1 Introduction

The “omnichannel” buyer is an evolution of the multichannel consumer to the extent that he uses all the channels available to him indistinctly in his relationship with a brand (Lazaris and Vrechopoulos 2013). Consequently, organisations have evolved their management strategies from offering new customer service channels (multi-channel company), to be able to continue a process that the customer has started in another previous channel (Brynjolfsson et al. 2013; Malik 2023). This new form of management is possible thanks to the appearance of new digital tools that complement traditional physical channels (Leeflang et al. 2014; Dogra and Kaushal 2023). The ultimate goal is to improve customer satisfaction and, therefore, achieve greater loyalty (Flavián et al. 2019; Ramos et al. 2023).

Establishing an adequate omnichannel strategy means overcoming certain organisational challenges that must be considered (Brewer and Holmes 2016; Simone and Sabbadin 2017). Adopting multi-channel strategies has already presented several challenges for organisations. In some cases, they are immoveable constraints to be mitigated and, in other cases, they are barriers that can be overcome. From a management perspective, it is highly beneficial to have prior knowledge of the factors that influence omnichannel management and their corresponding impact levels. This awareness allows for effective mitigation or alleviation of these factors. Such knowledge provides valuable management insights, enabling companies to formulate appropriate strategic action plans during transformation processes (Sultana et al. 2011; Ye et al. 2018).

In this paper, the performance of the industrial client is defined as having a direct impact on revenue growth for organisations that adopt an omnichannel B2B strategy (Alonso-Garcia et al. 2021b). Typically, improved performance is indicated by increased purchase frequency, which, in the omnichannel environment, is accomplished through enhancing the customer experience and fostering customer loyalty (Simone and Sabbadin 2017; Shen et al. 2018).

Five broad types of constraints and barriers are identified for multichannel implementation: data integration, understanding customer behaviour, channel evaluation, allocating people to channels, and cross-channel strategy coordination (Neslin et al. 2006). In fact, for these authors, the challenges were interrelated. It is not possible to know the behaviour of the client without an adequate integration of the data, for example, and, based on this necessary knowledge of the client, the strategy to be followed by the organisation is established. In the omnichannel strategy, these challenges persist and force corporations to change their competitive strategies (Brynjolfsson et al. 2013; Leeflang et al. 2014; Picot-Coupey et al. 2016). However, these management challenges, like most research on the omnichannel strategy, have focused mainly on the retail industry and several authors point to the limited research on the impact of the omnichannel in the industrial field (Strojny and Chromińska 2016; Russo and Confente 2017a).

There are papers that highlight some constraints for omnichannel implementation in the B2B field, in the scope of the study that these works carry out (Alonso-Garcia et al. 2021a). Similarly, there are papers that have focused on studying omnichannel barriers in general, especially in a retail environment, adding constraints to its implementation in three areas: Marketing, Logistics and Organisational Management, but always in a retail environment (Ye et al. 2018). Most of the previous contributions that have explored the implementation of an omnichannel strategy have predominantly concentrated on the retail environment (Cuesta-Valiño et al., 2023). In fact, recent studies highlight a significant scarcity of published research specifically addressing omnichannel management within the B2B area (Ballerini et al. 2024; Calderon-Monge and Ribeiro-Soriano 2024; Russo and Confente 2017a; Galipoglu et al. 2018; Alonso-Garcia et al. 2021b, 2023).

The main objective is to identify the barriers that hinder effective omnichannel management in the B2B field, negatively impacting the performance of industrial clients. Additionally, the goal is to gain a deeper understanding of the relationship between these limitations and their overall impact on omnichannel performance results. In other words, the main objective of the research question is:

What are the main barriers for an industrial client performance in an omnichannel strategy?

Subsequently, it is convenient to analyse the weight of these barriers when the industrial client is part of an omnichannel ecosystem, that is, when the industrial client is part of an intermediary channel necessary for the omnichannel management of the manufacturer or wholesaler.

2 Theory and hypotheses

In the scientific literature, different terms are often used to analyse the challenges that companies face when implementing a strategy or a new process: barriers, inhibitors, problems, or issues (Sultana et al. 2011; Chen et al. 2018; Ye et al. 2018; Wieczerniak and Milczarek 2019). Similarly, there are both drivers-enablers and barriers-constraints-inhibitors of omnichannel management. As has been collected in recent papers, there is still little published research on omnichannel management in the B2B field (Russo and Confente 2017a; Galipoglu et al. 2018; Alonso-Garcia et al. 2021b). Even the scarcity of studies on the barriers to implementation is a fact that has already been collected in the research on e-Commerce B2B itself. As a more common explanation, it can be said that the evaluation of B2B websites is difficult given the lack of standards for the representations and transactions of products and services (Lin et al. 2011). However, subsequent contributions have emerged that aim to address this problem. It is important to acknowledge and consider these subsequent contributions as well (Wang and Vaughan 2014; Wang and Xu 2017; Koronaki et al. 2023).

Therefore, the theoretical framework is based on two specific areas. On the one hand, the numerous papers related to the retail sector, which, although it has differences with the B2B field, at least allows us to identify variables of an omnichannel strategy (Cai and Lo 2020). On the other hand, the research on digital marketing strategies and B2B e-commerce, which, although they focus on a single digital channel, complements the research on omnichannel retail with variables typical of the B2B field (Pandey et al. 2020; Song et al. 2022).

