributor networks, thereby enhancing sustainable competitive advantage and contributing to the strategic positioning of distributors in their respective markets. These theoretical and empirical insights lead to the second and third hypotheses.

Hypothesis 2. 

Industry 4.0 technologies positively and significantly impact distributor sustainability development in global marketing channels.

Hypothesis 3. 

Inter-organizational trust positively and significantly impacts distributor sustainability development in global marketing channels.

2.5. Marketing Channel Operational Performance

The impact of Industry 4.0 technologies on marketing channel operational performance has become increasingly evident through enhanced information flow and communication capabilities. Irmayani [55] demonstrates how collaborative marketing partnerships, enabled by digital technologies, improve channel performance through enhanced collaboration and information sharing. These findings align with Potjanajaruwit’s (2023) [56] research, which emphasizes that innovation capabilities derived from Industry 4.0 technologies significantly enhance business performance through improved operational efficiencies and market responsiveness.

The technological transformation of marketing channels is manifesting across multiple operational dimensions. Advanced robotics and automation in warehousing and logistics operations significantly reduce order fulfilment times and errors, enhancing customer satisfaction [57,58]. Implementing blockchain technology, for example, has improved transparency among channel members, reduced transaction costs, and enhanced overall channel performance [59]. Big Data analytics (BDA) and AI enable deeper customer insights and personalized marketing efforts [60]. IoT devices facilitate real-time product tracking and improved inventory management [61].

The evolution of multichannel marketing in the Industry 4.0 era further demonstrates the impact of technology on performance. Tortorella et al. [62] and Sun et al. [52,63] found that integrating Industry 4.0 technologies with lean manufacturing principles leads to superior operational performance in distribution networks. Similarly, Ghadge [64] demonstrated that Industry 4.0 technology adoption enhances supply chain agility and resilience, crucial factors in today’s dynamic marketing environments. These theoretical and empirical insights lead to the fourth hypothesis.

Hypothesis 4. 

Industry 4.0 technologies positively and significantly impact marketing channel operational performance in global marketing channels.

The relationship between sustainability initiatives and operational performance represents another critical dimension in distribution channel effectiveness. Distributors implementing sustainable practices often experience improved operational efficiency [65]. Studies have shown a significant increase in channel productivity following using green technologies and sustainable logistics solutions [66]. Digitalization supports sustainability initiatives and enhances channel integration, benefiting small and medium-sized enterprises [67].

Sustainable practices contribute to performance improvements through multiple mechanisms. They lead to cost reductions and improved resource utilization [68], while continuous monitoring of sustainability criteria promotes operational efficiencies across various sectors [52,69]. Porter and Kramer [70] demonstrate that sustainable practices enhance brand reputation and customer loyalty, indirectly contributing to improved channel performance through increased sales and market share. This is particularly relevant as consumers increasingly value sustainability in purchasing decisions [71], enhancing customer loyalty and market share for environmentally conscious distributors. These theoretical and empirical insights lead to the fifth hypothesis.

Hypothesis 5. 

Distributor sustainability development positively and significantly impacts marketing channel operational performance in global marketing channels.

Inter-organizational trust has the potential to be a fundamental driver of marketing channel operational performance. Trust facilitates cooperation, reduces conflict, and promotes information sharing between channel partners [72,73]. When firms trust one another, they share valuable market information, customer insights, and operational data more readily, which may lead to better decision-making and improved channel performance.

Morgan and Hunt’s [74] seminal work established the role of trust in successful relationship marketing strategies, with subsequent studies confirming that higher inter-organizational trust may lead to enhanced channel efficiency and effectiveness [75]. Trust reduces transaction costs by minimizing the need for formal contracts and monitoring [33], enables more efficient resource allocation [76], and fosters innovation and problem-solving [77]. Recent meta-analyses have corroborated these findings, revealing significant positive correlations between trust and various performance measures, including market share, sales growth, and profitability [78]. These theoretical and empirical insights lead to the sixth hypothesis.

Hypothesis 6. 

Inter-organizational trust positively and significantly impacts marketing channel operational performance in global marketing channels.

