Consequences of Multigenerational Services Adoption Behavior: Global Client Engagement

Abstract

This study uses adoption and usage data on the client and firm–client interactions across four technology generations of new-age products/services from 13 developed and emerging markets over an eight-year period to describe how multigenerational service (MGS) adoption behavior influences direct (purchases) and indirect (references and feedback) global client engagement and whether this relationship is moderated by product/service failures and cultural factors. The authors propose metrics to measure the number of generations adopted (MGD), the number of products and features within a generation (MGFs), and the adoption time between generations (MGT). They find that client usage revenue (CUR) is enhanced by greater MGD and higher MGFs combined with lower MGT. However, CUR varies by differences in the needs of clients' own customers, failures, and culture. Greater direct engagement affects reference and feedback behavior, moderated by cultural differences in individualism, power distance, and masculinity. For a typical client in the United States and Canada, a one-unit improvement in MGD and MGFs and a one-year improvement in MGT enhance CUR by $8,150, $5,200, and $2,310 per client, respectively, versus a corresponding enhancement of $4,820, $3,640, and $1,620, respectively, per client in Colombia and Mexico. These findings provide several implications for executives who manage multigenerational innovations across countries regarding client engagement, launching MGS, market entry, and failure recovery.

Keywords

multigenerational services multigenerational depth multigenerational breadth multigenerational adoption time client engagement developed and emerging countries

The concept of customer engagement has attracted significant interest from both the academic and practitioner communities, as evidenced from many research studies, business conferences, educational seminars, and events organized on topics related to customer engagement in recent years (Brodie et al. 2011; Kumar and Pansari 2016; Van Doorn et al. 2010) and globally (Gupta, Pansari, and Kumar 2018). Customer engagement enhances clients’ knowledge of, involvement with, and commitment to the seller firm and strengthens long-term relationships, with benefits to both the firm and the clients (Palmatier, Kumar, and Harmeling 2018).

In recent years, multinational corporations have launched multigenerational services (MGSs) in global business-to-business (B2B) markets but are struggling with building and sustaining client engagement across multiple generations (Sood and Kumar 2018). For example, Intel has introduced multiple generations of innovative products and services across AI, networking, and data centers in the last few years. Similarly, the past few decades have witnessed a dramatic transformation of the manufacturing industry in many countries as firms upgrade internal manufacturing processes using automation, robotics, the Internet of Things, 3D printing, and AI. Multigenerational innovations are prevalent in B2B and business-to-consumer (B2C) markets, and each generation improves the existing performance and adds new functionalities to older product generations. Bass and Bass (2004) examine the diffusion of multigeneration products and define “product generation” as the set of product brands and models fitting the customer-perceived functionality characteristics of the generation. A similar conceptualization in the context of computer software is the evolution and growth of multiple generations over time, such as machine language (first generation); assembly language programming languages (second generation); structured programming languages such as C, COBOL, and Fortran (third generation); and object-oriented programming languages (fourth generation). Firms also launch multiple generations of proprietary software. For example, Varien Inc. first developed and launched a competitive price monitoring software, Magento, in 2008. It later released Magento 2.0 in 2015 with significant performance and security improvements over the first generation. Similarly, many firms have created a huge market for cloud services (see Web Appendix A, Figure W1a). Another familiar example from B2C markets is Apple Inc., which, despite being a late entrant, successfully built a formidable market dominance in the smartphone industry through multiple product generations of the iPhone (see Web Appendix A, Figure W1b).

However, one of the primary challenges for firms in such circumstances is to retain their relationship and engagement with existing clients as they continue to adopt multiple generations. Firms strive to enhance direct and indirect engagement with their clients to ensure overall market success (Kumar and Pansari 2016; Kumar, Petersen, and Leone 2013). In a direct engagement, clients generate value for the firm by purchasing from or subscribing to the firm's products and services and through continued patronage and long-term loyalty (Gupta, Pansari, and Kumar 2018; Pansari and Kumar 2017). Research on customer lifetime value suggests that firms can measure clients' direct engagement for MGSs in a B2B setting in the form of client usage revenue (CUR) over a given time (Reinartz and Kumar 2000, 2003; Venkatesan and Kumar 2004). From B2B market practice and our managerial interviews with client firms, we observe that client usage influences client references and client feedback. In indirect engagement, clients can generate value for the firm by providing references, positive word of mouth (WOM), and feedback. We suggest that in the B2B setting, firms can measure indirect engagement through two metrics: successful client references and valuable client feedback. Satisfied clients may share their experiences with other firms across industries within their sphere of influence and positively influence adoption decisions. We define successful client references as the number of prospects referred to the firm by a client the firm has successfully acquired as new clients (hereinafter, client references). Existing clients can also create value for the firm by providing feedback. Incremental improvements in product designs, features, and functionality can help firms increase overall functionality, usage, and customer satisfaction. We define valuable client feedback as the number of feedback comments provided by a client on the new generation that the firm accepts to improve the innovation generation (hereinafter, client feedback).