2.1 Retail omnichannel

The literature on omnichannel is extensive in the field of retail. This is how the different bibliographic reviews are published (Mirsch et al. 2016; Galipoglu et al. 2018; Melacini et al., 2018; Taylor et al., 2019Cai and Lo 2020; Alonso-Garcia et al. 2021a). Research addresses the barriers to omnichannel implementation in different fields and industries, as shown in Table 1. These barriers are subdivided into two, strategic and operational (Picot-Coupey et al. 2016; Ye et al. 2018). In addition, these limitations depend on the implementation phase in which the company is (Lewis et al. 2014). From a strategic point of view, the difficulty of being able to establish a value-added proposition stands out (Coelho and Easingwood 2008; Wang et al. 2013; Lewis et al. 2014; Melero et al. 2016; Mirsch et al. 2016; Picot-Coupey et al. 2016; Ye et al. 2018), which, in many cases, has to do with the context of the corporation itself, either due to the size of the company and/or its extensive catalogue extension (Coelho and Easingwood 2008), or due to the low organisational agility and capacity for innovation that this type of strategy requires (Coelho and Easingwood 2008; Wang et al. 2013; Melero et al. 2016; Mirsch et al. 2016; Picot-Coupey et al. 2016; Endres et al. 2022).

From an operational point of view, the barriers are also diverse, but they can be grouped into five areas. On the one hand, management, either due to a lack of leadership in the transformation process, or due to the brakes that the different stakeholders of the company can cause (Lewis et al. 2014; Ye et al. 2018). Secondly, the problems related to the supply chain are grouped, both supply and inventory (Bell et al. 2014; Lewis et al. 2014; Hübner et al. 2016a, b; Picot-Coupey et al. 2016; Yu et al. 2016; Ye et al. 2018), as well as the difficulty of orchestrating the online-offline assortment, that is, a centralised product management (Bell et al. 2014; Chopra 2016; Picot-Coupey et al. 2016).

Thirdly, within the operational sphere, there are some barriers that are intrinsic to all change management, such as internal rejection, especially because of what the new channels represent as competition for those already established (Lewis et al. 2014; Picot-Coupey et al. 2016); and on the other hand the necessary skills and training in the new tools and processes (Coelho and Easingwood 2008; Zhang et al. 2009; Lewis et al. 2014; Picot-Coupey et al. 2016).

Fourthly, the necessary technological barriers are collected, especially due to the difficulties involved in integrating all the customer service delivery systems required by the omnichannel strategy (Lewis et al. 2014; Chopra 2016; Melero et al. 2016; Mirsch et al. 2016; Picot-Coupey et al. 2016; Yu et al. 2016); and consequent to these constraints in terms of integration, the difficulty of having aggregate customer information in all channels (Zhang et al. 2009; Wang et al. 2013; Melero et al. 2016; Mirsch et al. 2016; Picot-Coupey et al. 2016).

Finally, it is worth considering, as in Table 1, the barrier the financial investment that the entire new strategy entails, both in new tools and in internal training and change management processes (Zhang et al. 2009; Chopra 2016; Hübner et al. 2016b; Lewis et al. 2014; Mirsch et al. 2016; Picot-Coupey et al.,2016).

Table 1 Main barriers/constraints for the implementation of omnichannel retail
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2.2 B2B e-commerce

The literature in the field of B2B e-commerce has been extensive since the 90s (Ngai and Wat 2002). In recent years, academic research has focused on specific areas of expertise, as a recent literature review shows. Under the heading B2B, it is studied from the content (Yaghtin et al. 2021), supply chain (Jamaluddin and Saibani 2021), loyalty (Kittur et al. 2021), social media (Dwivedi et al. 2021), digital marketing (Pandey et al. 2020; Song et al. 2022), digital platforms (Haster et al. 2020) and e-commerce implementation (Paris et al. 2016), among others.

As a result of this extensive research, constraints have been identified for the implementation of B2B e-commerce, which are listed in Table 2. Analysing the main constraints there is some coincidence with the omnichannel barriers in retail of the previous chapter. Thus, from the point of view of long-term objectives, the strategy to follow also appears as obstacles in the implementation of B2B e-commerce(Huang et al. 2007; Jehangir et al. 2011; Sultana et al. 2011) and the size of the company (Hong and Zhu 2006; Teo et al. 2009). However, in the field of B2B e-commerce, the clients themselves are also identified as one of the constraints for implementation, especially due to the lack of knowledge or preparation of these same clients (Huang et al. 2007; Wengler et al. 2021; Kaur et al. 2023). From an operational point of view, once again with omnichannel retail, the limitations posed by the lack of experience in management, which in some cases leads to the outsourcing of these processes (Hong and Zhu 2006; Teo et al. 2009; Lin et al. 2011; Sultana et al. 2011; Müller 2019; Wengler et al. 2021). The implementation of new processes derived from e-Commerce is another operational barrier (Wengler et al. 2021).

The human factor is once again key in terms of barriers to omnichannel implementation, both due to internal rejection and lack of confidence in the new channel (Hong and Zhu 2006; Lin et al. 2011; Müller 2019), as well as the technical expertise and skills required (Hong and Zhu 2006; Huang et al. 2007; Teo et al. 2009; Jehangir et al. 2011; Lin et al. 2011; Krell et al. 2020; Stange 2022).

From a technical point of view, security stands out as the main barrier, especially when it comes to data sharing. (Lin et al. 2011; Sultana et al. 2011; Müller 2019; Alexandrovskiy and Trundova, 2022; Stange 2022; Dogra and Kaushal 2023).

Finally, as for the omnichannel implementation in retail, the necessary financial investment represents a brake on the implementation of B2B e-commerce (Lin et al. 2011; Sultana et al. 2011; Wengler et al. 2021).