2.6. Conceptual Model

The conceptual model presented in Figure 1 was developed through an extensive review of the extant literature, integrating key frameworks and findings from previous studies. It serves as a visual representation of the relationships between the relevant constructs under investigation in this research. This model illustrates the proposed research hypotheses, providing a structured approach to understanding the anticipated interactions and outcomes. By synthesizing the insights gained from the literature, the conceptual model not only offers a theoretical foundation for the study but also guides the formulation of the research questions and the development of the methodology. It highlights the key dimensions expected to influence each other and sets the stage for testing these relationships in the empirical phase of the study.

3. Methodology

3.1. Sample and Respondent Characteristics

The sample was comprised jointly with the Centiment marketing research firm, which can target both B2C and B2B respondents. Centiment creates and profiles marketing panel members before the actual survey procedure. Upon being accepted to reply to surveys for remuneration, the panel members endure a series of security and other appraisals, which act as the basis for the target profiles. Engagement is observed with quality metrics to ensure that the respondents deliver decent survey responses reliably [79].

During Winter 2024, 944 responses were collected in Canada and the U.S., with respondents aged at least 18. The survey included qualification questions to confirm that suitable respondents were contacted. First, the researchers were interested in respondents from global or international firms. Second, the respondents were expected to be working in marketing and related fields or operations and not, for example, in finance. Third, the researchers wanted the respondents to employ at least 400 employees, as smaller firms have not been as quick to implement Industry 4.0 technologies (i.e., AI, CC, BDA, IoT) [80]. Finally, the respondents’ firms needed to be at least in the limited deployment stage of Industry 4.0 technologies (

Table 1. Qualification questions for the respondents.

Qualification Criteria	#	Question	Original Sample (N = 944)	%	Final Sample (N = 131)	Reference
Global vs. domestic	1	Not involved in global value chain activities.	813	86.1		[82]
Respondent’s affiliation	1	Other than 1. Marketing, business development & sales, 2. Distribution or 3. Operations.				[83]
Firm size	1	Less than 400 employees.				[84]
Deployment stage of Industry 4.0 technologies	1	Unaware of any marketing analytics applications.				[85]
	2	Aware of the Industry 4.0 technologies.
	3	Knowledge of the Industry 4.0 technologies but have not yet evaluated any.
	4	Evaluation of the potential of the Industry 4.0 technologies.
	5	Limited deployment of the Industry 4.0 technologies.	42	4.5	32.1%
	6	General deployment of Industry 4.0 technologies indicating wide impact on critical business processes.	57	6.0	43.5%
	7	Mature deployment for a longer period of time with legacy support.	32	3.4	24.4%

). Based on these qualification questions, the researchers perceive the sample to be very relevant for this research. The final sample comprised 131 responses, congruent with the current BDO report [81], disclosing that only about 5% of companies use or have used Industry 4.0 technologies.

3.2. Measurement and Questionnaire Development

The researchers established a survey questionnaire, amending the variables from extant research to gather data concerning the constructs (

Table 2. Target construct measurement.

Construct	Indicator Variable	Source
Industry 4.0	Big Data analytics has had a significant impact on building sustainable distribution channels (IND1).	[86]
	Cloud computing has had a significant impact on building sustainable distribution channels (IND2).
	The Internet of Things (IoT) has had a significant impact on building sustainable distribution channels (IND3).
	Artificial intelligence has had a significant impact on building sustainable distribution channels (IND4).
Marketing channel operational performance	Our delivery cycle times are good (MCOP1).	[87,88]
	Our manufacturing cycle times are good (MCOP2).
	Our missing/wrong/damaged/defective products shipped are at a low level (MCOP3).
	Our on-time delivery time performance is good (MCOP4).
	Our warranty/returns processing costs are at a low level (MCOP5).
Distributor sustainability development	We are engaging in distributors’ sustainability performance assessment through formal evaluation, monitoring and auditing using established guidelines and procedures (DSD1).	[89]
	We are engaging in training/education in sustainability issues for distributors’ personnel (DSD2).
	We are engaging in joint efforts with distributors to improve their sustainability performance (DSD3).
Inter-organizational trust	Our distributors have always been even-handed in their negotiations with us (TRU1).	[21]
	Our distributors may use opportunities that arise to profit at our expense (TRU2).
	Based on experience, we cannot confidently rely on our distributors to keep promises made to us (TRU3).
	We are hesitant to transact with our distributors when the specifications are vague (TRU4).
	Our distributors are trustworthy (TRU5).

).

