Man vs machine: Relational and performance outcomes of technology utilization in small business CRM support capabilities

Adam Powell (John L. Grove College of Business, Shippensburg University, Shippensburg, Pennsylvania, USA)
Charles H. Noble (Haslam College of Business, University of Tennessee, Knoxville, Tennessee, USA)
Stephanie M. Noble (Haslam College of Business, University of Tennessee, Knoxville, Tennessee, USA)
Sumin Han (Raymond J. Harbert College of Business, Auburn University, Auburn, Alabama, USA)

European Journal of Marketing

ISSN: 0309-0566

Publication date: 9 April 2018

Abstract

Purpose

The purpose of this paper is to examine the use of technology in customer relationship management (CRM) support capabilities by using an environmental contingency perspective. By examining the moderating effects of micro- and macro-environmental characteristics in which CRM support capabilities are used, the authors seek to extend the literature on CRM technology effectiveness in both customer commitment and overall firm performance. The authors also seek to advance managerial knowledge about CRM support capability technology utilization strategies in various market offering and dynamic market settings.

Design/methodology/approach

The authors utilized a questionnaire to collect data from a sample of 276 small business CRM managers across a wide range of industries. Measures were adapted from the existing literature, and these were largely multiple-item measures of latent variables. The hypotheses were tested using a combination of Ridge regression and a bootstrapping test of mediation. In addition, residual centering was used to reduce multi-collinearity in the interaction analysis.

Findings

The contingency/fit analysis performed in this research highlights the complex nature of the use of technology in CRM support capabilities. The benefits of a man vs a machine CRM support capability depend on the support function (whether marketing, sales, service, data access or data analysis), as well as upon the characteristics of the operating environment. Machine-based marketing support is positively related with customer commitment in turbulent markets, and machine-based service support is preferred in technologically turbulent markets. Sales support, on the other hand, is positively related to customer commitment in technologically turbulent markets when performed by man rather than machine.

Practical implications

CRM support capabilities differ across firms and markets, thus a “one size fits all” approach is not appropriate. This research shows under what conditions a machine-based approach to CRM can be effective for small businesses.

Originality/value

This research is the first to consider market offering and turbulence variables as moderators of the relationship between technology use in CRM support capabilities and customer commitment. Taking this contingency approach, the authors find that resource-based competitive advantage is obtainable based on the fit of the resources (e.g. CRM capabilities) to the environmental characteristics of the firm. Through this perspective that is unique to CRM research, the authors are able to provide both general and specific recommendations to managers and researchers.

Keywords

Citation

Powell, A., Noble, C., Noble, S. and Han, S. (2018), "Man vs machine", European Journal of Marketing, Vol. 52 No. 3/4, pp. 725-757. https://doi.org/10.1108/EJM-10-2015-0750

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Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


“One machine can do the work of fifty ordinary men. No machine can do the work of one extraordinary man.” – Elbert Hubbard, Author and Publisher

1. Introduction

Customer relationship management (CRM) is a “strategic approach that is concerned with creating improved shareholder value through the development of appropriate relationships with key customers and customer segments” (Payne and Frow, 2005, p. 168). While specific practices of CRM may take different forms, a technology-based approach is common (Avlonitis and Panagopoulos, 2005; Keramati et al., 2010), and the selling of CRM technology is big business. Revenue from CRM software sales rose from $20.4bn in 2013 to $23.2bn in 2014, a 13.3 per cent increase (Gartner, 2015). Adoption rates among organizations are also on the rise, with over 51 per cent of sampled organizations utilizing such technologies in 2011 (Economics, 2012). These substantial investments are made in the hopes of achieving many benefits, including more precise customer profitability assessment, centralized customer knowledge storage and access and increased customer satisfaction and retention.

Despite widespread organizational investments in CRM technologies, the success of CRM programs is elusive (Lindgreen et al., 2006), with some questioning the customer and organizational benefits of CRM technology when compared with human approaches to relationship building (Schelmetic, 2006). In terms of the adoption of CRM systems, one study showed less than 40 per cent of 1,275 participating companies experienced employee adoption rates above 90 per cent (Dickie, 2006). In a 2007 UK survey, 80 per cent of senior executives reported that their biggest challenge is getting their staff to use the systems they had installed. A total of 43 per cent of respondents said they use less than half the functionality of their existing system (Sims, 2007). The high stakes and generally disappointing results associated with CRM systems (Kendrick, 2014) show the need for further research in this domain.

This research contributes to the literature in three ways. First, we extend the CRM literature through a contingency theory assessment of CRM technology strategies. A major challenge with CRM research is the diverse and divergent definitions, scopes, functional areas and academic fields involved in its study (Zablah et al., 2004). For this reason, we approach CRM using a contingency framework, which is appropriate when studying the fit of company strategies among diverse environmental characteristics (Van de Ven and Drazin, 1984). Second, this research extends the literature on CRM capabilities. Past research on CRM capabilities has advanced understanding of relational (Tzempelikos and Gounaris, 2015), social (Trainor et al., 2014), customer-focused marketing (Vorhies et al., 2011) and tactical CRM capabilities (Wang and Feng, 2012), as well as their influence on relationship and financial performance. We contribute to this literature through an examination of the level of technology in CRM capabilities within a contingency framework. Third, this study contributes to the literature on small business management, an organization type that should be particularly sensitive to these CRM investment decisions. While a large, global company is almost forced to invest heavily in CRM technology to organize and stay competitive, smaller companies may have a harder time justifying the cost or they may just feel they can provide superior service with a more personal touch.

2. A contingency approach to level of technology in CRM capabilities

In this paper, we use a contingency theory perspective to investigate the influence that the extent of technology utilization in CRM support capabilities has on customer relationship and firm performance. A contingency perspective suggests “that there is no singular structure or strategy that is best for all organizations” (Ahearne et al., 2012, p. 122) and that examining different business strategies in relation to the environmental variables to which they are contingent can provide “richer characterizations” of the complex phenomena (Zeithaml et al., 1988, p. 44). A contingency approach has been used in CRM research both conceptually (Ahearne et al., 2012) and empirically (Daniela, 2015; Wang and Feng, 2012); however, no studies have examined the relationship between the levels of technology in CRM support capabilities and performance using a contingency theory or strategic fit perspective. We assert that there is likely no “one CRM strategy fits all” model and that utilizing a contingency approach to study modern-day CRM can provide needed clarity and valuable insight to the literature.

Day (1994, p. 38) describes capabilities as “complex bundles of skills and accumulated knowledge, exercised through organizational processes, that enable firms to coordinate activities and make use of their assets”. Beyond considering company assets in the resource-based view of the firm, capabilities are also a potential source of competitive advantage (Hooley et al., 1999). Therefore, marketing capabilities research has been founded on the theory that “organizations that stress the development of key capabilities are better able to achieve and maintain a position of advantage despite turbulent environmental impacts on the business” (Vorhies, 1998, p. 3; Achrol, 1991). Key marketing capabilities have been categorized in terms of inside-out, outside-in and spanning processes (Day, 1994), the importance of activities associated with the marketing mix (Vorhies, 1998), specialized and architectural marketing capabilities (Vorhies and Morgan, 2003), retailers’ market responsiveness (Adjei et al., 2009; Griffith et al., 2006) and relationship infrastructure, learning and behavioral capabilities (Jarratt, 2004), to name a few.

Despite the widespread adoption of a capabilities approach to strategy research in marketing, very little research in CRM has taken this perspective. In one example, CRM capabilities are defined as company investments, “in developing and acquiring a mix of resources that enables them to modify their behavior towards individual customers or groups of customers on a continual basis” (Zablah et al., 2004, p. 478). Examples include capabilities surrounding service support (solutions and delivery), analysis support (customer forecasts and lifetime value analysis) and data integration and data gathering methods (Jayachandran et al., 2005). This is consistent with recent research on the antecedents and consequences of CRM capabilities, in which CRM capabilities are described as being:

Reflected in major CRM activities, such as customer interaction management (e.g., customer identification, customer acquisition and customer retention), customer relationship upgrading (e.g., cross-selling and up-selling), and customer relationship win-back (re-establishing relationships with lost but profitable customers) (Wang and Feng, 2012, p. 117; Parvatiyar and Sheth, 2001; Reinartz et al., 2004).