However, clients display variations in the adoption pattern of MGS, such as adopting only a few generations, leapfrogging over others, or varying the timing of adoption (Bass and Bass 2004; Jiang and Jain 2012; Norton and Bass 1987; Pae and Lehmann 2003). Alternatively, clients may adopt only select features within a generation (Hauser, Tellis, and Griffin 2006; Mahajan, Muller, and Bass 1993). Therefore, understanding how the adoption pattern over time will affect client engagement within and across countries is important. Informed by our interviews with marketing and sales executives of sellers and client firms in developed and emerging markets and a review of the innovation adoption literature, we use three metrics that collectively capture variations in client adoption patterns across multiple generations: (1) multigenerational depth (MGD), or the number of innovation generations a particular client adopts; (2) multigenerational features (MGFs), or the number of the different products or services within an innovation generation adopted by a client; and (3) multigenerational adoption time (MGT), or the average time between adoptions of MGSs by a client. Whereas Sood, Kumar, and Gupta (forthcoming) also use these three metrics to examine the antecedents of MGS by identifying factors influencing the adoption decision, we focus on the consequences of adopting an MGS and show that clients that adopt multiple generations and multiple features within a generation sooner do not necessarily generate higher revenues for the seller firm than clients that adopt fewer and later.¹

Furthermore, the country's cultural differences and economic factors influence global clients’ direct and indirect engagement with the firm (Gupta, Pansari, and Kumar 2018). Dwyer, Mesak, and Hsu (2005) suggest that variation in national culture serves as a dominant predictor in explaining variations in cross-national diffusion patterns. Thus, understanding cultural nuances may be critical when global clients adopt and use MGSs over time. Moreover, customer engagement theory proposes that postadoption experiences increase clients’ repeat purchases, references, and feedback to the firm (Kumar 2013). We also explore how product or service failures at the postadoption stage moderate the relationship between MGS adoption behavior and CUR. In the B2B context, product and service failure has a considerable impact on a client's use of the adopted services, because the failure directly affects the experience of the client's customers, which may have implications for its revenue given its inability to deliver the guaranteed service. We identify a failure as an incident in which the seller firm could not adequately resolve a complaint filed by a client in the guaranteed time (usually within a 24- to 48-hour period). To the best of our knowledge, no research has examined the variation in the adoption pattern over time on client engagement within and across countries, while considering both cultural and failure factors.

To fill this gap, in this study, we show how MGS adoption behavior measured by the three MGS adoption metrics affects CUR, client references and client feedback. Using observed data of four generations of services adopted by the clients of a large B2B technology manufacturer operating across 13 developed and emerging markets in North America, South America, Europe, Asia, and Australasia over an eight-year period, we address the following research questions:

How does a client's MGS adoption behavior (measured by MGD, MGFs, and MGT) individually and collectively affect the client's usage revenue with the firm?

How do product/service failures and cultural factors affect the relationship between MGS adoption and CUR? How does CUR affect client references and client feedback? How do cultural factors moderate the relationship between client usage and client references and feedback?

What are the managerial and theoretical implications for global client engagement strategies for the launch of MGSs by multinational firms?

First, the findings show that each MGS adoption metric (MGD, MGFs, and MGT) differentially influences a client's usage revenue with the firm. Second, failures and specific cultural differences influence the effect of MGS adoption on CUR. Third, CUR enhances client references and feedback. As a result, increasing client usage leads to multiple benefits. Fourth, when we control for firmographics, cultural differences influence the effect of client's usage revenue on client references and feedback.

We compare the impact of all three MGS adoption metrics on client engagement, both direct and indirect. For example, our results show that in the United States and Canada, a one-unit improvement in MGD and MGFs and a one-year improvement in MGT translates, on average, to an increase of 11%, 7%, and 3% in CUR per client, respectively. The impact of these improvements in MGS adoption metrics translates (through CUR increases), on average, to an increase of 7%, 5%, and 2% of client references per client, respectively, and an increase of 6%, 4%, and 2% of client feedback per client, respectively. By contrast, the corresponding numbers for emerging countries are considerably different. Thus, our framework helps managers allocate scarce resources and build dynamic capabilities to enhance overall firm revenues by fostering MGS adoption. In summary, we develop generalizable findings on the relationships between client adoption metrics and client engagement, as our data represent both developed and emerging markets.² This study also empirically validates the theoretical principles of customer engagement and develops new managerial insights into B2B markets.

We discuss the research motivation in the next section. We then draw on the literature to propose how MGD, MGFs, and MGT influence client engagement. Finally, we discuss the research methodology, model results, implications for managers, study limitations, and potential directions for further research.