3.3. Method of Statistical Analysis

The structural model was examined with Smart-PLS. Two alternatives for structural equation modelling exist: partial least squares (PLS-SEM) and covariance-based (CB-SEM). The measurement idea and purpose vary between them [90].

In the CB-SEM, the constructs are represented by factors; in the PLS-SEM, they are represented by components [91,92]. Additionally, the PLS-SEM includes several statistical add-ons that do not exist in CB-SEM, such as the Necessary Condition Analysis (NCA). As the NCA will be used in this study, the PLS-SEM was selected as the statistical method. To support this selection, it is imperative to acknowledge that the goal is to predict the target construct (marketing channel operational performance) and identify essential antecedent constructs. Up-to-date recommendations for assessing the quality of the models were adhered to [93].

NCA is a relatively new statistical tool that was recently incorporated into SmartPLS. SPSS (version 29.0.0.0 (241)) and the latest version 4.1.0.9 of SmartPLS were used for statistical analysis. NCA is an exceptional statistical tool, as it can distinguish the degree of a necessary condition that must be achieved to obtain an exact outcome in the target endogenous construct, marketing channel operational performance. When using the NCA, it is feasible to calculate the necessary degree of Industry 4.0 technologies, distributor sustainability development, and inter-organizational trust to recognize a precise level of marketing channel operational performance [94].

The idea of the NCA is to detect which constructs are “necessary” (necessity logic), and which constructs are “sufficient” (sufficiency logic) for the endogenous construct [95,96]. Customary steps were taken to perform the NCA based on suggestions stated in earlier research. These include the clarification of the objectives of the study and relevant theories, provision and examination of the data, running the conventional PLS-SEM, assessing the reliability and validity of the measurement model, transferring the latent variable scores to the dataset in unstandardized form, the NCA, assessing the structural model, and reporting the results [94] (Appendix A).

4. Data Analysis

4.1. Background Data

Table 3. Sample description (N = 131).

#	Country of Residence	N (%)		Years with the Organization	N (%)
1	Canada	19 (14.5%)	1	Less than year	5 (3.8%)
2	United States	111 (84.7%)	2	2–5 years	35 (26.7%)
3	Other	1 (0.8%)	3	6–10 years	36 (27.5%)
	Age group	N (%)	4	11–15 years	25 (19.1%)
1	19–24	3 (2.3%)	5	16–19 years	12 (9.2%)
2	25–28	8 (6.1%)	6	Over 20 years	18 (13.7%)
3	29–34	19 (14.5%)		Education	N (%)
4	35–40	27 (20.6%)	1	High school or less	18 (13.7%)
5	41–45	11 (8.4%)	2	Some college–no degree	30 (22.9%)
6	46–54	15 (11.5%)	3	College diploma	4 (3.1%)
7	55–64	39 (29.8%)	4	Associate	18 (13.7%)
8	+65	9 (6.9%)	5	Bachelor’s	44 (33.6%)
			6	Master’s	13 (9.9%)
			7	Doctorate	4 (3.1%)
			8	Other	0 (0.0%)

depicts the sample. The respondents originated from various industries, including finance and insurance, information and cultural industries, education services, manufacturing, construction, and real estate.

4.2. Preparation and Checking of Data

The preparation and checking of data include the assessment of the adequacy of the sample size, the data distribution, the existence of outliers, and the measurement level/coding of scales [94]. Cochran’s rule for continuous data was used to evaluate the adequacy of the sample size [97]. It specified the necessity for 61 responses when the alpha level of 0.25 in each tail was 1.96, the anticipated standard deviation on a 5-point scale was 1.0, and the margin of error was 0.25. To assess the sufficiency of the sample size concerning the use of PLS-SEM, a sample size of 69 was needed when the significance level was 5%, and the path coefficient level was at least 0.21 [90]. Accordingly, the sample size was adequate.

Regarding the data distribution, using NCA or PLS-SEM does not necessitate any distributional assumptions [94]. It is, however, plausible that highly skewed data may enhance standard errors in statistical significance testing with bootstrap analysis, reducing statistical power [94]. To evaluate the skewness (distribution balance) and kurtosis (peakedness), the data were examined in SPSS (

Table 4. The data’s mean values, standard deviations and normality with z-scores for skewness and kurtosis.