CRM support capabilities are specified in this research as the skills present in the organization that are necessary to or supportive of the essential functions of the corporate CRM processes. Jayachandran et al. (2005) specified five support functions that a CRM system should be capable of doing. These include front-office functions, including sales, marketing and service support, as well as back-office functions, including data integration and access (hereafter “data”) and analysis support (Greenberg, 2004). Companies that accomplish these CRM support capabilities through primarily machine-based means are presumed to be more efficient and effective at fostering customer commitment.

CRM Support Capability: Marketing Support – Marketing support refers to the capacity to provide support for marketing planning and budgeting, assessing marketing campaign response, providing promotional literature, executing promotions, customizing offers to customers and managing customer communications (Jayachandran et al., 2005). These efforts can also include loyalty programs and direct promotional efforts (Greenberg, 2004).

CRM Support Capability: Service Support – Service support provides the resources needed for service delivery scheduling and tracking, access to customer interaction data, providing solutions to service customers and service customization (Meuter et al., 2000). In some cases, service support “helps customers serve themselves by providing ready access to a knowledge base of solution” (Jayachandran et al., 2005, p. 181). More than the other CRM support capabilities, service support is about the direct assistance of customers during their receipt of the market offerings of the company.

CRM Support Capability: Analysis Support – Analysis support is also a back-office function and is the capacity of the firm’s CRM program to examine and utilize the CRM data that have been collected and integrated. CRM data may be used to “understand customer preferences and estimate customer lifetime value, retention, and loyalty” (Jayachandran et al., 2005, p. 181). Channel performance, product profitability and forecasting are also potentially supported through the analysis function of CRM (Greenberg, 2004).

CRM Support Capability: Data Support – While sales, marketing and service support functions are front-office functions, this and the following CRM support capability are back-office functions. Jayachandran et al. (2005, p. 181) suggest that “creating a database that is guided by market intelligence is a critical component of a firm’s attempts to create customer assets through long-term relationships”. This CRM support capability is motivated by the need to have CRM-generated information appropriately integrated with other relevant data and have data accessible to salespeople and other front-line employees. This includes combining transaction data with customer profile data and integrating customer data from multiple contact points (Jayachandran et al., 2005).

CRM Support Capability: Sales Support – Sales support is a CRM capability focused on providing the salesforce with customer information, competitor information and information about customized offers, prospect leads and leads for cross-sell/up-sell opportunities. In addition to supporting salespeople in new business generation, sales support also functions to assist with existing customer maintenance through providing information on product availability and inventory status. In sum, “sales support is designed to help the sales force acquire and retain customers, reduce administrative time, and enable the efficient management of accounts” (Jayachandran et al., 2005, p. 181; Speier and Venkatesh, 2002).

3. Extent of technology utilization in the CRM capabilities of small businesses

The term CRM is often used synonymously with the technology behind the practice of CRM and studies of CRM often incorporate technology as an important aspect of CRM research (Zablah et al., 2004). We incorporate technology in this research by examining the degree to which a firm attempts to achieve CRM support capabilities along a continuum of technology utilization (i.e. the extent of technology used), framed as “man” vs “machine”. At any stage of the CRM process, computers or other electronic devices may be used to leverage firms’ CRM initiatives. We refer to this combination of hardware and software as “CRM technology”, which is the basis of a machine-based approach to CRM. An example of a primarily technology-based CRM strategy is a sophisticated intranet-based service through which business customers can build customized orders directly through the firm’s CRM technology interface without any human interaction. On the other hand, CRM can be primarily human-based. In primarily human-based CRM strategies, frontline employees and service support personnel engage the customer and manage the relationship with little or no aid from CRM technologies. Although we refer to this juxtaposition as “man vs machine” in reality the range of CRM strategies that are used in small businesses today likely form a continuum, with firms using both human and machine-based resources to varying degrees in their CRM practices (Ahearne and Rapp, 2010).

We examine the outcomes of the extent of CRM technology utilization (i.e. this man vs machine continuum) in the small business context for several reasons. First, according to the US Small Business Administration’s Office of Advocacy, 99.7 per cent of all US firms are small businesses[1], with similar numbers reported across Europe, such that small businesses are considered the backbone of the European economy[2]. Thus, their size and economic power across the globe warrants investigation. We adopt the US Small Business Administration classification of small businesses as being those with 500 employees or fewer[3]. These types of businesses are competing against corporate giants such as Nestle, Siemens, Unilever and Tesco, just to name a few. Second, the stakes of CRM system investment are especially high for small businesses for whom IT investment is often substantial (García Pérez de Lema and Duréndez, 2007; Shin, 2006), and the payoff of such investments is unclear (Nguyen et al., 2015). Small businesses are inherently late adopters of technology due to fewer resources (Shin, 2006) and fewer in-house technological expertise (Fuller, 1996), although many have been able to use CRM technologies designed for small businesses. Third, the lack of an IT department in small businesses means that management shoulders the responsibility of CRM systems, often without the necessary expertise to do so (Fuller, 1996). A sophisticated machine-based system for CRM support capabilities might be the norm in larger corporations, but the smaller number of employees and often smaller number of customers in small businesses makes more human-based approaches to CRM a realistic option. In light of these issues, understanding the benefits from a man vs machine approach to CRM capabilities in small businesses can be critical for organizational success.

4. Outcomes of the extent of technology utilization in CRM support capabilities

With few exceptions (Palmatier et al., 2006b; Reinartz et al., 2004), CRM “technology is widely seen as an enabler of an effective CRM system” (Ernst et al., 2011, p. 292; Payne and Frow, 2005). It has also been suggested that “CRM technology plays a critical role in the context of leveraging CRM-related activities and thus contributes to improved organizational performance in the market” (Reinartz et al., 2004, p. 296). Accordingly, we anticipate a positive direct relationship between CRM support capability technology utilization and company performance.

Several empirical studies provide support for this relationship. Rapp et al. (2010) found evidence that a firm’s CRM technical capability is positively related to customer relationship performance. In another study, Saini et al. (2010) examined the influence that the utilization of CRM technology has on CRM performance, including customer retention. They found that CRM technology utilization has a positive direct relationship with CRM performance. Accordingly, we adopt the position that “ceteris paribus, CRM technology functions as a facilitator of CRM activities and contribute[s] to better performance in the market” (Reinartz et al., 2004, p. 296). Specifically, the use of technology in firms’ CRM support capabilities is hypothesized to positively relate with their relational performance. In this study, we utilize an accepted indicator of relational performance: customer commitment. Customer commitment is defined as “an enduring desire to maintain a valued relationship” with the company (Moorman et al., 1992, p. 316), and it has been advanced as a “more suitable concept to capture the intention to continue a business relationship” (Ritter and Andersen, 2014, p. 1007). Customer commitment has been shown to relate positively with word-of-mouth activity and praise (Harrison-Walker, 2001), relationship value creation (Ryssel et al., 2004) and behavioral intentions to invest in and maintain the relationship (Gounaris, 2005). Customer commitment will be used in this study as a marker of the future intentions of the customer to maintain a relationship with the company. Thus, we hypothesize that the extent of technology utilization (i.e. the man vs machine continuum) in CRM support capabilities is positively related with customer commitment:

H1.

The extent of technology utilization in CRM support capabilities (1a: marketing support (MS); 1b: services support (ES); 1c: analysis support (AS); 1d: data support (DS); 1e: sales support (SS)) is positively related with customer commitment.