Table 2 Main barriers/constraints to the implementation of e-Commerce B2B
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To define the primary groups of barriers (constructs) that impact omnichannel performance, the paper follows the common division of limitations found in the omnichannel literature: strategic and operational (Picot-Coupey et al. 2016; Ye et al. 2018). Within the operational realm, two major groups can be distinguished based on the five main types of operational barriers (Neslin et al. 2006):

  1. 1.

    Channel evaluation, assignment of people to channels, and coordination of strategies across channels, collectively grouped to as “value proposition and operational constraints”.

  2. 2.

    Understanding customer behaviour and data integration, as “effort and technological constraints”

To determine the indicators for these constructs, a Delphi process was conducted with a panel of experts, as described in detail in the materials and methods section. The Delphi process was carried out in four iterations with the panellists, as the emphasis of the indicators is on comprehensiveness, rather than coherence, especially for formative variables (Diamantopoulos and Siguaw 2006). Thus, based on the division of constructs and indicators from the expert panel, the barriers listed in Tables 1 and 2 can be mapped and indicators can be identified as follows in Table 3.

Table 3 Constructs, indicators, and mapping with barriers
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2.3 Value proposition and operational constraints

The omnichannel strategy focuses on creating value for the customer and, therefore, aims to maximise the value that said customer perceives from the company (Hure et al. 2016; Acquila-Natale and Iglesias-Pradas 2021). However, one of the main limitations of omnichannel implementation will be the employees themselves. According to their motivation and training, they are the ones who will make the new processes successful to the extent that greater value is delivered to the client and to the channel to which these employees are assigned (Yrjölä 2014; Beck and Rygl 2015). There are several research in which the conflicts between channels and a disparate approach to the client are collected, especially in the first stages of the omnichannel transformation (Lewis et al. 2014; Herhausen et al. 2015b). These conflicts range from a lack of process integration and scattered information between departments to conflicting motivations between different teams (Lewis et al. 2014; Hübner et al. 2016b, c).

Therefore, as the first element within the model and precursor to any other constraint, the construct of value proposition and operational constraints is established. This construct encompasses various variables that contribute to its composition. Firstly, it involves reaching an agreement on the value approach and the strategy to be offered to clients, which serves as a guiding principle for the entire company’s operations. On the other hand, omnichannel management in a B2B sphere is supported by two main levers, both the channels through which the services are provided and the human team (Alonso-Garcia et al. 2021b). Thus, other obstacles for the omnichannel operation are given by the immaturity of the channel, as well as by the large number of actors involved to complete these operations in practice (Bouncken and Kraus 2022). It is important to highlight that this construct is co-substantial to the B2B sphere. In other words, the retail channel does not require a distribution channel in its relationship with the final client since it interacts with the end consumer directly. Lastly, are the digital capabilities and skills of the team that must carry out the value creation. This last constraint can be overcome through the process of hiring personnel with skills to carry out the new processes. However, a related limitation is triggered here, such as the difficulty of finding resources with the appropriate skills for the firm strategy.

As for achieving internal agreement on value [Value], one of the main challenges in the B2B omnichannel strategy is to offer greater value from the combination of online and offline channels (Russo and Confente 2017b). Thus, it has been emphasised that, under an omnichannel strategy, the consideration of the final value for the customer must be redefined and aligned among the different stakeholders of the company as one of the main challenges to overcome (Vrontis et al. 2017). The involvement of multiple stakeholders in omnichannel management carries a potential risk. When the responsibility for transmitting the value from marketing strategies is diffused among many stakeholders without a clear sense of individual ownership, it can result in less coordinated actions and adversely impact overall performance (Leeflang et al. 2014). This lack of internal agreement on what is the value for the customer in an omnichannel strategy has also been reported in the literature as a loss of competitive advantage (Ye et al. 2018). The scientific literature shows cases of companies that fail to align a single objective of customer value as one of the barriers to omnichannel implementation (Ye et al. 2018) and consequently align staff incentives (Gallino and Moreno 2014).

Regarding channel maturity [Channel], one of the characteristics of B2B omnichannel management is being able to expand the offer of solutions through the distribution channel (Russo and Confente 2017b). In previous research, case studies have been shown in which one of the failures has been the immaturity of the channel in an omnichannel strategy, such as franchise networks (Ye et al. 2018). In previous multichannel papers, the immaturity of the channel favoured disintermediation by the manufacturer in a B2B scenario (Chung et al. 2012). This situation increases, if possible, in an omnichannel scenario, although it depends considerably on the preference that the final consumer has for the digital channel over the physical channel (Kim and Chun 2018).

Regarding the difficulty in finding resources with the right skills for the business (Human Resources) [HR], this is a constraint already noted in previous research. Both the need to train staff in new technologies and processes (e.g., a store employee to attend to offline orders), such as the incorporation of new math profiles to study customer behaviour patterns (Gao and Su 2017). It has already been described as one of the challenges for companies to face the new jobs created in the digital age. But equally, given that the omnichannel concept is general in the company, there are several authors who point out that the HR team must incorporate new skills in hiring regardless of the role to be played, such as analytical and technical capacity (Leeflang et al. 2014). Technical profiles are necessary to undertake an adequate reengineering of business processes in pursuit of a successful omnichannel approach (Simone and Sabbadin 2017).

The model incorporates these indicators as formative variables, assuming that the construct is manifested through these indicators, meaning they shape, cause, or precede the construct. The model does not assume any correlation between the indicators. For instance, the agreement on the omnichannel value proposition is unrelated to the challenges associated with recruiting suitable staff.