Construct	Variable *	Mean	Std. Dev.	Skewness	Kurtosis	Kolmogorov–Smirnov **	Sign.	Shapiro–Wilk	Sign.
Industry 4.0	IND1	4.08	0.94	−0.91	0.55	0.23	***	0.82	***
	IND2	4.18	0.85	−0.73	−0.24	0.26	***	0.81	***
	IND3	4.05	0.92	−0.70	−0.07	0.26	***	0.84	***
	IND4	4.04	0.96	−0.76	0.07	0.24	***	0.83	***
Marketing channel operational performance	MCOP1	4.24	0.86	−1.16	1.36	0.27	***	0.78	***
	MCOP2	4.19	0.96	1.24	1.36	0.27	***	0.78	***
	MCOP3	3.98	1.07	−0.91	0.19	0.23	***	0.83	***
	MCOP4	4.29	0.90	−1.19	0.91	0.31	***	0.76	***
	MCOP5	3.95	1.08	−1.02	0.62	0.23	***	0.83	***
Distributor sustainability development	DSD1	3.98	1.00	−0.77	−0.04	0.22	***	0.84	***
	DSD2	3.95	0.99	−0.89	0.64	0.23	***	0.84	***
	DSD3	4.01	0.99	−0.89	0.46	0.22	***	0.83	***
Inter-organizational trust	TRU1	3.73	0.98	−0.60	0.27	0.23	***	0.87	***
	TRU2	3.46	1.04	−0.09	−0.52	0.23	***	0.89	***
	TRU3	2.96	1.29	0.14	−1.04	0.18	***	0.91	***
	TRU4	3.45	1.21	−0.40	−0.64	0.17	***	0.89	***
	TRU5	4.18	0.87	−1.09	1.10	0.25	***	0.80	***

* See Table 2 for the shortcuts. ** With a Lilliefors Significance Correction, *** less than 0.001.

). The extant literature has quantified the frequently used standards for skewness and kurtosis to be 2.58 (0.01 level of significance) and 1.96 (0.05 level of significance) [98], with higher values signifying kurtosis and skewness in the data. These values are, however, fitting just for small samples (N < 50). For medium-sized samples (50 < N < 300), the recommended threshold value not to exceed is 3.29 [99].

The Kolmogorov–Smirnov and Shapiro–Wilk tests completed the normality test, demonstrating significance (Table 4) for all variables, indicating non-normality in the data. As the data analysis showed a lack of skewness, kurtosis, and non-normality, the data could have reduced statistical power.

The literature instructs using Mahalanobis distance in multivariate analysis to examine outliers in the data. This method identifies abnormal response combinations in the variable set [98,100]. This analysis discovered six outliers in the dataset, which were removed.

Finally, scales were measured and coded using metric or quasi-metric Likert scales with theoretically expected relationships. This completes the stage of preparing and checking the PLS-SEM and NCA data.

4.3. Evaluation of the Reliability and Validity of the Measurement Model

At this stage, the first phase is to evaluate the individual scales to measure the relevant constructs. The evaluation of the measurement model starts with assessing the indicators’ reliability. A bias-corrected and accelerated bootstrapping analysis was executed to establish the significance of indicators. The extant literature has indicated that if loadings surpass the value of 0.70, they should be retained in the model. In contrast, indicators with loadings between 0.40 and 0.70 should be considered for potential elimination. All loadings exceeded the 0.70 threshold, except for the variables TRU2, TRU3, and TRU4. These variables were removed from further analysis because their loadings were less than 0.40.

The reliability of internal consistency was examined after assessing the indicators’ reliability (

Table 5. Construct reliability and convergent reliability.

Construct	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted (AVE)
Distributor sustainability development	0.894	0.934	0.824
Industry 4.0	0.836	0.890	0.669
Marketing channel operational performance	0.852	0.895	0.635
Inter-organizational trust	0.702	0.870	0.769

). The existing literature considers Cronbach’s alpha a cautious measure of reliability. In contrast, the composite reliability (target range between 0.70 and 0.95) tends to overstate the internal consistency reliability. Thus, the actual reliability falls between these criteria, with Cronbach’s alpha value as the lower limit and the composite reliability as the upper limit for internal consistency reliability [90]. In terms of convergent validity, which is usually assessed using the average variance extracted (AVE) values, the threshold level of 0.50 should be exceeded (Table 5).