Figure 1 depicts all of the relationships hypothesized in this research. Next, we consider that relationship outcomes should not be goals unto themselves, as they should result in financial benefits for the company to be truly worthwhile. Consistent with this, customer commitment has been studied as a mediator of loyalty (Dagger et al., 2011) and increased purchase intentions (Lacey, 2007). In this study, we frame commitment as an exogenous variable which is positively related with performance outcomes (e.g. market share, profitability and return on investment). Rapp et al. (2010) found that customer relationship performance is positively related with organizational performance. Taylor et al. (2008) likewise examined the relationship between relationship commitment and business performance and confirmed finding a positive relationship between them. Following this stream of literature, we hypothesize that relational performance (e.g. customer commitment) positively influences firm performance.

H2.

Customer commitment is positively related to firm performance (2a: market share; 2b: profitability; 2c: sales).

Figure 1 also depicts customer commitment as a mediating variable between the extent of technology utilization in CRM support capabilities and firm performance. Palmatier et al. (2006a) performed a meta-analysis of customer-focused relational mediators and found that customer commitment mediated the relationship between organizational characteristics (e.g. relationship investment and seller expertise) and objective performance of the seller. Customer commitment has also been shown to mediate the relationship between trust and switching intentions (Bansal et al., 2004), customer-oriented service employees and customer retention (Hennig-Thurau, 2004) and numerous other antecedents and purchase intentions or loyalty (Čater and Čater, 2010; Lacey, 2007). This literature confirms the assertion that commitment is a key mediating variable that is “critical to the study and management of relationship marketing” (Morgan and Hunt, 1994, p. 31). In addition, according to Zhao et al. (2010), a direct effect does not need to exist to determine mediation. The only requirement for mediation is the presence of an indirect effect. As such, we expect commitment to act as a mediator here, rather than to see direct effects between CRM technology use and firm performance. Therefore, in our model, we anticipate that customer commitment mediates the relationship between the extent of technology utilization and firm performance.

H3a.

Customer commitment mediates the relationship between the extent of technology utilization in CRM support capabilities [3a.MS: marketing support (MS); 3a.ES: services support (ES); 3a.AS: analysis support (AS); 3a.DS: data support (DS); 3a.SS: sales support (SS)] and market share.

H3b.

Customer commitment mediates the relationship between the extent of technology utilization in CRM support capabilities [3b.MS: marketing support (MS); 3b.ES: services support (ES); 3b.AS: analysis support (AS); 3b.DS: data support (DS); 3b.SS: sales support (SS)] and profitability.

H3c.

Customer commitment mediates the relationship between the extent of technology utilization in CRM support capabilities [3c.MS: marketing support (MS); 3c.ES: services support (ES); 3c.AS: analysis support (AS); 3c.DS: data support (DS); 3c.SS: sales support (SS)] and return on investment.

5. A contingency model of CRM support capabilities

Figure 1 depicts our use of a contingency theory perspective to investigate the influence that the extent of technology utilization in CRM support capabilities has on customer relationships and firm performance. The extant literature in marketing supports a contingency approach to research when studying “alternative strategies under various environmental characteristics” (Zeithaml et al., 1988, p 44). This approach is featured prominently in the sales literature when studying salesperson effectiveness (Weitz, 1981) and CRM implementation in business-to-business (B2B) markets (Ahearne et al., 2012). In both of these examples, the authors sought further understanding of the field through the study of salespeople and CRM implementation across differing contexts. Accordingly, our proposed model approaches environmental contingencies of the organization through an interaction approach to fit (Van de Ven and Drazin, 1984), by presenting the extent of technology use in CRM support capabilities as organizational characteristics and customer commitment as the performance outcome that is contingent on micro- and macro-environmental forces.

5.1 Micro-environmental characteristics

The standardized vs customized nature of the market offering of the organization is one micro-environmental characteristic that deserves examination, due to the potential for customer relationship enhancement through customization, made possible through CRM (Stefanou et al., 2003). If a company’s market offering is not customizable it means that they sell goods or services that are standardized (Syam and Kumar, 2006). A standardized offering requires less customer coordination, cooperation and insights prior to the exchange (Gilmore and Pine, 1997). In addition, standardized offerings are less dynamic in nature, enabling the use of a more machine-based approach to succeed, whereas more customizable offerings might require the free thinking of human-based CRM support to drive customer commitment (Hong-kit Yim et al., 2004). In small businesses, the fewer the number of employees, less is the rigidity in structure (i.e. flatter organizations) (Spelman, 2016) and more is the innovativeness in one’s job (Hogg, 2011). Thus, this type of free thinking should be prevalent in smaller businesses over larger organizations. Accordingly, we hypothesize that more customizable offerings will negatively moderate the positive relationship between technology in CRM support capabilities and customer commitment, given the rigidity of technology in its capacity to manage the complexities of customer needs when offerings are customizable. Therefore, we expect:

H4.

The positive relationship between the extent of technology utilization in CRM support capabilities (4a: MS; 4b: ES; 4c: AS; 4d: DS; 4e: SS) and customer commitment is negatively moderated by the customizability of the market offering, such that the more customizable the offering, the weaker the relationship between the extent of technology utilization in CRM support capabilities and customer commitment.

The nature of the type of customer is also important to include as an environmental characteristic in this study (Palmatier et al., 2006a). B2B customers tend to be worth more as customers and often receive the attention of a salesperson who is their interface to the rest of the company. The salesperson, or in some cases, sales team, performs the initial sale and maintains the ongoing relationship (Moon and Armstrong, 1994). Although salespeople often have the support of machine-based CRM support capabilities, the importance of the human-based CRM support capabilities cannot be understated and has become an expectation to some degree with B2B customers. On the other hand, customers in a business to consumer (B2C) relational exchange tend to not have a single point of contact with the company. This is due both to their low relative value to the company, their expectations for service and the large number of customers that B2C firms engage with. Therefore, when a company puts forth the extra effort with a human-based approach to CRM support services customers are likely to take notice. Due to their size and number of customers, larger corporations often cannot put this human touch element into supporting their B2C customers, however, small corporations can garner loyalty from meaningful interpersonal interaction with B2C customers (Noble et al., 2006). As such, for B2C consumers, a more human-based approach (i.e. less technology utilization) to CRM support should be related to commitment more than a machine based approach due to exceeding customer expectations. Thus:

H5.

The positive relationship between the extent of technology utilization in CRM support capabilities (5a: MS; 5b: ES; 5c: AS; 5d: DS; 5e: SS) and customer commitment is negatively moderated by the company’s customers, such that when the company’s customers are individual consumers (rather than business customers), the weaker the relationship between the extent of technology utilization in CRM support capabilities and customer commitment.

5.2 Macro-environmental characteristics

Market turbulence (MT) refers to “the rate of change in the composition of customers and their preferences” (Jaworski and Kohli, 1993, p. 57). In other words, MT is concerned with the stability of customers for companies within a given market. Studies that examine MT often focus on the potential moderating effect of this environmental variable. For example, MT was found to improve the relationship between market orientation and marketing communication capability (Murray et al., 2011), firm innovativeness and business performance (Tsai and Yang, 2013) and information capability and external collaboration effectiveness (Wang et al., 2015).

CRM is inherently influenced by MT due to its focus on consumers and customers. The dynamism of these stakeholders in turbulent markets is therefore hypothesized to influence the relationship between CRM support capabilities and customer commitment. Again, at the core of this hypothesis is the more rigid nature of machine-based CRM systems. IT systems are less adaptable than are humans, who can adjust to the market without reprogramming or scaling disruptions; whereas information transfer on a human level has fluidity and allows for agility (Camisón and Villar-López, 2011). In smaller businesses with a smaller number of employees and flatter organizational structures (Spelman, 2016), this fluidity and agility of information can be advantageous. As such, in more turbulent markets human-based CRM support capabilities are anticipated to relate to higher customer commitment than machine-based processes and capabilities:

H6.