Research Motivation

The primary focus of our research is to develop insights into how the speed and the level of a new MGS adoption influence direct and indirect engagement between a firm offering multigenerational products and its clients located across countries. We define MGS as a series of successive generations of bundled innovative products and services. In the long run, multigenerational innovation contributes to building a stronger demand by creating a higher level of direct and indirect customer engagement across developed and emerging markets and bringing in high revenues at the customer segment level (Sood and Kumar 2018).

Our review of the literature reveals three research gaps. First, despite the many calls in extant literature to examine the new product diffusion of MGSs in multiple countries, research in this area is scarce. The literature on cross-national diffusion of innovations is extensive and spans more than five decades (Bass 1969). Given the large number of studies published in this domain, we do not present a full review of the literature and limit our focus to some of the key review studies (Chandrasekaran and Tellis 2017; Mahajan, Muller, and Bass 1993; Meade and Islam 2006; Peres, Muller, and Mahajan 2010), selecting articles more directly related to our research questions to establish the research gap and highlight the contributions of our study. Mahajan, Muller, and Bass (1993) call for research examining how product features influence the new product diffusion process. Peres, Muller, and Mahajan (2010) highlight the differences in diffusion patterns between emerging and developed countries and stress the need for future research in cross-country comparisons. In their review of the literature, Meade and Islam (2006) stress the need for research to examine the launch of multiple generations of services across multiple countries. Chandrasekaran and Tellis (2017) also focus on consumer durables and call for future research to focus on services. MGSs offer a unique context, especially given the technological advancements in the past few decades enabling firms to develop technological platforms to offer multiple services to potential clients. Clients then decide to adopt the new platform, but firm revenue is generated only from clients’ continued usage of services on that platform. Thus, examining not only the decision to adopt a new MGS platform but also the decision to adopt multiple features within that generation is important.

Second, to our knowledge, no study has examined all three MGS adoption metrics (MGD, MGFs, and MGT). One stream of research offers various modeling approaches to examine the adoption of multiple generations (Islam and Meade 1997; Meade and Islam 2006; Kim, Srivastava, and Han 2001; Mahajan and Muller 1996; Norton and Bass 1987). Prior research has explored various factors influencing the diffusion process, including forward-looking behavior (Shi, Fernandes, and Chumnumpan 2014), customer satisfaction with prior generations (Bansal, Anand, and Aggrawal 2021), adoption speed (Kapur, Panwar, and Singh 2019; Stremersch, Muller, and Peres 2010), and price and consumer behavior (Tsai 2013). Kim, Jee, and Sohn (2021) examine firms’ decision whether to launch multiple generations of products under a regular schedule but with uncertain quality or to launch intermittently but with assured quality. The size and speed of improvements in performance and functionality also vary across generations (Sood and Tellis 2005). For example, firms in the automobile, engineering, and financial services industries regularly launch multiple generations of their products and services. Some clients regularly adopt each innovation generation, while others adopt more selectively, such as by leapfrogging over some innovation generations or delaying their adoption decisions. However, we uncovered no study that examines all three metrics, including their interrelationships, as we do herein (see Table 1).

Table 1.

Relevant Literature Review.

Study	Study Objective	Data on …		# Generations	N	Empirical/Conceptual	Dependent Variables	Independent Variables	Measures …		Examines Effect of …		Implications
Study	Study Objective	Developed Markets	Emerging Markets	# Generations	N	Empirical/Conceptual	Dependent Variables	Independent Variables	Direct Client Engagement	Indirect Client Engagement	Economy	Culture	Implications
Bansal, Anand, and Aggrawal (2021)	Impact of customer satisfaction with the first generation on the adoption of subsequent generation, accounting for competition within generations and across brands.	No	India	4	N.A.	Empirical	Sales	Brand switching behavior, customer satisfaction	No	No	No	No	Developed a multigenerational-based mathematical model incorporating competition and satisfaction factors.
Kapur, Panwar, and Singh (2019)	New product diffusion model incorporating dynamic prices and goodwill.	No	No	N.A.	N.A.	Conceptual	Sales	Prices and goodwill	No	No	No	No	Proposes a new two-dimensional diffusion model integrating goodwill and the impact of price change in a diffusion process.
Shi, Fernandes, and Chumnumpan (2014)	New product diffusion model incorporating customers’ forward-looking behavior in multigenerational products	No	No	2–6	10–49	Empirical estimation of global sales data from four companies	Annual adoption rates	N.A. (data fitting of historical data with proposed model)	No	No	No	No	Proposed model incorporating forward-looking behavior has a good fit.
Kim, Srivastava, and Han (2001)	New product diffusion model incorporating both initial and repeat purchases and leapfrogging behavior for multigeneration products	No	No	4, 3	1,677	Survey data from 129 consumers	Purchase history	Consumer attitudes toward future generations	No	No	No	No	Developed a purchase timing and generation choice model for initial and repeat purchases of multigeneration products using an individual-level approach.
Kim, Jee, and Sohn (2021)	Comparison of sequential (fixed interval) new generation launch strategy and relatively uncertain quality with intermittent launch strategy with higher quality	No	Yes (1)	3	N.A.	Data fitting and simulation scenarios	Profits	Intergenerational launch time and quality	No	No	No	No	Optimal scenario balances the benefits of innovativeness and the costs of quality failures.
Shi and Chumnumpan (2014)	New product diffusion model incorporating coexistence of brand competition and generation substitution	Yes (1)	No	2	827	Empirical estimation of sales data from three companies	Annual adoption rates	N.A. (data fitting of historical data with proposed model)	No	No	No	No	Proposed model incorporating brand competition and generation substitution has good fit.
Sood, Kumar, and Gupta (forthcoming)	Modeling the Antecedents of Multi-Generational Services Adoption Behavior of Clients Across Countries: The Role of Mindset Metrics	Yes (6)	Yes (7)	5	58476	Arellano–Bond GMM model	Multigenerational depth (MGD), MGFs, MGT	Client satisfaction, client sentiment	No	No	Yes	Yes	Marketing mindset metrics and adoption metrics, moderated by cultural and economic factors influence adoption patterns.
This study	Consequences of Multigenerational Services Adoption Behavior: Global Client Engagement	Yes (6)	Yes (7)	4	52,360	Seemingly unrelated regression	CUR, successful client engagement (client references), and client feedback	MGD, MGFs, and MGT	Yes	Yes	Yes	Yes	Each MGS adoption metric differentially influences CUR. Controlling for firmographics, culture differentially influences the effect of CUR on client references and client feedback.