After this, the discriminant validity was tested, which describes how a construct diverges from other constructs present in the model [90]). This is typically performed with Fornell and Larcker’s [101] criteria. Recent research suggests, however, using the Heterotrait–Monotrait (HTMT) of the correlations denoting the ratio between-trait and within-trait correlations [90]. Research has endorsed that the HTMT values should not exceed 0.90 [102], so the significance of the HTMT values is also checked with bootstrap confidence intervals. After this, the latent variable scores were transferred to the dataset in preparation for the NCA (

Table 6. Discriminant validity assessment.

Relationship	HTMT	Bias-Corrected Confidence Intervals
		2.5%	97.5%
Industry 4.0 ↔ Distributor sustainability development	0.775	0.615	0.895
Marketing channel operational performance ↔ Distributor sustainability development	0.613	0.422	0.750
Marketing channel operational performance ↔ Industry 4.0	0.665	0.496	0.809
Trust ↔ Distributor sustainability development	0.859	0.718	0.969
Trust ↔ Industry 4.0	0.675	0.496	0.825
Trust ↔ Marketing channel operational performance	0.762	0.538	0.947

).

4.4. Appraisal of the Structural Model

The quality of the structural model assessment starts with the collinearity evaluation, which displays a correlation between the exogenous predictors. Collinearity is usually assessed using variance inflation factors (VIF). As all VIF values were below 3, there was no indication of collinearity in the structural model [103].

Next, predictive validity and relevance analysis were completed, commonly undertaken with the R² and Stone–Geisser Q2 values [104,105]. Previous research has documented that R2 values of 0.67, 0.33, and 0.19 determine substantial, average, and weak predictive validity, respectively [106]. The R² and the adjusted R2 values of the marketing channel operational performance construct’s endogenous construct were 0.470 and 0.457, respectively. Current research has also recognized strength criteria for the Stone–Geisser Q² values, indicating that values larger than 0.25 and 0.50 portray medium and large predictive relevance [107]. The PLSpredict investigation showed a Q² value of 0.978, signifying large predictive relevance.

To conclude the structural model evaluation, the assessment of the path coefficients was completed. However, it is important to stress that recording the statistical significance is insufficient [108]; therefore, the effect size should also be reported [109,110], as the effect size may be the most critical finding in quantitative research. Moreover, with an adequately large sample size, statistical analysis can recognize significant differences that are essentially worthless in practice. In contrast, the effect sizes do not depend on the sample size and are equivalent across different research projects [98]. The values of 0.02, 0.15, and 0.35 indicate the exogenous construct to have small, medium, or large effect sizes, respectively [90] (

Table 7. The significance of the path coefficients and effect sizes.

H	Relationship	Path Coeff	p-Value	Hypothesis Acceptance	Effect Size (f2)	Total Effect	Effect Size Descriptor of the Total Effect
1	Inter-organizational trust → Industry 4.0	0.494	0.000	Yes	0.388	0.494	Large
2	Industry 4.0 → Distributor sustainability development	0.527	0.000	Yes	0.350	0.527	Large
3	Inter-organizational trust → Distributor sustainability development	0.509	0.000	Yes	0.374	0.770	Large
4	Industry 4.0 → Marketing channel operational performance	0.351	0.020	Yes	0.116	0.387	Large
5	Distributor sustainability development → Marketing channel operational performance	0.068	0.541	No	0.005	0.068	-
6	Inter-organizational trust → Marketing channel operational performance	0.358	0.000	Yes	0.136	0584	Large

). The results imply that all relationships are significant.

4.5. Necessary Condition Analysis (NCA)

Further, to determine the relationships between the exogenous and endogenous constructs, the NCA in Smart-PLS provides a more comprehensive description of the exogenous constructs’ impact on the central endogenous construct. This can be envisioned with scatter plots between the exogenous and central endogenous constructs, which plausibly disclose necessary conditions [94]. Specifically, precise levels of Industry 4.0 technologies, distributor sustainability development, and inter-organizational trust may be needed to achieve an exact marketing channel operational performance. Thus, the NCA enables researchers to discern which endogenous constructs are necessary and the degree to which each exogenous construct is needed to accomplish a precise level of the endogenous target construct, i.e., marketing channel operational performance [96].

The NCA scatter plot diagrams typically contain two upward-sloping lines, referred to as the ceiling envelopment-free disposal hull (CE-FDH) and the ceiling regression-free disposal hull (CR-FDH) (Appendix B). The difference between these two is that the CR-FDH is a direct line through the CE-FDH stepwise line, demonstrating that the larger the area in the upper left corner is, the greater the constraint of the endogenous constructs on the marketing channel operational performance construct [94]. This can also be confirmed with the bottleneck table.