The positive relationship between the extent of technology utilization in CRM support capabilities (6a: MS; 6b: ES; 6c: AS; 6d: DS; 6e: SS) and customer commitment is negatively moderated by market turbulence, such that the more turbulent the market, the weaker the relationship between the extent of technology utilization in CRM support capabilities and customer commitment.

Technological turbulence refers to the uncertainty in the specific industry of the company that is due to changing technology, rather than the dynamism of customers, as in a turbulent market (Jaworski and Kohli, 1993). Technological turbulence suggests that products in the industry are unstable, given a rapid progression of technology. As the machine-based CRM support capabilities of the firm are outpaced by the rate of technological innovations and new product development, a more human-based approach to CRM support capabilities are anticipated to relate positively with customer commitment, again due to the fluidity and agility of information transfer likely capable in small businesses because of their structures (Spelman, 2016). This suggests:

H7.

The positive relationship between the extent of technology utilization in CRM support capabilities (7a: MS; 7b: ES; 7c: AS; 7d: DS; 7e: SS) and customer commitment is negatively moderated by technological turbulence, such that the more turbulent the technology, the weaker the relationship between the extent of technology utilization in CRM support capabilities and customer commitment.

6. Method

6.1 Data collection procedure

To test the hypothesized relationships, we collected data through a targeted, on-line survey of CRM managers. We obtained a sample of respondents who had “CRM Manager” (or “Customer Relationship Management”) in their job title from a commercial list provider. This job title specification was necessary to ensure the respondent would have an adequate knowledge level of the company’s CRM practices. The commercial list broker contacted respondents to inform them about the survey. Only respondents who indicated that their company’s customer relationship strategy has generally stayed the same for the past two years were eligible for the survey. This was to ensure that reported profitability measures reflected the company’s current CRM practices. Owing to the proprietary nature of their respondent sourcing, the commercial list broker could not share further information regarding the sample, including country of origin or specific industry information. Respondents were paid $40 for their participation.

A total of 1,586 CRM managers from the subject panel were eligible for the survey. After the removal of surveys due to missing data issues, a total of 350 managers completed the survey, for a 22.1 per cent response rate. To better understand the small firms’ CRM practices for the current study’s purposes, each respondent was screened on whether the firm size is small. In this study, a small business is defined as a firm with fewer than 500 employees, so respondents were excluded from the sample if the number of employees is greater than 500. After detecting outliers, the final sample size was 276 responses from CRM managers for an effective response rate of 17.6 per cent. Some variables included missing values and those values were replaced by the expectation maximization (EM) approach adopted by Little and Rubin (2014).

Non-response is always a concern in survey research; however, this response rate is comparable to past research (Homburg et al., 2008; Olson et al., 2005). Nevertheless, to test for non-response bias we used the procedures put forth by Armstrong and Overton (1977). No differences existed between early and late respondents on customer commitment, CRM support capabilities or environmental variables, indicating systematic non-response issues are likely not a concern in the present study.

6.2 Measures

Scales for the present study were adapted and modified from items and scales that were used in previous studies. The respective items of the main constructs, reliability measures and their original source are shown in Appendix 2.

6.2.1 Dependent variable.

The firm’s market share, return on investment and profitability were chosen as the firm performance (dependent) variables. Participants rated these company metrics relative to their competitors. They were captured using seven-point scales ranging from “much worse than competitors” to “much better than competitors” (Homburg et al., 2008; Homburg and Pflesser, 2000). Although self-reported data are not ideal for measures of firm performance, the literature suggests that they are a good proxy and highly correlated with objective measures (Dess and Robinson, 1984; Venkatraman and Ramanujam, 1986).

6.2.2 Independent variables.

CRM support capabilities (CP) were measured using five constructs – data analysis support (AS), service support (ES), marketing support (MS), data integration and access (DS) and sales support (SS). This approach was intended to identify strategies which differed in their human vs machine approach across these important factors. The measures were adapted from Jayachandran et al. (2005). A part of this adaptation was to change these measures from a Likert scale to one which measured a semantic differential between human-based and machine-based CRM processes and CRM capabilities. This was done on a seven-point scale, with higher numbers reflecting more machine-based CRM and lower numbers reflecting more human-based CRM; an even mix of human- and machine-based CRM marks the center of the scale. Scales for each dimension of CRM capabilities ranged from three to six items.

6.2.3 Mediating and moderating variables.

Customer commitment was adapted from previous work (Lohtia et al., 2005; Morgan and Hunt, 1994) and measured managers’ perceptions of their customers’ commitment to the firm. Participants rated commitment using seven-point scales ranging from “strongly disagree” to “strongly agree”. The micro-environmental contingencies consisted of the firm’s standardized vs customized offering and customer type. The customized or standardized nature of the firm’s offering was measured on a continuum from completely standardized to completely customized. The firm’s customer type was also measured on a continuum from primarily business to primarily individual customers (Palmatier et al., 2006a).

In addition, organizational culture and institutional theory (DiMaggio and Powell, 1983; Sethi and Iqbal, 2008) suggest that two environmental variables, market turbulence (MT) and technology turbulence (TT) will impact CRM support capabilities. For both macro-environmental contingencies we utilized four-item scales based on Sethi and Iqbal (2008) and they were measured using a seven-point Likert scale for each item anchored by 1 (strongly disagree) and 7 (strongly agree).

6.3 Psychometric evaluations

Using Amos 19, composite reliability, coefficient alpha and the discriminant validity of variables were evaluated. Discriminant validity was assessed through the variance extracted test (Fornell and Larcker, 1981). In this test, the variance extracted estimates for the two constructs of interest are compared to the square of the correlation between the two constructs. Discriminant validity is demonstrated for two constructs if their average variance extracted (AVE) estimates are greater than their squared correlations. All variable combinations demonstrated discriminant validity. The correlations between the constructs as well as simple descriptive statistics (mean, SD) are shown in Table I.

In this study, we applied two methods to check for common method bias issues including, Harman’s single factor test (Podsakoff et al., 2003; Podsakoff et al., 2012) and a partial correlation test (Lindell and Whitney, 2001). Using Harman’s single factor test, the first factor in the exploratory factor analysis accounted for 32.7 per cent of the variance. This was well below the typical 50 per cent recommended cutoff point. In Lindell and Whitney’s (2001) partial correlation procedure our measures were correlated with a measure unrelated to the topic of study (a three-item measure assessing liking for Apple products) and overlapping variance between the marker and the predictor variables is partialled out. If common method bias is not a problem, we would expect very low partial correlations. As shown in Table I, our correlations ranged from 0.0004 to 0.003, illustrating that common method bias does not appear to be a problem in our data.

6.4 Testing approach

To test the hypothesized relationships among the firms’ CRM support capabilities and customer commitment, a Ridge regression was applied. As collinearity tends to inflate the variances of the estimated regression coefficient, the least-squares solution of a traditional multiple regression may produce unstable and biased estimates (Mason and Perreault, 1991). Ridge regression is widely used to relax the multi-collinearity assumptions of a multiple regression by eliminating the correlation effects among independent variables (Lembregts and Pandelaere, 2013). Using STATA 11.2, the hypothesized main effects, mediating effects and moderating effects were tested. We undertake mediation tests by running two regression equations. We implement the bootstrapping procedure recommended by Preacher and Hayes (2004, 2008) with SAS 9.3 and STATA 12 to assess the indirect effect estimates, and then draw on Zhao et al. (2010) to classify the type of mediation. The marketing literature accepts that the bootstrapping method is a superior alternative to estimating indirect effects through the repeated resampling schema (Homburg et al., 2009) and the classification scheme of mediation in Zhao et al. (2010) is now widely embraced. To understand the mediating role of customer commitment, we employ the bootstrapped indirect effect test (5,000 draws) proposed by Zhao et al. (2010).