The process of transforming a business towards an omnichannel approach involves a gradual evolution that takes place after defining the strategy to be pursued. It is crucial to proactively address operational constraints, which will be further elaborated on later (Picot-Coupey et al. 2016). Furthermore, the level of channel maturity and the involvement of various internal stakeholders play a significant role in determining the level of effort needed to achieve successful implementation of omnichannel management (Alonso-Garcia et al. 2021b). Thus, this research hypothesises that:

Hypothesis 1

(H1). Value proposition and operational constraints have a positive effect on effort and technological constraints in an omnichannel B2B company.

2.4 Effort and technological constraints

The strategy followed to address an omnichannel model establishes the depth that this change implies for the organisation (de Faultrier et al. 2014; Hansen 2015; Chou et al. 2016). The internal alignment in value and subsequent operations set the stage for the final transformation of a corporation. In this context, information technology has been seen by several authors as an accelerator of change, but in other cases as a brake (Hansen 2015). In omnichannel retail, technology is seen as the main driver, both to obtain and have customer information at all points of contact, and for the necessary integration of channels (Neslin et al. 2006; Simone and Sabbadin 2017).

Regarding integration of new technology with existing solutions in the company (backoffice), [Integration], the integration between corporate systems and channels has become one of the main challenges of the omnichannel literature and is a barrier to overcome, especially regarding information systems and logistics structures (Simone and Sabbadin 2017; Wollenburg et al. 2018a, b). Companies must integrate physical and digital channels, using information related to inventory, marketing campaigns and pricing strategies, among others (Zhang et al. 2009; Gallino and Moreno 2014). One of the areas most affected by the lack of integration is logistics. For a successful omnichannel implementation, proper inventory management is crucial to optimise processes and costs, e.g., last mile order fulfilment (Hübner et al. 2016b).

As per funds/Finance [Funds], omnichannel integration requires large and expensive investments in technologies (Herhausen et al.,. 2015), and in the so-called new service efforts (Li et al. 2022). The lack of funds is one of the challenges already mentioned that underlies some of the basic objectives of the omnichannel strategy, such as the limitation to build an adequate knowledge of the client, expanding on the idea that the challenges are intertwined with each other (Leeflang et al. 2014). This lack of funds is one of the main blocks in an omnichannel strategy and loss of competitive advantage (Ye et al. 2018).

As for customer or supplier knowledge (data, single view) [Info], knowledge of the customer is vital for a customer-centric strategy such as the one proposed by an omnichannel (Gupta and Ramachandran 2021). However, adequate customer information and integrated data pose a challenge already identified in multichannel strategies (Stone et al. 2002; Neslin et al. 2006; Pauwels et al. 2011). In the purely digital marketing field, knowing the customer and having adequate insights has been one of the main challenges that companies have faced (Leeflang et al. 2014; Trischler and Li-Ying, 2022).

The main issue arises from the fact that new technologies allow to collect a large amount of data through the online channel, and this is a real challenge for corporations (Russo and Confente 2017b; Cui et al. 2019).

Similar to the value proposition and operational restrictions construct, the model assumes formative indicators for this particular construct as well. In other words, the indicators shape the construct and are not correlated with each other. For example, the level of integration with legacy systems is unrelated to the team’s proficiency in working with new solutions. Likewise, the level of investment does not necessarily correspond to the extent of knowledge about the client.

The literature shows cases of companies that have the development of a unified information system as one of the barriers to omnichannel implementation, especially due to the cost and investment that such implementation requires (Ye et al. 2018) The efforts that a company must make both in investment and in the adoption of new tools are in those that most directly slow down the deeper technological deployment the omnichannel strategy may need. Implementing an omnichannel strategy faces technology-related obstacles (Lewis et al. 2014). Thus, this research hypothesises that:

Hypothesis 2

(H2). Effort and technological constraints have a direct positive effect on strategy implementation constraints.

2.5 Strategy implementation constraints

Omnichannel seeks a completely customer-centric business objective, and the corporate strategy must lay the foundations and organisational transformation in pursuit of that target (Ye et al. 2018). According to published research, the main challenges for an organisation when adopting omnichannel are those related to the company’s strategy, both from a cultural, management and resource point of view (Picot-Coupey et al. 2016). According to the experts interviewed, there are two main indicators that make up this construct. Thus, limitations for the implementation of the corporate strategy are customer approach, inherent in the omnichannel strategy and its consequent transformation of traditional sales processes.

Regarding customer approach, the omnichannel strategy is based on a necessary customer centricity and this centrality is what directs all the company’s efforts (Simone and Sabbadin 2017; Gupta and Ramachandran 2021). The customer approach must go through a previous stage, which is to be clear about the customer’s behaviour (Neslin et al. 2006). An adequate customer approach lays the foundations of any omnichannel strategy and is, therefore, one of the main challenges to be overcome by a corporation (Alonso-Garcia et al. 2021b).

As for the transformation of the process and traditional way of selling [NewSelling], since the implementation of multichannel strategies, companies must reformulate their channels, especially the sales force in a B2B corporation. Thus, the sales force must focus more on advising and guiding customers rather than on a mere entry of orders (Lapoule and Colla 2016). In addition, companies must rethink how to add more value to each customer with respect to the new customer journeys that the incorporation of new channels requires (Carvalho and Campomar 2014). Both the sales force itself in the B2B channel, as well as the staff of the store or even a warehouse, must understand that digital channels also function as a source of information and can also be encouraged to refer customers to these digital channels since they are already part of an overall process, such as ordering online and picking it up in store (Wollenburg et al. 2018b).