Table 8. Bottleneck table-CE-FDH (values) (Note: NN = not necessary).

	Marketing Channel Operational Performance	Distributor Sustainability Development	Industry 4.0	Inter-Organizational Trust
20%	2.33	NN	NN	NN
30%	2.67	NN	NN	1.57
40%	3.00	1.67	NN	2.43
50%	3.33	1.67	NN	2.43
60%	3.67	1.67	NN	3.00
70%	4.00	1.67	NN	3.00
80%	4.33	1.67	2.82	3.00
90%	4.67	2.94	3.00	3.00
100%	5.00	3.00	3.00	3.57

illustrates three necessary conditions to attain an 80% marketing channel operational performance (4.33): distributors’ sustainability development at no less than 1.67, Industry 4.0 at no less than 2.82, and inter-organizational trust at no less than 3.00. In the sample population of this research, 63 (48.1%) observations exceeded all threshold levels, signifying a necessity for substantial enhancement to achieve top-level (80%) performance in marketing channel operational performance. Appendix B illustrates the scatter plots for the relations in the structural model.

The primary measure of the NCA’s effectiveness is the ceiling accuracy and necessity effect size d. Ceiling accuracy is calculated by dividing the number of observations at or below the ceiling line by dividing it by the total observations and multiplying by 100. Evaluating this accuracy against a benchmark value (such as 95%) helps assess solution quality [93,94]. The necessity effect size d and its statistical significance reveal whether a construct is essential. It is determined by dividing the “empty” space (known as the ceiling zone) by the total number of observations [94].

The effect sizes and their significance are illustrated in

Table 9. Necessary Condition Analysis (NCA) effect sizes and their permutation significance.

Construct	CR-FDH Effect Size (d)	Permutation p-Value	Effect Size Descriptor on the Social Performance
Distributor sustainability development	0.168	0.001	Medium
Industry 4.0	0.117	0.279	-
Inter-organizational trust	0.286	0.000	Medium

. Research has classified 0 < d < 0.1 as a small effect, 0.1 ≤ d < 0.3 as a medium effect, 0.3 ≤ d < 0.5 as a large effect, and d ≥ 0.5 as a very large effect [95].

4.6. Interpretation of the Results

The findings indicate that settings 1, 2, and 3 are supported (see Appendix A). According to the data in Table 9, the NCA is summarized in

Table 10. The scenarios in the interpretation of the NCA results.

Setting	PLS–SEM Results	NCA Results	Conclusion	Condition
1. Distributor sustainability development construct is a …	insignificant determinant	and a necessary condition	A certain level of the distributor sustainability development construct is necessary for the marketing channel operational performance to manifest. However, a further increase is not recommended, as it will not increase the marketing channel operational performance any further.	Must have!
2. Industry 4.0 is a …	significant determinant	But not a necessary condition	On average, an increase in the Industry 4.0 construct will increase the marketing channel operational performance; no minimum level of the construct is needed to ensure that the marketing channel operational performance will manifest.	Should have!
3. Inter-organizational trust construct is a …	significant determinant	and a necessary condition	On average, an increase in the inter-organizational trust construct will increase the marketing channel operational performance. However, a certain level of the exogenous construct is necessary for the marketing channel’s operational performance to manifest.	Must have!

. Research indicates that a medium effect must be considered a necessary condition [111]. Accordingly, inter-organizational trust and the sustainable development of distributors are necessary “must-have” conditions for the operational performance of the marketing channel. However, it is crucial to note that elevating distributor sustainability development may not necessarily improve the operational performance of the marketing channel. This finding is significant as additional investments in this and other performance drivers may fall short unless all essential conditions are fulfilled. After existing bottlenecks are addressed, other investments can only improve operational performance in the marketing channel.