For estimating main effects and moderating effects including mediation, two separate two-stage Ridge regression models are specified. These models test for main effects of the relationship between CRM support capabilities and customer commitment and moderated effects of micro- and macro-environmental contingencies. We checked normality, linearity and homoscedasticity assumptions for regression analysis using scatterplot and diagnostic tests, and all regression assumptions were satisfied. As well, a residual centering transformation was used to calculate the interaction terms, which resulted in acceptable variance inflation factors (VIF < 3.71; Mean VIF = 2.42).

7. Results

7.1 Main effects and mediating effects modeling

We used Ridge regression to examine the main effects of the extent of technology use in CRM support capabilities on customer commitment (DV1). These results are provided in Table II. H1 states that the extent of technology use in CRM support capabilities will be positively associated with customer commitment. This direct relationship was observed for three of the five support capabilities. The positive sign of marketing support indicates that a more machine-based approach or a greater extent of technology use with this CRM capability is positively associated with customer commitment (β = 0.696, p < 0.001). In contrast, the negative signs for service and analysis support indicate a lesser extent of technology in these CRM capabilities has a greater influence on customer commitment (β = −0.387, p < 0.001; β = −0.286, p < 0.001, respectively). Thus, H1 shows mixed results.

The direct relationship between customer commitment and firm performance comprised H2. As seen in Table III, customer commitment is positively related with market share (β = 0.331, p < 0.001), profitability (β = 0.310, p < 0.001) and sales (β = 0.396, p < 0.001). Therefore, H2 is fully supported.

Turning to the mediating effect of customer commitment hypothesized in H3, Table IV shows the analyses of the indirect effects. First, it should be noted that the direct relationship from CRM support capabilities to the three firm performance measures do not show significant results (see direct path coefficient column results in Table IV). Instead, our results show that the influence of CRM support capabilities to firm capabilities is felt through customer commitment in the majority of cases, as the confidence interval of the indirect effect did not include zero, indicating indirect-only mediation (Zhao et al., 2010). Thus, H3b is supported and H3a and H3c are partially supported. The results of the test of H3a indicate that the relationship between the extent of technology use in CRM support capabilities and market share is indirectly mediated by customer commitment for service support [β = 0.125, CI = (0.001, 0.250)], data analysis support [β = 0.129, CI = (0.004, 0.257)] and sales support [β = −0.140, CI = (−0.274, −0.006)].

The test of H3b, which places profitability as the dependent variable, also provided support for the indirect mediating influence of customer commitment. This was true for all of the support capabilities (MS: [β = −0.187, CI = (−0.338, −0.039)]; ES: [β = −0.138, CI = (0.009, 0.267)]; AS: [β = 0.038, CI = (0.001, 0.076)]; DS: [β = −0.156, CI = (−0.307, −0.008)]; SS: [β = 0.214, CI = (0.062, 0.367)]). The test of H3c likewise provided support for the mediating effect of customer commitment, but with the return on investment performance outcome variable. The lone exception among the CRM support capabilities in this analysis is marketing support. The extent of technology use in service, data analysis, data integration and access and sales support capabilities was indirectly mediated by customer commitment in each of their relationships with return on investment (ES: [β = 0.128, CI = (0.006, 0.249)]; AS: [β = 0.038, CI = (0.009, 0.084)]; DS: [β = 0.173, CI = (0.028, 0.318)]; SS: [β = −0.159, CI = (−0.307, −0.010)]).

7.2 Moderating effects modeling

The hypothesized contingency model frames two micro-environmental variables and two macro-environmental variables as moderators of the influence that technology use in CRM support capabilities has on customer commitment. H4 examined one of these environmental factors, specifically, a micro level contingency of the firm’s standardized vs customized offerings. H4 tested whether a lower level of technology use in CRM support capabilities offered a positive benefit to customer commitment over more technology use when a company’s offerings are customizable. The influence of two CRM support capabilities on customer commitment levels varied as a function of the customizability of the company’s offerings; these significant effects are graphed in Figure 2 and appear in Table II.

As shown in Figure 2, there is marginal support for the theorizations put forth in H4 for the CRM capability of marketing support (left graph), in that a human-based approach led to higher levels of customer commitment than a greater extent of technology use when dealing with high customization offerings (β = −0.136, p < 0.10). Interestingly, a slightly different pattern, yet significant interaction emerged between the capability of CRM service support and customization on customer commitment (β = 0.126, p < 0.05). As shown in Figure 2 (right graph), either a machine-based or a human-based approach to service support had an equal impact on customer commitment when the offer was customized, however, when the offer was standardized, commitment was enhanced through a higher level of technology use.

H5 related to the types of customers (business vs consumer) of the company. When a company’s customers are individual consumers we expect lower levels of technology use in CRM support capabilities to be more positively related with customer commitment than higher levels of technology use. As shown in Table II, support for this hypothesis is found for two of the extent of technology use in CRM support capabilities variables. An inspection of graphs in Figure 3 (from left to right) shows support for the theorizations put forth in H5, in that, a more human-based approach to data integration and access (β = −0.090, p < 0.10) and sales support (β = −0.112, p < 0.05) led to higher levels of customer commitment than did a greater extent of technology use, when the company’s customers were individual consumers.

H6 and H7 outlined the moderating effects of macro-environmental factors. H6 noted that when in a turbulent market, efficiencies of machine-based CRM support capabilities will likely erode customer commitment in comparison to human-based CRM support capabilities that are more fluid and agile. The results for the significant interactions are mixed as can be seen Table II and in Figure 4. When it comes to the CRM capability of marketing support (left graph), a greater extent of technology use yields higher levels of customer commitment than a human-based approach which was not consistent with our theorization (β = 0.347, p < 0.05). The opposite was found for data analysis support. Consistent with the idea that in turbulent markets a human-based CRM approach would lead to more customer commitment, we found for data analysis support (right graph), a human-based approach in a highly turbulent environment yielded marginally higher levels of customer commitment than did a greater level of technology use (β = −0.189, p < 0.10).

In H7 we hypothesized that in technologically turbulent environments, the relationship between the extent of technology use in CRM support capabilities and customer commitment would be negatively moderated, again due to machine inflexibility and lack of agility. As shown in Figure 5 (top graphs, from left to right), we found support for this theorization with the CRM capabilities of data integration and access (β = −0.241, p < 0.05) and sales support (β = −0.397, p < 0.05). However, a greater extent of technology adoption in CRM service support led to marginally more customer commitment in technologically turbulent environments (β = 0.302, p < 0.10).

8. Limitations, discussion and managerial implications

Before discussing our findings and managerial implications, a discussion of the limitations seems warranted. First, approximately 60 per cent of the sample firms had less than 10 employees (Appendix 1); thus, our results are geared toward truly small businesses, aligning with the positioning of this work. Second, our data set was derived from only a single respondent per firm and objective performance data could not be obtained, thus raising potential concerns over common method bias. However, several pre-planned steps were taken to address this concern including the use of a marker variable which was unrelated to our variables of interest. The low correlations of this marker variable to the planned data set increased confidence that common method variance was not a major concern. We also included attention filters within the instrument to ensure that respondents were actively processing the questions – those failing these filters were removed from the data set. Despite these limitations, our results provide insights into different approaches to using the five CRM support capabilities we identified for small businesses, enriching our theoretical and managerial knowledge in this area.