Notes: GMM = generalized method of moments.

Third, we extend the literature in this field by examining the impact of the adoption decisions on subsequent firm engagement with its clients. Thus, in contrast with many studies in this field that focus on modeling the diffusion process, we explore the consequences of MGS adoption behavior (MGD, MGFs, and MGT) on global client engagement and the various factors that influence these relationships. To increase customer loyalty and obtain higher profitability, firms pursue strategies to transform transactional customer behavior to build better relationships through customer engagement (Reinartz and Kumar 2000, 2003; Van Doorn et al. 2010). A large stream of literature documents that customers represent an asset to organizations and use customer lifetime value as a marketing metric (e.g., Gupta, Lehmann, and Stuart 2004; Hogan, Lemon, and Rust 2002; Rust, Lemon, and Zeithaml 2004; Venkatesan and Kumar 2004). Kumar et al. (2010) show that customer lifetime value underestimates the value of customers to a firm as they also contribute value in the form of referrals and feedback. We explore this value across clients in the B2B space driven by their differences in MGS adoption patterns.

Thus, in this study, we uniquely explore the consequences of analyzing MGS adoption behavior. We focus on two research perspectives to connect MGS adoption behavior with client engagement. First, we aim to extend the research on radical and incremental innovations by including multigenerational technological innovations comprising both products and services (Schilling 2008). We develop dimensions suitable for investigating and managing MGS adoption behavior. In doing so, we generate insights into how MGS adoption metrics affect direct and indirect engagement. Second, using empirical data on the three MGS adoption metrics and client engagement, we aim to quantify the impact of a one-unit improvement in an adoption metric on client engagement, thus allowing for better control of marketing efforts over adoption behavior. In the next section, we discuss our conceptual framework and the relevant propositions to address our research questions.

Concepts, Framework, and Propositions

We link MGS adoption and client engagement in the B2B context using a conceptual framework. Our objective is to examine how observed differences in MGS adoption behavior can explain firm–client engagement in global B2B markets. Before doing so, we first discuss the literature on the global diffusion of MGS and customer engagement and then explain the relationship between the MGS adoption metrics and client engagement (direct and indirect). We propose that the number of failures and cultural differences across countries moderate the relationship between MGS adoption behavior and CUR. Cultural differences across countries also influence the effect of client usage on client references and feedback.

Global Diffusion of MGS

Global diffusion refers to the diffusion of products across different countries. Firms commonly have products with high adoption rates in some countries while experiencing low adoption rates in others. Factors both within (e.g., product characteristics, marketing-mix variables) and out of (e.g., cultural, economic, geographic, legal, and political environments) firms’ control influence the diffusion of products across countries. The diffusion of innovations theory, which was developed in the 1960s (Bass 1969; Frank, Massy, and Morrison 1964), is useful in explaining the impact of consumer-level factors such as the flow of information, ideas, practices, products, and services across a wide context of cultures, markets, and segments on the diffusion process. The theory posits that consumers have different propensities toward adopting new products and that innovations are first adopted by a few innovators, who in turn influence others to adopt it (Kumar and Krishnan 2002). The number of initial innovators and the interaction process between the innovators and the later adopters (also called imitators) thus explain the shape of the diffusion process over time. The classic Bass (1969) model integrates these internal and external factors and models the diffusion rate as a function of consumers who have bought the product and those who are yet to buy. Similarly, the diffusion of innovation theory provides insights into product-related attributes that can significantly affect the rate of diffusion or adoption (Rogers 1983). This theory posits that the relative advantage of the new product or generation, the compatibility with existing usage and behavior patterns, the perceived complexity, divisibility (trialability), the communicability of new benefits, and the perceived risk all influence new product adoption decisions (Rogers 1983). This theory derives from roots in sociology (Rogers 1983), cultural anthropology (Barnett 1953), and industrial economics (Mansfield 1961). Prior research on new product diffusion sheds light on the global diffusion of new products and services (for reviews, see Chandrasekaran and Tellis 2017; Meade and Islam 2006; Muller, Peres, and Mahajan 2007). Dekimpe, Parker, and Sarvary (2000) summarize certain empirical generalizations drawn from various studies.