5. Discussion

The study’s findings demonstrate a strong positive relationship between inter-organizational trust and adopting Industry 4.0 technologies, supporting H1. This relationship extends beyond the traditional understanding of trust in technological innovation [37]. Our analysis reveals that trust is both a significant determinant (0.494, p < 0.000) and a necessary “must-have” condition for successful technology implementation, with a large effect size (Table 7). This dual role suggests that trust facilitates technology adoption and is a “must-have” prerequisite for digital transformation initiatives, particularly in complex Industry 4.0 implementations that require substantial coordination and resource sharing between channel partners. This discovery supports existing research as it has been shown that digital trust, comprising trust and Industry 4.0 technologies, has a significant positive impact on a firm’s open innovation, further moderated strongly by its technological orientation [112]. Furthermore, the findings in extant research verify that a high degree of coordination and trust between partners is essential for the close integration of value-creation activities in Industry 4.0 [39,61,113]. On the other hand, the coordination efforts assume the sharing of data and information, for which trust is necessary (Müller et al., 2020 [84]).

In continuation of the above, H3 was also accepted (0.509, p < 0.000) with a large effect size (Table 7). Previous research has stated that when partners have relational governance based on competence trust, they are more prone to learn [114] and more committed to the partnership [115]. Competence trust has been defined as “the confidence in the abilities of the other party to perform its share of the workload in an exchange” [116]. Sustainable development may require the exchange of tacit knowledge in the distributor network, in which inter-organizational trust plays a key role [116].

The support for H2, demonstrated by the significant path coefficient (0.527, p < 0.000) and large effect size (Table 7) between Industry 4.0 adoption and distributor sustainability development, validates the theoretical link between digital technologies and sustainability outcomes. This finding aligns with recent research [1,65] while providing new empirical evidence for how technological integration creates tangible sustainability improvements. Importantly, our results suggest that this relationship may be more nuanced than previously theorized, particularly in global distribution channels where technological implementation must account for varied organizational capabilities and market conditions. It is also notable that the specific indirect effect (Interorganizational trust → Industry 4.0 → Distributor sustainability development) was significant (0.260, p < 0.000), indicating that about half (0.260/0.527, 49.3%) of the effect from Industry 4.0 on marketing channel operational performance stems from the inter-organizational trust construct, highlighting that it is the critical role.

Confirming H4 through a significant path coefficient (0.351, p < 0.020) with a large effect size (Table 7) provides robust empirical support for the theoretical connection between Industry 4.0 technologies and marketing channel operational performance. Our NCA results further clarify this relationship by identifying Industry 4.0 technologies as a “should have” rather than a “must-have” condition. This distinction suggests that while these technologies significantly enhance channel performance, they serve more as performance enablers than fundamental prerequisites, aligning with findings from recent studies [64]. It is also noteworthy that the specific indirect effect (Interorganizational trust → Industry 4.0 → Marketing channel operational performance) was significant (0.173, p < 0.022), indicating that about a quarter (0.173/0.770, 22.5%) of the effect from Industry 4.0 on distributor sustainability development stems from the inter-organizational trust construct, reiterating that it is the vital role.

A key finding is the lack of support for H5, challenging conventional assumptions about the direct relationship between sustainability initiatives and marketing channel operational performance. This unexpected result suggests that the link between sustainability practices and operational performance outcomes may be more complex than previously understood. While this finding contradicts earlier research [65,68], the results align with our NCA results, as distributor sustainability development is a “must-have” necessary condition for marketing channel operational performance. This indicates that sustainability initiatives may influence performance through indirect mechanisms or longer-term effects not fully captured in our cross-sectional analysis.

A significant contribution of this study is validating H6 and our NCA results. The identification of trust as both a significant determinant (path coefficient = 0.358, p < 0.000) with a large effect size (Table 7) and a “must-have” necessary condition for marketing channel operational performance extends beyond traditional views of trust as merely beneficial. This finding finds trust as an essential foundation for channel performance, suggesting that a minimum threshold of inter-organizational trust is required to achieve high marketing channel operational performance levels.

To conclude, this study provides a novel contribution by empirically demonstrating the dual role of inter-organizational trust as both a critical enabler and a “must-have” prerequisite for Industry 4.0 adoption and its downstream impacts on distributor sustainability and marketing channel performance. While previous research has acknowledged trust as beneficial for collaboration, the findings extend this understanding by quantifying its effect size and necessary role in successful digital transformation within complex distribution networks. Furthermore, this study highlights the nuanced interplay between Industry 4.0 technologies and sustainability, showing that technological adoption alone does not guarantee improved operational performance but functions as a performance enabler contingent on existing trust structures. By challenging conventional assumptions about the direct impact of sustainability initiatives on marketing performance, the results call for a reassessment of how firms integrate digital trust into their strategic decision-making for long-term competitive advantage. These insights provide fresh empirical evidence and a refined theoretical perspective on how trust-driven digital ecosystems shape the evolving landscape of Industry 4.0 implementations.