Once again, the construct is modelled using formative indicators. It is important to note that a customer-focused strategy does not necessarily entail a complete transformation of the traditional sales process. The extent of this transformation may vary depending on the nature of the relationship that exists within the corporation.

The omnichannel strategy has been seen as an opportunity for companies to achieve greater customer loyalty and experience (Alonso-Garcia et al. 2021b). Hence, any brake on the implementation of that strategy has an impact on customer performance. Consequently, this research hypothesises that:

Hypothesis 3

(H3). Strategy implementation constraint has a direct and positive impact on customer performance.

2.6 Customer performance

The performance of the industrial client can be observed from loyalty and experience (Simone and Sabbadin 2017; Shen et al. 2018). Both indicators are collected as variables to measure the performance of the industrial client according to the Delphi panel of experts described in the methodology section. Both variables influence each other since loyalty will be greater the better the customer experience. Both indicators are decisive in the recurrence of purchase and, therefore, in the income of the organisation that pursues an omnichannel B2B strategy (Alonso-Garcia et al. 2021b).

As per customer loyalty [loyalty], omnichannel customers are more loyal than single channel customers and higher loyalty leads to more repeat purchases and, therefore, higher revenue. This goal of customer loyalty is one of the reasons why companies carry out an omnichannel strategy (Wollenburg et al. 2018a).

Improving the customer experience is the main objective of the omnichannel strategy (Weber and Chatzopoulos 2019). The better the experience, the greater the omnichannel consumption by the customer (Shen et al. 2018). Therefore, it can be said that the omnichannel strategy seeks to increase the value perceived by the customer (Acquila-Natale and Iglesias-Pradas 2021), improving the experience and, as a result, improving the overall performance of the customer (Herhausen et al.,2015; Hübner et al. 2016b, c); Hoehle et al. 2018).

For the final model shown in Fig. 1, the customer performance construct has reflexive indicators. That is, the observable variables are measures of the construct. Therefore, the model assumes that both indicators covary and describe the customer performance.

Fig. 1
figure 1

Conceptual model and hypotheses

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3 Materials and methods

This research follows a survey method design and applies the Partial Least Squares Structural Equation Model (PLS-SEM). Since the omnichannel study in the B2B sphere is a field of study with little diffusion, this is considered exploratory research. It is precisely in exploratory research where PLS is a valuable tool. PLS estimates a more general model than covariance-based SEM and is less affected by incorrect model specification in some model subparts (Henseler et al. 2014).

To build the model, a review of the literature in the B2B omnichannel field has been carried out. In this way, the constructs, indicators, and hypotheses have been established. Table 4 shows a summary of the main variables of the model and the sources related.

Table 4 Items, and sources
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Regarding sample size, again PLS-SEM applies much more for small samples. Unlike covariance-based SEM, PLS can be used even if the number of observations is less than the number of variables (manifest or latent) or the number of parameters in the model (Henseler et al. 2014). Being able to work with small samples, however, we must establish the target size and for this the “10 times” rule is considered (Hair et al. 2016). According to this rule, the sample size must be equal to the greater of (a) 10 times the index with the largest number of formative indicators or (b) 10 times the largest number of structural paths directed to a given latent variable in the structural model. According to this rule and studying the model shown in Fig. 1, the sample size must be the greater number between both criteria: 40 subjects (according to a), or 30 (according to b).

To assess the limitations that an omnichannel strategy may have in all areas of the company, it is necessary to interview managers of companies that are applying this type of strategy in the B2B field. This survey is a challenge, due to the difficulty of accessing this type of director of multinational companies, and due to the degree of dedication they can provide. To build a survey that minimises the response time of a manager of a multinational company, a two-phase methodology has been followed. In a first phase, a Delphi was built with a small group of managers (30) who agreed on the main obstacles to omnichannel implementation. To achieve this goal, a panel of 30 experts, from 17 different countries, was created, who followed a four-step process to reach a consensus on the main limitations of the omnichannel implementation. Table 5 shows how the Delphi sample was composed.

Table 5 Delphi sample
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The main objective of the initial Delphi round was twofold. On the one hand, discern the limitations and obstacles that influence the effectiveness of customer performance for an omnichannel organization. On the other hand, obtain which variables are those that, according to experts, best measure the performance of the omnichannel customer in an industrial environment. After this round, the research team compiled and organized a comprehensive list, which was then distributed to the experts. During the following second round, consensus was achieved on key limitations and barriers, culminating in the identification of ten critical constraints, which serve as fundamental indicators and shape the constructs of the model. Likewise, there were only two variables that measured customer performance in an omnichannel industrial sphere.

Two additional rounds were subsequently carried out to facilitate the establishment of relationships and weightings by the experts. This involved an exploration of how these barriers impact Omnichannel Management performance. Finally, in the fourth round, the experts had the task of indicating the sign of each relationship (positive or negative), both between the identified limitations and their interconnections, and in relation to their impact on Omnichannel Management.

Based on the consensus reached in the Delphi, the survey was conducted to collect the importance (weight) that each manager gave to each indicator, following a Likert-type scale. The survey has exceeded the minimum objective of 40 subjects defined by the “10 times” rule and 102 responses have been collected. Once again, it is important to point out the difficulty of collecting responses from 102 C-level directors of important and well-known multinational companies with an omnichannel strategy, both manufacturers and wholesalers, mainly from the consumer goods and food sectors. The sample was acquired in the final months of 2022.