Over the years, research focus has expanded in line with changes in product-service markets. Later studies have also examined the interactions of innovators and adopters by exploring social contagion, including WOM communications, signals, and network externalities (Van den Bulte 2000, 2004; Van den Bulte and Stremersch 2006). A stronger need to emphasize managerial diagnostics and change the unit of analyses from an aggregate category level to disaggregate levels of firms, individuals, or brands is also more evident. This trend is in line with our discussions with managers of firms introducing MGS who are interested in understanding the phenomenon of client adoption behavior of continuous innovations in interactional markets and its impact on firm–client engagement. We draw from the literature to develop arguments for the proposed relationships in this study. Figure 1 shows the varying innovation adoption patterns of the global client base of a firm that offers MGSs in B2B markets and its linkage with client engagement. To encourage the timely adoption of products with multiple generations, prior research recommends that firms’ marketing strategies account for consumer demand and any market strategic and cultural factors that influence clients’ adoption and substitution decisions (Norton and Bass 1987). Firms respond to client queries by adjusting marketing visits and calls for new generations (Mahajan, Muller, and Wind 2000). Each MGS adoption metric is affected by the unique characteristics of B2B markets. For example, clients differ in their openness to explore and use more product features within an innovation generation, and these differences may be driven by clients' requirements to serve their customers, leading to differences in MGFs and MGT across clients. As a result, they differ in their overall direct and indirect engagement with the firm.

Figure 1.

Conceptual Framework Linking MGS Adoption Behavior to Client Engagement.

Customer Engagement

Beginning with an initial focus on customer buying behavior process models in the 1960s and 1970s, research on customer experiences expanded to an exploration of the impact of customer satisfaction, loyalty, service quality, and relationship marketing (1970s–1990s) (Lemon and Verhoef 2016). More recent research has focused on customer relationship management and the roles of customer centricity and focus, both of which help firms design and manage customer experiences (2000s–2010s). The concept of customer engagement has appeared in the customer experience research literature since the 2000s and has attracted hundreds of research articles and books. This emerging body of literature on consumer engagement is soundly based on the foundations of multiple disciplines—psychology, management, sociology, and marketing. We draw from this literature to conceptualize customer engagement as cognitive, affective, and behavioral engagement between current and potential internal and external customers in multiple channel and touchpoint offline and online environments across industries and sectors (see also Brodie et al. 2011; Brodie et al. 2013; Harmeling et al. 2017; Hollebeek 2011; Hollebeek, Conduit, and Brodie 2016; Kumar et al. 2010; Lim and Rasul 2022; Roy et al. 2018; Van Doorn et al. 2010; Vivek et al. 2014).

Not only does the customer engagement construct capture the key value that existing and new customers bring to a firm by purchasing its products and services (Gupta, Lehmann, and Stuart 2004), but it is also a unifying construct that goes beyond purchase behavior to include customer recommendations and referrals (Jin and Su 2009; Ryu and Feick 2007; Senecal and Nantel 2004), cross-buying (Anderson and Sullivan 1993; Bolton 1998; Mittal and Kamakura 2001; Zeithaml, Berry, and Parasuraman 1996), and feedback and knowledge (Kumar 2018; Venkatesan 2017; Venkatesan, Kumar, and Reinartz 2022). The firm–client interactions associated with client revenue are commonly referred to as “direct engagement” and the firm–client interactions associated with references/feedback as “indirect engagement” (Kumar 2013). We now consider how MGS adoption behavior influences CURs and the impact of failures and cultural differences on this relationship.

Impact of MGS Adoption Behavior on CUR

MGD and CURs

The adoption of multiple innovation generations is due to various reasons. For example, a higher intrinsic need for functionality can lead to regular adoption and increased usage. Clients may be driven by their dependence on the firm's new generations to acquire new customers or to retain existing customers (Aaker and Jacobson 1994). Adopting the latest innovation generations may be critical for clients to target diverse customers. A client's familiarity and expertise with the firm's products and services will also reduce its dependence on the firm for technical support. Therefore, greater MGD will lead to higher CURs for the firm. Over time, clients’ needs may not increase with adoption because of the saturation of demand from the clients’ customers. Clients may also continue to adopt the later generations under the expectation of better quality, given the low switching costs without an increase in use. In such cases, greater MGD may decrease usage rates, driving down CURs. Thus, we propose the following:

P_1a: An increase in MGD is positively associated with an increase in CURs, but at a decreasing rate.