6. Implications

The study’s findings illuminate critical implications for global marketing channels, with inter-organizational trust emerging as a determinant and a “must-have” necessary condition for marketing channel operational performance. This dual role of inter-organizational trust extends beyond traditional perspectives [29,75] and challenges the technology-centric view of Industry 4.0 transformation. While Industry 4.0 technologies are a significant determinant of marketing channel operational performance [64,117], their effectiveness appears contingent on established inter-organizational trust between channel partners [72].

A notable finding concerns distributor sustainability development, which showed no direct impact on operational performance despite positive influences from inter-organizational trust and Industry 4.0 technologies [1,39]. This contradicts earlier findings by Carter and Rogers and suggests that sustainability initiatives may generate value through indirect pathways [65], such as enhanced stakeholder relationships [70], rather than immediate operational gains. The finding could also be increasingly dependent upon the industry, because distributor sustainability development influences operational performance may not be as prominent in this setting but could be in other settings. Notably, distributor sustainability development was a necessary “must-have” condition for marketing channel operational performance.

For practitioners, these findings indicate that trust-building should precede or occur in parallel with technological investments, creating what Dyer and Chu [33] term “relationship-specific assets”. Organizations must develop a “trust–tech balance”, integrating relationship building with digital transformation. This aligns with recent research by Kumar et al. [60] and Lumineau et al. [38] on the interplay between trust and technology adoption. Companies should view sustainability initiatives as strategic long-term investments rather than quick performance enhancers [71], integrating them into broader digital transformation efforts supported by robust inter-organizational relationships [73].

This research contributes to the theory by repositioning trust from a mere facilitator to a necessary condition for high performance in the digital age. It suggests a fundamental revision of existing marketing channel performance models [74,75]. The findings align with and extend recent work on trust in digital transformations [38,39], offering a more nuanced understanding of how trust enables technological adoption and sustainable development in marketing channels.

7. Limitations and Future Research

The study faces three crucial limitations: its cross-sectional design prevents analysis of causal relationships over time, particularly regarding sustainability and technology adoption impacts; the North American-focused sample limits global generalizability; and the aggregated measurement of Industry 4.0 technologies masks individual technological effects on performance outcomes.

Future research should address these limitations through (1) longitudinal studies examining temporal dynamics of trust-building and technology adoption, especially given our finding that trust (d = 0.286) shows stronger necessary effects than technology (d = 0.117); (2) cross-cultural studies validating whether trust’s “must-have” status and technology’s “should have” role persist across different contexts; and (3) disaggregated analysis of specific Industry 4.0 technologies’ impacts, given their significant influence on sustainability development (path coefficient = 0.527) and dependence on trust foundations (path coefficient = 0.494). Such research would enhance the understanding of how different technologies contribute to channel performance and sustainability outcomes in varied cultural and temporal contexts. Finally, in a related context, previous research [112] has incorporated absorptive capacity (both realized and potential) into the research framework as an outcome of trust. So, introducing absorptive capacity as an antecedent of Industry 4.0 technologies and the result of inter-organizational trust might be an exciting research venue.

8. Conclusions

The research examined the interface between Industry 4.0 technologies and global sustainable marketing channel development, focusing on the role of inter-organizational trust in facilitating advanced technology integration and its impact on operational performance. The sample comprised 131 respondents from Canadian and U.S. global firms with over 400 employees. The statistical methods included PLS-SEM and NCA. The results disclosed that inter-organizational trust is both a significant determinant and a necessary condition for marketing channel operational performance. While Industry 4.0 technologies surfaced as a significant element, they were not recognized as a necessary condition. Further, distributor sustainability development demonstrated neither a significant determinant nor a condition needed for marketing channel operational performance.

This study contributes to appreciating Industry 4.0 and marketing channel dynamics by integrating inter-organizational trust analysis with NCA methodology. By recognizing trust as both a significant determinant and a necessary condition, the research provides practical guidance for managers navigating technological adoption in global marketing channels. The findings challenge conventional assumptions about sustainability development while emphasizing trust’s crucial role in the digital age, offering valuable insights for achieving high marketing channel operational performance during digital transformation.