Table 6 Sample by region
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Table 7 Sample by country
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Although companies from all over the world have participated in the sample, the majority are North American and Western European companies, as shown in Table 6. The sample is represented by 35 countries, with the US being the most represented with 20% of the sample, as shown in Table 7. Half of the countries in the sample belong to Europe. Table 8 shows that most of the managers belong to the managerial area (general management and VP). More than half of the companies are manufacturers, as shown in Table 9.

Table 8 Sample by manager area
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Table 9 Sample by type of corporate
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Table 10 shows an overview of the various industries represented in the sample, without consideration for the specific position each company holds within the value chain of its respective industry.

Table 10 Sample by industry
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4 Results

4.1 Assessment of the measurement model: reliability and validity

The measurement model verifies that the theoretical concepts are correctly measured through the observed variables. This analysis is carried out regarding the attribute’s validity (it really measures what it is intended to measure) and reliability (it does so in a stable and consistent way).

The evaluation of the measurement model implies the analysis of the individual reliability of the item, the internal consistency or reliability of a scale, the convergent validity, and the discriminant validity (Zhou et al. 2023). Moreover, in the assessment of measurement models, it is imperative to discern between constructs measured reflectively and formatively. Consequently, distinct modes of evaluation are essential for each to ensure accuracy and validity in the assessment process (Hair et al. 2012).

Table 11 Formative measurement model. Constructs, items, weights, VIF and R2 values
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When assessing individual item reliability of the formative constructs, it is observed that not all the weights collected in Table 11 exceed the threshold of 0.707 (Cenfetelli and Bassellier, 2014). However, formative measurement exhibits a limitation concerning the number of indicators that can maintain a statistically significant weight. The maximum possible outer weight is determined by dividing 1 by the square root of the number of indicators (Cenfetelli and Bassellier 2009). In the case of the construct of value and operational constraints, which consists of four indicators, the reference threshold in the model would be 0.5. Moreover, it is crucial to consider the individual contribution of each formative indicator, which reflects the information provided by that specific indicator independently, without considering the influence of other indicators. This absolute contribution is quantified by the outer loading of the formative indicator, which is always accompanied by the indicator weights (Hair et al. 2016). When an indicator’s external weight is found to be statistically insignificant, but its outer load remains high (0.50 in the model), the interpretation should acknowledge its absolute importance while recognising its lack of relative significance. In other words, although the indicator may not contribute significantly to the overall construct, it still holds substantial relevance on its own. In the model, the criterion for fulfilling this condition is satisfied by all the indicators except for the integration. In the case of the integration indicator, its inclusion in the analysis is justified by considering its theoretical significance and potential overlap with other indicators within the same construct. The authors contend that the construct’s conceptualisation, supported by expert opinions and the results of the panel, strongly justifies retaining the integration indicator in the formative model of measurement.

In view of the loads, it is necessary to verify that there is not a high multicollinearity between them. The presence of high multicollinearity between the formative indicators of an emerging construct would produce unstable estimates and make it difficult to separate the different effects of the individual indicators on the construct. When verifying the variance inflation factor, it is observed that the VIF all the indicators in Table 11 are below the reference level of 3,3 (Diamantopoulos and Winklhofer 2001; Hair et al. 2011).

Table 12 Reflective measurement model. Constructs, items, factor loading, reliability and validity
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Regarding reflective items (see Table 12), factorial loadings, reliability, and discriminant validity assessment are conducted to determine the model fit. To assess the factor loading or reliability of the indicators, the relationship of each item to the latent constructs is scrutinized. The external loadings of reflective constructs surpass the threshold value of 0.707, from which indicator reliability can be ascertained (Hair et al. 2011). In this research, both items achieve this acceptable reliability level as their loadings exceed 0.7 and are higher in their own construct than in others. These results strongly support the reliability of reflective measures.

For evaluating convergent validity, the average variance extracted (AVE) is applied, and a value of at least 0.5 is recommended. This implies that a construct explains more than half of the variance in its indicators (Hair et al. 2016). This recommendation holds true for the Customer Performance construct in this research.

Assessing the reliability of a construct allows checking the internal consistency of all the indicators when measuring the concept. To carry out this evaluation, we found two indicators: the traditional Cronbach’s alpha coefficient and the composite reliability (ρc) of the construct. Composite reliability values of 0.60 to 0.70 are acceptable in exploratory research, while, in more advanced stages of research, values between 0.70 and 0.90 can be considered satisfactory (Hair et al. 2016). In the results obtained with the sample, Cronbach’s alpha is 0.800 and the composite reliability is 2.500 (rho_a) and 0.876 (rho_c).

The assessment of discriminant validity provides insight into the extent to which a construct genuinely differs from other constructs (Hair et al. 2016). The Fornell-Larcker criterion, a traditional metric employed for this purpose, indicates that each construct should exhibit stronger associations with its own measures than with measures of other constructs. Thus, the Fornell-Larcker criterion says that the square root of the AVE of each construct must be greater than its highest correlation with any other construct (Hair et al. 2016). The criterion is met since none of the correlations listed in Table 13 exceed the square root of the AVE value (0.888).

Table 13 Correlations
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4.2 Assessment of the structural model

Having verified that the measurement model is satisfactory in relation to the previous criteria (the measures of the construct are reliable and valid), in this section the structural model is evaluated. The structural model evaluates the weight and magnitude of the relationships between the different variables.

After running the PLS-SEM algorithm, estimates are obtained for the structural model relationships (i.e., the path coefficients), which represent the hypothesised relationships between the constructs. Whether a coefficient is significant ultimately depends on its bootstrap standard error. When an empirical value of t is greater than the critical value, we conclude that the coefficient is statistically significant with some probability of error (i.e., level of significance). The critical value commonly used for two-tailed tests is 2.57 (significance level = 1%) (Hair et al. 2016). The data provided by Table 14 show a value greater than 2.57 in all the trajectory coefficients.