A summary of the findings is shown in Table V. This snapshot also provides an at-a-glance view of the managerial implications of this research. For each CRM support capability, there are circumstances under which customer commitment is not related with utilizing technology. For example, data support and sales support capabilities are performed effectively with or without CRM technology use, except when customers are B2C, or when there is technological turbulence in the market. When either of these contingencies are present, technology utilization should be avoided, as suggested by the minus signs in the data support and sales support rows of Table V. On the other hand, using technology in marketing support, service support and analysis support is either positively related with customer commitment (marketing support) or is negatively related (service support and analysis support). Each of these direct relationships is also moderated by the difference contingency variables, in different ways (see also the figures referenced in each column). As seen in the marketing support row of Table V, having customized offerings calls for less technology utilization, whereas more MT can be best handled with more technology utilization. The negative relationship between technology utilization in service support and customer commitment does not hold constant in the presence of TT, with more technology use needed, once again. Finally, the negative relationship between technology use in analysis support and customer commitment is exacerbated in turbulent markets, calling for less technology in this case.

8.1 CRM technology utilization in marketing support

The marketing support capability entails, among other things, market planning and budgeting, assessing marketing campaign response, executing promotions and managing customer communications (Jayachandran et al., 2005). As hypothesized, small businesses that use technology to carry out these CRM activities also tend to foster more customer commitment and resulting profit. One exception uncovered in the study is when the market offering of the company is customized, more human touch is preferred. Another exception is when MT is low, a balanced approach to CRM technology use might be preferred. Otherwise, this research suggests that small business managers consider utilizing CRM technologies that assist in planning, budgeting and managing customer communications. This is especially true in markets where customer composition and preferences are dynamic. These managerial implications are consistent with past findings on the virtues of automated CRM (Reinartz et al., 2004), generally backed by the idea that profitable efficiencies can be realized as repetitive marketing tasks are performed by machine rather than man-power (Riggins, 2017).

8.2 CRM technology utilization in service support

The service support CRM capability includes service delivery scheduling and tracking, providing solutions to service customers and service customization. Contrary to the hypothesized relationship, technology utilization is not a boon to fostering customer commitment for activities involving services support. Quite the opposite, the more service support is achieved through human-based means, the more customer commitment is likely. This finding is consistent with observations about service delivery made in practice. Specifically, Fremery (2016, p. 1) noted that:

Anytime customers engage with a company, they’re hoping to have a genuine, personalized experience that doesn’t feel “one size fits all” or worse, tone deaf to their specific interests and preferences.

Having a primarily human-based approach to service CRM support capabilities fills this customer need, and it relates positively with customer commitment as a result. In addition to this explanation, we also consider the fluidity and agility of information transfer between customers and employees (Camisón and Villar-López, 2011), which a programed, formulaic information technology system would be hard-pressed to replicate. Managers should carefully consider the purpose of their CRM technologies, and they should avoid technology investments into areas involving services support.

The sole, but substantial exception to this is the relationship between using technology less in CRM services support and customer commitment is when the small business is in a technologically turbulent market. In such a market, the rapid rate of technological advancement is likely a challenge to many small businesses. It appears that when trying to keep with technology in the marketplace, for services specifically, a machine-based approach to CRM is preferred. One possible alternative explanation for this finding is that small businesses that have invested in and utilize CRM technologies in service support are also those that are likely to succeed in a technologically turbulent environment, though this does not hold true for the other CRM capabilities. So, the implication for managers who are considering CRM technology investments in services support tools is they should proceed with caution. Unless their company operates in a technologically turbulent market, maintaining human-based CRM services support capabilities should be better for fostering customer commitment, and related market share, profitability and return on investment.

8.3 CRM technology utilization in analysis support

Analysis support capabilities entail the assessment of channel performance, customer lifetime value, customer retention rates and product profitability. Examining the direct effect of CRM technology utilization in analysis support on customer commitment reveals that investments in technology should not be the emphasis for this capability. However, the volume and complexity of data in small businesses is dwarfed by the data collected in large corporations. Given this understanding, we suspect that in large firms CRM technology utilization in analysis support is positively related with customer commitment. We see in this finding why assuming that CRM technology will solve all problems is a pitfall in CRM (Rigby et al., 2002), specifically in small businesses. Simply, there are CRM support capabilities that produce better outcomes when performed by employees rather than relying heavily on information technology.

This direct effect on customer commitment is moderated by MT, suggesting that this man-based emphasis on analysis support is only effective in turbulent markets, but that companies in stable markets experience no rise in customer commitment from either more man or machine-based analysis support. It is noteworthy that this interaction is moderately significant and that analysis support does not interact with any of the other micro- or macro-environmental characteristics. As with all of the non-findings in the contingency analysis, an absent interaction suggests that managers should not emphasize man or machine, depending on the environmental characteristic, but strike a careful balance between them (Chen and Popovich, 2003). Although this article and analysis and discussion focuses on statistically significant findings, the importance of the non-findings should not be overlooked.

8.4 CRM technology utilization in data support

Of all the CRM support capability constructs, data support places the most emphasis on customer data. It refers to the means by which customer data is gathered, integrated and accessed. For companies large enough to support complex information technology systems, the expectation is that customer data could be gathered and integrated digitally. However, for small businesses, data gathering and entry are often the responsibility of front-office personnel. Despite this, a more balanced human-/machine-based approach to CRM technology utilization in this CRM capability is more beneficial in our sample than either a “high-touch” or “highly automated” approach with these CRM support capabilities.

This recommended balanced approach to CRM technology use in data capabilities should be adjusted for the customer type and TT contingencies. When small businesses are engaged in data support activities for B2C rather than B2B customers, surprisingly, a more human-based approach is results in higher commitment. For large corporations B2C customers are generally too numerous and not profitable enough to provide them with human-based attention. However, for small businesses, it appears that a hands-on experience may be expected by their individual customers, this being one of the benefits of patronizing a smaller company. For small business managers, it appears that a careful balance between having enough hands-on experience for customers and maintaining profit margins should be sought.

The extent of CRM technology utilization in the data support capability is also moderated by technological turbulence in its relationship with customer commitment. As hypothesized, in technologically turbulent industries, human-based data access and integration is positively related with customer commitment. Similar to the discussion above, customers expect a certain hands-on experience with small businesses. However, it may be challenging for small businesses in turbulent markets to remain profitable while maintaining human support. In highly technical industries, especially, companies tend to look to automation to reduce costs. This research suggests that taking this too far in customer data gathering, integration and access may be detrimental to the company.

8.5 CRM technology utilization in sales support

The level of customer commitment for small businesses is likewise not dependent on the extent of CRM technology utilization in sales support capabilities. This capability involves the provision of customer information to the sales force, including lead assignment, based on the needs of the customers and fit to the salesperson. Sales force automation is a burgeoning industry, with salesforce.com the market leader, followed by SAP, Oracle and others. This market has expanded to $26.3B in 2015 (Columbus, 2016), suggesting that most modern sales forces have information technology assistance at their fingertips. It follows, then, that B2B customer commitment is maintained by both man and machine-based CRM sales support. On the other hand, customer commitment for B2C customers who interface with salespeople are better served with a more human-based approach to CRM. Again, this may stem from consumers’ desire for personalized experience with the company (Fremery, 2016).

Technological turbulence likewise plays a role in man vs machine CRM sales support’s relationship with customer commitment. As hypothesized, it seems that the rigidity of information technology, or the fluidity of the human touch, lends human-based CRM sales support to be positively related with customer commitment in technologically turbulent markets. As with data support, small business managers are advised to not overlook the importance of their employees in CRM in technologically turbulent markets.

8.6 Discussion overview

Taken together, the results of the main effects in this study illustrate that small businesses need to assess each CRM support capability individually to determine the level of CRM technology needed. These support capabilities are leveraged differently in machine and human-based approaches, and thus, the potential competitive advantage of each CRM support capability needs to be assessed via this man vs machine CRM continuum to determine how each can best be leveraged. These results support a capabilities framework (Day, 1994; Hooley et al., 1999; Vorhies and Morgan, 2003) by showing firms can leverage a resource (i.e. particular CRM support capability) via either a man or machine approach to CRM (or a balanced approach) to achieve competitive advantage (i.e. customer commitment).