MGFs and CURs

As clients may use a larger variety of product features driven by higher needs, their dependence on the firm and its offerings will increase (Kumar and Pansari 2016). Greater dependence on a single source reduces the variability of revenues over time (Srivastava, Shervani, and Fahey 1998). Multiple features within an innovation generation may increase opportunities for clients to target additional customers, further expanding CURs. The more customers demand select innovative features, the higher are the opportunities for the client to acquire new customers and retain existing customers. Finally, with higher adoption of MGFs, clients can increase their overall usage because they have more product knowledge and lower dependence on the firm's technical support (Tsai 2017). However, a firm may not observe high usage by promoting the adoption of features beyond the key features, because the adoption of innovative niche features in the same generation may not positively affect CURs in the same way as adopting key features of a new generation given lower overall demand for such features. Demand saturation and lower marginal utility from these niche and fringe features reduce usage by the client. Thus, we propose the following:

P_1b: An increase in MGFs is positively associated with an increase in CURs, but at a decreasing rate.

MGT and CURs

If clients do not expect the new generation to serve their absolute needs or enhance their ability to offer solutions to their customers, they may delay the adoption decision, thereby increasing the MGT. Such clients may reduce usage and exhibit greater switching behavior. A slower adoption process due to clients’ leapfrogging behavior reduces the firm's opportunities to recover new product development and launch costs (Jiang and Jain 2012) and increases client acquisition costs (Kumar 2018). The product support offered by the firm for older innovation generations also diminishes after the new generation is launched, as the firm will often focus on trying to sell the newer generation, thereby hurting the relationship between MGT and client usage. Thus, we propose the following:

P_1c: An increase in MGT is negatively associated with an increase in CURs, but at a decreasing rate.

Moderating Influence of Number of Failures

New generations of products and services expand the limits of technology and the functionality of current generations. However, despite many checks and balances by the seller firm, a new generation may not perform as expected in some respects. Sometimes, failure may occur because firms rush to the market without adequate testing because of competitive pressures to be the first to market and obtain first-mover advantage. In some cases, failure may occur because of a lack of sufficient understanding of clients' needs, resulting in a mismatch of the functionality of the new generation with the target applications. In still other cases, failure may occur because the client did not fully understand the functionalities of the new generation. Regardless of the cause, a significant consequence of failure is lower CURs (Kumar, Bhagwat, and Zhang 2015). Thus, although greater MGD has a positively impact on CURs, failure may negatively influence the relationship (Moorman, Zaltman, and Deshpandé 1992). Thus:

P_2a: Service failures mitigate the diminishing positive effect of MGD on CURs.

Undesirable interruptions in clients' experiences with the new generation may further impede their exploration of the new features. When clients experience failures, they feel more apprehensive about the other unexplored products within that generation, thus exhibiting lower usage, even if they adopted higher MGFs. If the failures are observed by clients’ customers, the impact on CURs may be even stronger. Failures make clients feel less invested in the firm and its offerings and reduce their trust in and dependence on the firm (Morgan and Hunt 1994) and its offerings. As failure in one feature can hurt the usage of other features, to avoid any inconvenience for their customers, clients may not offer this variety of products to them because of poor reliability. Thus:

P_2b: Service failures mitigate the diminishing positive effect of MGFs on CURs.

Although a longer MGT can signal that the requirements of clients are fully met, leading to lower CURs, failures can further worsen the negative impact of later adoption on revenues. Failures suggest a lack of commitment, unreliability, inconvenience, and lack of fulfillment on the part of the firm. Managers who make decisions about new adoption may engage in WOM with peers in the industry. Prior research suggests that the products that are more in the news for any reason receive more WOM immediately and over time (Berger and Schwartz 2011). Consumers also engage in more WOM when they have something valuable to share. Service failures make for relevant and interesting conversations and spur WOM (Berger and Schwartz 2011). Negative feedback on the service innovation or recent updates on service failures may lead potential customers to delay adoption time; it may also cause current customers to reduce usage out of fear of failure. Thus, service failures may also hurt the adoption decisions of other potential clients and increase the MGT for existing clients. Our interviews also revealed that negative experiences outweigh positive ones in long-term B2B relationships. Moreover, the impact of failures also lasts longer than satisfactory product performance, leading clients to question their past adoption decisions and explore new options. Thus:

P_2c: Service failures worsen the diminishing negative effect of MGT on CURs.