On the other hand, if we evaluate p, and continuing with a restrictive level of significance (1%), the corresponding p value must be less than 0.01 to indicate that a relationship is significant, as can be seen in the values shown in Table 14 (Hair et al. 2016).

Table 14 Path coefficients – Mean, STDEV, T values, p values
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To carry out an adequate interpretation of the internal or structural model in the field of PLS modelling, the following questions must be answered, among others (Falk and Miller 1992):

  • How much of the variance of the endogenous variables is explained by the constructs that predict them?

  • To what extent do the predictor variables contribute to the explained variance of the endogenous variables?

To answer both questions we use two indexes: R2 and the standardised path coefficients β.

One measure of the predictive power of a model is the R2 value of the dependent latent variables. This measure tells us the amount of construct variance that the model explains. The explained variance of the endogenous variables (R2) must be greater than or equal to 0.1. Tables 11 and 12, and Fig. 2 show that all the values exceed the threshold, being the lowest of the restrictions to the implementation of the strategy.

Fig. 2
figure 2

PLSc results

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Alternatively, changes in indicator R2 can be explored to determine whether the influence of a particular latent variable on a dependent construct has a substantial impact: the importance of the effect (f2). The f2 levels of 0.02, 0.15, and 0.35 can be viewed as a test or an indication of whether a latent predictor variable has a small, medium, or large effect on the structural domain, respectively (Hair et al. 2011; Henseler et al. 2014). Table 15 shows that value and operational constraints have a large effect, while for the predictive effect of effort and technology constraints on strategy implementation constraints and strategy implementation constraints on customer performance, both effects are moderate.

Table 15 f-square matrix
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The second question can be answered with the help of the coefficient β. This represents the path coefficients or standardised regression weights. Standardised path coefficients should reach at least a value of 0.2, and ideally be above 0.3 (Hair et al. 2011). As observed in Table 15, all values surpass the threshold, indicating that the structural model is strengthened.

As a measure of model fit, the standardised root mean square residual (SRMR) is evaluated. The SRMR is defined as the root mean square discrepancy between the observed correlations and the correlations implied by the model (Henseler et al. 2014). Because the SRMR is an absolute measure of fit, a value of zero indicates perfect fit. A value less than 0.1 is generally considered a good fit, as are the values shown in Table 16.

Table 16 Model fit (with 5,000 bootstrap subsamples)
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As for the discrepancy between the observed correlations and those implicit in the model, it is calculated in two ways, both by the d_ULS (squared Euclidean distance) and by the d_G (geodesic distance) shown in Table 16 (Dijkstra and Henseler 2015). In the model, all sample values are less than the 99% upper limit point. Therefore, the model fits well since the discrepancy is very small.

5 Discussion

5.1 Theoretical implications

From a theoretical point of view, the main contribution comes from the very construction of the model in an area where, as has been seen, B2B omnichannel studies do not proliferate. In the model, all the constructs that offer limitations to omnichannel management, the relationship among them and their constitutive variables have been identified.

All constructs in the model, except customer performance, are formative; that is, the causal indicators form the construct through linear combinations. Thus, each construct indicator captures a specific aspect of the construct domain (Hair et al. 2016). For its part, the construct of customer performance is reflexive. Its two indicators, experience, and loyalty are interchangeable and are equivalent measures of the construct.

When assessing the individual item reliability, it is convenient to refer to authors who argue that the empirical rule (λ >= 0.707) should not be as rigid in the initial stages of scale development, as we found in this research (Barclay et al. 1995; Chin and Newsted 1998). Despite this, the possibility of item purification in the model could be considered. However, there are specific reasons and arguments that support why it has been decided not to pursue item purification.

When multiple formative indicators are used to measure a single construct, the probability of having one or more indicators with low or negligible external weights increases. Researchers should be cautious in removing formative indicators based on statistical results for at least two reasons. First, formative measurement is subject to a limitation regarding the number of indicators that can have a statistically significant weight. In cases where the indicators are uncorrelated (as explained above), the maximum possible external weight is 0.5 (Cenfetelli and Bassellier 2009). Negligible indicator weights should not automatically be interpreted as indicating poor quality of the measurement model. Here the absolute contribution of formative indicators has been considered, that is, the information provided by one indicator without considering any other indicator (Hair et al. 2013). The absolute contribution is given by the outer loading of the formative indicator, which is always provided together with the indicator weights. Second, since formative indicators define the empirical meaning of the construct, removal of the indicator should be considered with caution and should generally be the exception. Content validity considerations are imperative before removing formative indicators (Sarstedt et al. 2021).

As shown in Table 16, the model is fitted based on the standardized root mean square. It should be noted that this indicator has been taken as a measure of model fit given that it is the one recommended in this type of research. That is, when the objective of the research is to test a novel model to perform a structural analysis of it and for a sample size of less than 200 (Mai et al. 2021).

As shown in Fig. 1, when constraints are assessed, the main construct that affects customer performance is what is known as the strategy implementation constraints. In other words, digitisation affects business models (Martín-Peña et al., 2018; Trischler and Li-Ying 2023), with a moderate intensity, and the implementation of the omnichannel strategy is limited by the transformation of the traditional way of selling and the limitations that the organisation imposes on the necessary customer-focused value proposition. It is these limitations that directly affect customer performance, especially in terms of customer experience in a B2B sphere. The weight of both formative variables being significant, the incidence of the approach especially stands out. In turn, the implementation of the strategy is limited by technological constraints.