Our results further show that no universal CRM strategy should be seen as the epitome of what all small businesses should strive for. Instead, the use of CRM technology appears to be context specific when trying to realize performance outcomes. Small businesses that have primarily B2C customers and customized offerings obtain benefits in some areas from a human-based approach, but not in all areas. In turbulent environments (market or technological), both a machine and human-based approach could help, but again, it depends on the CRM support capability in question. A key message for managers is that a human-based approach can leverage CRM capabilities just as a machine-based approach can, but that the environment might be a deciding factor about whether a man vs machine-based approach is best for their company.

8.7 Theoretical implications

Our study makes several theoretical contributions. First, our results show that a contingency approach to the examination of CRM technology utilization in support capabilities is warranted as there is “no singular structure or strategy that is best for all organizations” (Ahearne et al., 2012, p 122). Examining the interaction of both the CRM support capability (e.g. service support, analysis support, data integration and access, etc.) and the environmental contingency factor yields a better indication of whether a man vs machine approach leads to better outcomes than one element in isolation.

Second, our results show that CRM support capabilities are not constructs that necessarily move in the same direction when in the same environment. In other words, the CRM support capabilities of marketing, service, sales, data integration and access and data analysis are not, together, reflective of a single CRM support capabilities construct and should not be treated as such when studying the effectiveness, outcomes or approaches of each.

Third, the extent of technology use in CRM support capabilities does not have a direct influence on firm outcomes, but rather is mediated through customer commitment. Thus, understanding the impact that CRM support capability technology has on the customer is critical to understanding how it will impact firm performance measures.

Fourth, our boundary conditions also add to our theoretical knowledge. We examined both micro and macro environmental factors and found moderating effects for each across different CRM support capabilities. Therefore, our results show that firm level factors such as the standardization vs customization of the offering and the types of customers of the firm (B2B vs B2C), as well as more macro level factors such as the degree of turbulence in the environment (both market and technology) are important to consider when understanding relational and performance outcomes of human vs machine approaches to CRM capabilities.

Finally, our results extend our knowledge about the extent of technology use in CRM support capabilities. Jayachandran et al. (2005) illustrated the antecedents and outcomes of CRM capabilities, but did not study the human vs machine elements of these CRM strategies (nor has this been examined in the CRM literature). Thus, our findings provide novel insights into ways to implement CRM technology strategies within small businesses.

9. Conclusion

The goals of this research were to examine how different approaches to technology use in CRM support capabilities influences commitment and firm performance for small businesses. Using data from CRM managers across a range of industries, we identified situations when man vs machine approaches yielded differential outcomes under micro and macro environmental conditions and offered theoretical and managerial insights from these findings. This study represents the first to identify human vs machine-based approaches to CRM support capabilities and to link those to performance outcomes, while also suggesting the value of focused future research and thinking in this important area.

Figures

Extent of technology use in CRM support capabilities contingency model

Figure 1.

Extent of technology use in CRM support capabilities contingency model

Moderating effects of offering customization on customer commitment

Figure 2.

Moderating effects of offering customization on customer commitment

Moderating effects of customer type on customer commitment

Figure 3.

Moderating effects of customer type on customer commitment

Moderating effects of market turbulence on customer commitment

Figure 4.

Moderating effects of market turbulence on customer commitment

Moderating effects of technology turbulence on customer commitment

Figure 5.

Moderating effects of technology turbulence on customer commitment

Mean, standard deviation, and correlation between constructs

Mean SD Partial correlation 1 2 3 4 5 6 7 8 9 10 11 12 13
1. MS 4.55 2.71 0.001 NA
2. PROF 4.56 1.30 0.0004 0.574*** NA
3. ROI 4.63 1.24 0.0003 0.592*** 0.830*** NA
4. COM 4.65 1.35 0.004 0.261*** 0.241*** 0.254*** 0.72
5. AS 4.47 6.03 0.001 0.101 0.147** 0.123** −0.012 0.68
6. ES 3.51 2.31 0.0001 0.093 0.103 0.099 0.012 0.643*** 0.62
7. MS 3.93 2.29 0.0001 0.033 0.058 0.036 0.022 0.612*** 0.655*** 0.60
8. DS 4.19 2.35 0.0002 0.085 0.139** 0.157*** −0.078 0.661*** 0.494*** 0.612*** 0.57
9. SS 4.38 1.17 0.0001 −0.118 −0.275*** −0.138** 0.044 −0.583*** −0.494*** −0.559*** −0.605*** 0.64
10. OC 3.99 2.67 0.0001 −0.075 −0.065 −0.016 −0.047 −0.013 −0.027 −0.004 −0.004 0.038 NA
11. CT 4.59 1.87 0.0003 −0.027 0.003 −0.022 0.050 −0.028 −0.043 −0.065 −0.051 −0.061 0.022 NA
12. MT 4.23 2.83 0.0000 −0.071 −0.058 −0.083 −0.113 −0.013 −0.026 −0.055 −0.030 −0.051 0.030 0.048 0.63
13. TT 4.84 2.37 0.0001 −0.045 −0.073 −0.027 0.102 −0.109 −0.140** −0.105 −0.068 0.020 0.096 −0.147** 0.005 0.65
Notes:

***p < 0.001; **p < 0.05 (two-tailed); The numbers on the diagonal are the average variance extracted by each construct; 1 = market share, 2 = profitability, 3 = ROI, 4 = commitment, 5 = analysis support, 6 = service support, 7 = marketing support, 8 = data support, 9 = sales support, 10 = offering customization, 11 = customer types, 12 = market turbulence, 13 = technology turbulence, NA = AVEs are not applicable for single item measures

Estimated direct and moderated relationships with customer commitment

Model 1
Main effects
Model 2
Moderating effects
IVs – H1
Marketing support (MS) 0.696 (0.207)*** 0.208 (0.136)
Service support (ES) 0.387 (0.123)*** 0.098 (0.113)
Analysis support (AS) 0.286 (0.142)** −0.019 (119)
Data Integration and Access
Support (DS)
0.293 (0.263) −0.010 (128)
Sales Support (SS) 083 (0.055) 0.151 (0.111)
Moderators
Offering Customization (OC) 0.036 (0.043)
Customer Types (CT) 0.041 (0.028)
Market Turbulence (MT) –0.045 (1.03)
Technology Turbulence (TT) −0.028 (0.077)
Interactions
H4: MS × OC 0.136 (0.082)*
H4: ES × OC 0.126 (0.068)**
H4: AS × OC 0.019 (0.070)
H4: DS × OC −0.030 (0.059)
H4: SS × OC 0.077 (0.063)
H5: MS × CT −0.015 (0.061)
H5: ES × CT −0.021 (0.050)
H5: AS × CT 0.017 (0.051)
H5: DS × CT 0.090 (0.052)*
H5: SS × CT 0.112 (0.052)**
H6: MS × MT 0.347 (0.167)**
H6: ES × MT −0.064 (0.134)
H6: AS × MT 0.189 (0.146)*
H6: DS × MT −0.071 (0.132)
H6: SS × MT −0.104 (0.077)
H7: MS × TT 0.055 (0.171)
H7: ES × TT 0.302 (0.171)*
H7: AS × TT −0.150 (0.155)
H7: DS × TT 0.241 (0.118)**
H7: SS × TT 0.397 (0.218)**
Adjusted R2 0.216 0.295
Log-likelihood −441.838 −399.994
Notes:

Standardized estimates, standard errors in parentheses ***p < 0.001; **p < 0.05 (two-tailed); *p < 0.1

Estimated effects of customer commitment on performance

Market share Profitability Sales
Customer commitment 0.331 (0.078)*** 0.310 (0.078)*** 0.396 (0.106)***
Adjusted R2 0.064 0.057 0.085
Log-likelihood −440.404 −440.662 −445.820
Notes:

Estimates, standard errors in parentheses; ***p < 0.001

Bootstrapped indirect effect estimates on firm performance

Hypothesized indirect effects Direct path coefficient Indirect path coefficient Confidence interval of indirect effects
Lower CI Upper CI Type of mediation
H3a MS → customer commitment → market share −0.009 0.051 −0.111 0.210 No mediation
H3a ES → customer commitment → market share −0.003 0.125* 0.001 0.250 Indirect-only mediation
H3a AS → customer commitment → market share 0.005 0.129* 0.004 0.257 Indirect-only mediation
H3a DS → customer commitment → market share 0.026 0.086 −0.065 0.239 No mediation
H3a SS → customer commitment → market share −0.015 −0.140* −0.274 −0.006 Indirect-only mediation
H3b MS → customer commitment → profitability −0.004 −0.187** −0.338 −0.039 Indirect-only mediation
H3b ES → customer commitment → profitability 0.003 −0.138* 0.009 0.267 Indirect-only mediation
H3b AS → customer commitment → profitability −0.072 0.038* 0.001 0.076 Indirect-only mediation
H3b DS → customer commitment → profitability −0.024 −0.156** −0.307 −0.008 Indirect-only mediation
H3b SS → customer commitment → profitability 0.013 0.214** 0.062 0.367 Indirect-only mediation
H3c MS → customer commitment → ROI −0.063 0.009 −0.031 0.049 No mediation
H3c ES → customer commitment → ROI −0.050 0.128* 0.006 0.249 Indirect-only mediation
H3c AS → customer commitment → ROI −0.034 0.038* 0.009 0.084 Indirect-only mediation
H3c DS → customer commitment → ROI 0.024 0.173** 0.028 0.318 Indirect-only mediation
H3c SS → customer commitment → ROI −0.014 −0.159** −0.307 −0.010 Indirect-only mediation
Notes:

CRM support types include marketing (MS); service (ES); data analysis (AS); data integration and access (DS); sales (SS); all path coefficients are reported in standardized form; *p < 0.1; **p < 0.01; results are based on two-tailed t-tests; level for the confidence intervals are chosen by the significance level of the indirect path coefficient

Managerial implications of CRM technology use

When not contingent Micro-environmental contingencies Macro-environmental contingencies
Customized offering
(Figure 2)
B2C customers
(Figure 3)
Market turbulence
(Figure 4)
Technology turbulence
(Figure 5)
Marketing support (MS) †† ††
Service support (ES) – – * ††
Analysis support (AS) – –
Data support (DS) – –
Sales support (SS) – –
Notes:

†† Technology utilization positively related with customer commitment (r > 0.20); – technology utilization negatively related with customer commitment (r < 0.20); – – (r > 0.20); * although this was a moderated relationship, there is no reasonable implication to be derived from the results (Figure 2)

Composition of the sample

Variable Category Frequency (%) Mean Median
Gender Male 152 55.1
Female 124 44.9
Industry Service 100 36.2
Manufacturing/logistics 28 10.2
Marketing/sales 24 8.7
Media/entertainment 15 5.4
Real Estate/construction 28 10.2
Retail/wholesale 24 8.7
Social service 26 9.4
Technology 21 7.6
Others 10 3.6
Number of employees <5 130 47.4 40.53 5.00
5-9 46 16.7
10-49 57 20.2
50-100 18 6.5
101-500 25 9.2
Work experience with current company <5 years 62 22.5 13.26 12.00
5-10 years 71 25.7
11-20 years 85 30.8
>20 years 58 21.0
Work experience in position <5 years 63 22.8 11.82 10.00
5-10 years 65 23.5
11-20 years 85 30.9
>20 years 63 22.8
Total 276 100.0

Scale item sources and statistics

Construct Items (coefficient alpha/composite reliability)
CRM support capabilities
Reflective measure based on Jayachandran et al. (2005)
Seven-point differential
(anchors: 1= “Primarily technology based means”. 2 = “Primarily technology based means with limited human support”, 3 = “Technology based means with human support”, 4 = “An even balanced of human and technology based means”, 5 = “Human based means with technology support”, 6 = “Primarily human based means with limited technology support”, 7 = “Primarily human based means”
Marketing support (0.910/0.895)
Marketing planning and budgeting is supported through….
Responses to marketing campaigns are assessed through….
Routine activities such as providing promotional literature are performed through….
Marketing promotions are enabled through….
Customized offers are created through….
Communications with customers are customized through….
Service support (0.856/0.892)
Customer interactions with all functional areas of the firm, customer support relies on….
Solutions to commonly occurring problems are provided to customers through….
Service delivery tracking is primarily handled through….
Service delivery scheduling is primarily handled through….
Service scripts are customized to particular customer needs through…
Analysis support (0.908/0.900)
Channel performance is assessed through….
Customer preferences are forecasted through….
Customer loyalty is measured through….
Customer life time value is assessed through….
Customer retention rates are assessed through….
Product profitability is primarily assessed through…
Data integration and access support (0.863/0.853)
Customer transaction data are combined with externally sourced data through….
'Customer information from different contact points (e.g. mail, telephone and Web) is integrated through….
Relevant employees have access to unified customer data through…
Sales support (0.928/0.911)
…. provide(s) sales force in the field with customer information
…. provide(s) sales force in the field with competitor information
…. assign(s) leads and prospects to appropriate sales personal
…. provide(s) customized offers to sales people in the field
…. provide(s) the sales force with leads for cross-sell/up-sell opportunities
Customer commitment
Reflective measure based on Crosby et al. (1990), De Wulf et al. (2001), Johnson et al. (2004), Moorman et al. (1992), Morgan and Hunt (1994)
Seven-point scale
(anchors: 1= “Strongly disagree” and 7 = “Strongly agree”
Customer commitment (0.854/0.874)
Our customers would say that the relationship they have with our firm:
…. is something they are very committed to
…. is very important to them
…. is of very little significance to them
…. is something they intent to maintain indefinitely
…. is very much like being family
…. is something they really care about
…. deserves their maximum effort to maintain
Relative outcomes
Measure adapted from Homburg et al. (2008b) and Homburg and Pflesser (2000)
Seven-point scale
(anchors: 1= “Much worse than competitors”, 4= “about the same as competitors”, and 7= “much better than competitors”
Market share, profitability, return on investment
Relative to competing firms in your industry, how has your firm performed in the past 12 months, in the following areas:
Market share
Profitability
Return on investments
Offering customization
Seven-point scale
(anchors: 1= “Primarily product offering” and 7= “Primarily product offering”
Which of the following best describes your firm’s types of offering(s)?
Customer types
Seven-point scale
(anchors: 1= “Primarily business customers (B2B)” and 7= “Primarily consumers (B2C)”
Which of the following best describes the type of customer your firm serves?
Market turbulence
Measure adapted from Sethi and Iqbal (2008)
Five-point scale (anchors: 1= “Strongly disagree” and 7= “Strongly agree”)
Market turbulence (0.862/0.876)
Please indicate the extent to which you agree with the following statements:
It is difficult to predict how customers’ needs and requirements will evolve in our markets
It is difficult to forecast competitive actions
Generally, it is difficult to understand how the market will change
There is a great deal of uncertainty in our markets
Technology turbulence
Measure adapted from Sethi and Iqbal(2008)
Five-point scale (anchors: 1= “Strongly disagree” and 7= “Strongly agree”)
Technology turbulence (0.872/0.881)
Please indicate the extent to which you agree with the following statements:
The technology in our industry is changing rapidly
Technological changes provide big opportunities in our industry
A large number of new product or service ideas have been made possible through technological breakthroughs in our industry
Technological developments in our industry are rather minor

Notes

Appendix 1. Sample composition

Table A1

Appendix 2. Scale items for construct measures

Table A2

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Corresponding author

Adam Powell can be contacted at: wapowell@ship.edu