Moderating Influence of Cultural Differences

Prior research has examined the impact of macro-level, country-specific differences on new product diffusion (e.g., Gatignon, Eliashberg, and Robertson 1989; Sood and Kumar 2018). However, although studies suggest that cultural differences across countries affect marketing outcomes, findings on the impact of specific cultural variables on different metrics of innovation diffusion are mixed (Dwyer, Mesak, and Hsu 2005; Helsen, Jedidi, and DeSarbo 1993; Kumar, Ganesh, and Echambadi 1998). For example, Van den Bulte and Stremersch (2004) conduct a meta-analysis highlighting the limitations of standard neutral diffusion models in capturing the underlying social contagion. Van den Bulte (2000) highlights the scant evidence of diffusion acceleration (MGT) in the literature. Research on the impact of culture on MGD, MGFs, and MGT is also scant. Cultural differences may influence the CURs of a firm that sells to clients worldwide, depending on the needs of the clients offering their services to their customers. Hofstede, Hofstede, and Minkov (2010) describe cultural differences across countries through six dimensions. We examined each dimension and its impact on the relationship between adoption metrics and client engagement and retained those relevant for the current study.

Indulgence versus restraint (IVR) is the extent to which a society allows easier gratification (Hofstede, Hofstede, and Minkov 2010). This dimension may influence a client's exploration of new functionalities in an affirmative manner. Indulgence is about consuming good things in life, while restraint is about curbing consumption and gratification. The indulgence dimension exerts a significant influence on the society-level consumption of new offerings in the presence of already-existing offerings. People in an indulgent society tend to have less control over their desires and engage in the gratification of basic and natural human desires related to enjoying life and having fun. In the B2B context, if a firm operates in an indulgent country, its customers may tend to demand new offerings frequently. B2B markets may vary in their ability to either constrain or indulge firms in enjoying various experiences due to such intrinsic cultural differences (Maleki and De Jong 2014). Because indulgent cultures encourage the expression of freedom and happiness, customers may be open to faster adoption of new functionalities to improve their consumption experiences. A culture high on the indulgence dimension may exhibit more willingness to accept the risks inherent in new product generations. Thus, we propose the following:

P_3a: The cultural dimension of IVR enhances the diminishing positive effect of MGD on CURs.

The preference for increasing consumption may also drive the exploration of new features and benefits in the new generation. As a result, we expect a stronger relationship between MGFs and CURs for clients from cultures with higher IVR. Such cultures have a greater proclivity for instant gratification and will be positively inclined to explore new features in a new product generation (Heydari et al. 2021). That is, firms in such cultures are more willing to accept the inherent risks with new features introduced in each product generation. Thus:

P_3b: The cultural dimension of IVR enhances the diminishing positive effect of MGFs on CURs.

Despite the lower attraction of the new product generation due to a lower fit with needs, resistance to update adopted innovation generations, or an unwillingness to learn new skills, clients from higher-IVR cultures may still want to engage in exploratory behavior (Griffith and Rubera 2014). Higher-IVR cultures have a greater tendency to engage in the exploration of new ideas and experiences. This intrinsic openness to consider new functionalities and features tends to reduce the MGT. Thus:

P_3c: The cultural dimension of IVR mitigates the diminishing negative effect of MGT on CURs.

Clients rarely make decisions on the depth, features, and timing of adoption in isolation. The interdependence of adoption metrics influences the usage. For example, on the one hand, clients that adopt multiple generations may have a shorter MGT than clients that adopt a few innovation generations. Similarly, regular adoption of newer innovation generations also enhances clients’ familiarity with, relationship with, and confidence in the firm, making them more likely to adopt new features and thereby influencing MGFs. On the other hand, interdependence can also hurt firms if high MGD or MGFs lead to lower client usage. At the same time, indulgence, can positively influence MGS adoption and client usage relationships. However, given many possible combinations of the MGS adoption metrics, predicting the exact direction of the relationship on a purely theoretical basis is difficult, and an empirical approach is necessary. Therefore, we test for the effects of all possible interactions of the adoption metrics on CUR.

Impact of CURs on References and Feedback

The adoption decision of a firm in a B2B context is often dependent on the choices of other firms in its value chain. Sivadas and Dwyer (2000) show that interfirm cooperation hinges on clients’ ability to trust the firm, communicate with it, and coordinate their decisions with it. More interdependence between a client and other firms in its value chain may spur a client to encourage its contacts to adopt and use similar features, as the use of similar technologies and features across firms in a value chain increases the quality of communications and can lead to greater productivity. Therefore, firms in B2B environments with higher CURs may have more incentives to create more client references and engage in secondary activities that indirectly benefit the firm. This behavior is in line with consumers’ engagement with a firm in B2C markets, in which both direct expressions of client engagement, such as current and future transactions with the firm, and indirect expressions of client engagement, such as WOM, customer cocreation, and return behavior, are common (Garbarino and Johnson 1999; Kumar et al. 2010; Van Doorn et al. 2010). Clients may share success stories and testimonials with the selling firm. In B2B markets, such behavior is elevated because of the interdependence among a firm, its clients, and its clients' customers (Kumar, Petersen, and Leone 2013).