The technical constraints inherent to an omnichannel strategy are constituted by the lack of customer knowledge and the integration of data between the different actors and elements of the ecosystem. Both formative variables have a similar weight.

The effort that the organisation must make from an economic point of view, and especially in terms of adopting new technologies, determines the technological constraints. This effort is a limitation that is doubly influenced by the operational constraints of the new omnichannel model and by the value proposition that has been established at the origin. The two formative variables of this construct have a similar weight, although the costs of technology adoption are somewhat higher than the funds.

For its part, the limitations to the value proposition condition the omnichannel strategy of the entire model at source, by achieving an internal agreement on the value and the numerous stakeholders that must be aligned in that agreement. This restriction in the value proposition determines the limitations that the ecosystem itself may have for the operation of services, both due to the lack of resources trained in the new models, and due to the immaturity of the traditional channel that is part of the ecosystem. The limitations to the operation enclose the variables that most differentiate this B2B model from a retail model: the distribution channel and the specific skills required by the team involved in the operation. Both are part of the decisions and the provision of services in a B2B company. These variables are formative in the operational construct. Human resources have a greater weight than the distribution channel itself represents for the construct. The operational constraints condition the effort that the organisation must make.

5.2 Managerial implications

The main contribution for managers is the identification of the main constraints that prevent a good performance of an industrial client in the face of an omnichannel strategy. The model provides an initial insight into the constraints that affect optimal omnichannel performance. That is, both the intensity of these constraints and the relationship between them.

Omnichannel management seeks to maximise customer performance. As seen in the model, this can be well-measured by the improvement in customer experience, as well as the improvement in terms of loyalty.

The model establishes the definition of the omnichannel value proposition as the main stumbling block for a company when addressing an omnichannel strategy that improves the customer experience or loyalty. This value proposition is characterised by two main variables. On one hand, the difficulty of aligning what must be agreed as a value to be provided to the customer by the different teams involved in a company’s supply chain when an omnichannel strategy has been established. On the other hand, there is the difficulty of finding resources in the market that can be part of those same teams. This initial value proposal impacts directly and with similar intensity, both in the operational constraints and in those of the effort. In other words, the more difficult it is to align the teams internally and find capable profiles, the greater the limitation from the operational point of view and the greater the effort that the company will have to make to carry out the strategy.

In view of the model, although the immaturity of the distribution channel imposes limitations on omnichannel implementation, it has less of an impact than is assumed by the numerous actors involved in the provision of services or in decision-making. These operational constraints, typical of the B2B model, have an important effect similar to that of the value alignment construct itself (difficulties in finding a team and consensus on value) for the effort that the company must make.

The greater or lesser intensity of the effort that the company can make to transform its omnichannel management has a direct impact on the technological solutions that support the omnichannel strategy (Adner 2017). The lower the investment and the higher the cost of technology adoption (effort constraints), the worse technical solutions the company will have.

The less customer information is available, and the less systems are integrated with each other (technological constraints), the more difficult it will be to implement an omnichannel strategy.

In this way, it is observed that the main limitation is the way in which the omnichannel strategy is put into practice. This is more important than any other constraint, as it directly affects the experience or loyalty of a business customer. That is to say, the main stumbling block is given by the change that the new strategy supposes on the traditional sales processes and how the approach to the client is carried out. In other words, the companies that are most reluctant to have a variable value proposition, a portfolio that can be adapted to customer demand, are the ones that will have the worst omnichannel behaviour. This brake is the one that most directly impacts omnichannel performance.

5.3 Limitations and future research

The limitations of the paper offer new areas for future research. Several companies from different geographical areas participated in the panel. Although almost all of them operate in an international environment, the barriers that are also established can come from their geographical context, especially in terms of their channels. In fact, the scientific literature has pointed out that there is a strong relationship between the challenges for a company’s omnichannel implementation and the geographical context of the company itself (Hübner et al. 2016b).

On the other hand, the indicators collected from the expert panel warrant a more detailed analysis. For instance, the barrier posed by finding a team with the right skills is a limitation that may seem intuitive, but its impact can vary significantly based on factors such as company size, industry, product/service variety, among others.

Similarly, the concept of channel maturity requires further examination. What defines whether a distribution channel is truly ready for effective omnichannel management? Is it the level of digitisation, the capacity to integrate with manufacturer procedures, the management of end customer information, or a combination of these factors?

In summary, each indicator needs to be developed with a higher level of precision to ensure comparability across companies operating in different fields and sectors. This precision will allow for a more accurate assessment and meaningful comparisons between organisations.

Finally, it is worth questioning whether the measurement of client performance, with only two items, is a limitation. Although the model is based on these two indicators, both by the Delphi result and by the bibliographic review, a greater number of items could improve measurement capabilities. It is essential to balance this consideration with the understanding that an excessive number of indicators for formative constructs might result in the inclusion of indicators with nonsignificant weight (Cenfetelli and Bassellier 2009).

6 Conclusion

The paper expands the field of research that is still incipient in the new digital models, with the industrial omnichannel approach. Within this scarcity of articles, but of growing interest, one of the main axes of study is the one that addresses the point of view of barriers and constraints (Senyo et al. 2019). The model that is presented has been built from the consensus of experts, to subsequently analyse it with a significant sample of multinational companies, manufacturers, and wholesalers. From a theoretical point of view, constructs and variables are identified. From the management point of view, the model structures the main aspects that must be alleviated or mitigated for optimal customer performance.