Thus, higher levels of client usage, possibly driven by positive service experience, may correlate with positive reference behavior. However, the number of prospects any client can meaningfully influence in terms of their purchase decision declines in the later stages (Liu 2006). In addition, a client's excitement may decrease over time, reducing WOM activity (Berger and Schwartz 2011). Thus, we propose the following:

P_4a: An increase in CURs positively affects client references, but at a decreasing rate.

We consider the impact of three main cultural dimensions—individualism, power distance, and masculinity—on the relationship between CURs and client references (Hofstede, Hofstede, and Minkov 2010) for several reasons. First, the cultural dimension of individualism³ will influence the correlation between CURs and client references. This cultural dimension reflects the preference for cohesiveness and ties within a social group. Individualism explains the extent to which people feel independent, self-reliant, and distant from others rather than being interdependent and closely tied to others (collectivism) (Allik and Realo 2004). High-individualism cultures have weaker ties and an expectation that everyone should take care of themselves, and thus decisions are made primarily individually. Therefore, managers from high-individualism cultures generally restrict any display of emotions, are low on communication intimacy, and provide fewer WOM references due to a greater need for consensus-driven decision making (Gudykunst, Yoon, and Nishida 1987; Money, Gilly, and Graham 1998). As such, the diminishing positive effect of CURs on client references is mitigated in high-individualism cultures.

Second, we expect the cultural dimension of power distance⁴ to influence the correlation between CURs and client references. This cultural dimension reflects the extent to which inequalities between more and less powerful members of society are deemed acceptable (Hofstede 1991). Centralized decision structures reduce the usage of firm-incentivized reference programs, as managers take their cues from senior management. In high-power-distance cultures, an existing hierarchical and normative structure maintains inequalities among people. In such cultures, people tend to accept exclusive tasks and exhibit behaviors that make them appear powerful in the system and structure (Hofstede, Hofstede, and Minkov 2010). High-power-distance cultures have greater in-group WOM behavior (Lam, Lee, and Mizerski 2009), and managers in such cultures are influenced more by references from experts than peers (Pornpitakpan and Francis 2001). Expertise conveys superior knowledge, skill, and experience, which confers status in high-power-distance cultures (Samaha, Beck, and Palmatier 2014). In decision making, managers tend to value and rely more on people with higher status (Hofstede, Hofstede, and Minkov 2010). Thus, high-power-distance cultures mitigate the diminishing positive effect of CURs on client references.

Third, we expect the cultural dimension of masculinity⁵ to influence the correlation between CURs and client references. This cultural dimension reflects the general value a culture puts on assertiveness, aggressiveness, and competitiveness. Countries with a higher level of masculinity put more value on such outcomes, whereas countries with lower levels of masculinity put more value on relationship reciprocity, benevolence, nurturing, and compromise. In a masculine culture, clients may exhibit higher competitiveness and achievement drives to promote firm-incentivized client reference programs to influence potential customers and increase firm productivity (Hofstede, Hofstede, and Minkov 2010). Clients may provide references to other noncompeting firms in the same industry, as they benefit from the incentives. Moreover, clients are incentivized to accelerate the standardization of adopted innovation generations across their supply chain (e.g., clients and their customers) to enhance overall productivity and lower transaction costs. Thus, higher levels of masculinity enhance the diminishing positive effect of CURs on client references. In line with these arguments, we propose the following:

P_4b: The diminishing positive effect of CURs on client references is (i) mitigated by higher individualism, (ii) mitigated by higher power distance, and (iii) enhanced by higher levels of masculinity.

Prior research on new product development suggests that the amount of client feedback is higher when it is closer to product launch. Moreover, clients work with firms on two key objectives: to increase their efficiency and to decrease costs (Kumar 2018). Clients with more experience due to their adoption and usage of multiple generations of services are more equipped to codevelop new features and generations with the firm. Such clients are also better able to help the firm sell the products to new prospects by offering their perspectives as a user. The firm's dependence on client feedback after launch is high because new products usually have a high failure rate (Castellion and Markham 2013). Similarly, clients that may not have adopted many generations but have extensively used many features of a single generation are also equipped to help the firm with feedback and can work with the firm to increase their efficiency, decrease their costs, and contribute to new product development (Kumar 2018).

Clients with high usage may provide inputs as codevelopers, as usage dependency helps provide inputs of customized products and features. (Fang 2008). Client feedback can lead to an early resolution of complaints, lower product returns, and higher purchase and usage rates. Therefore, clients in B2B environments with higher usage revenues will have more incentives to engage with the firm and provide feedback. However, the need for and volume of client feedback decrease over time because the firm can better understand its client's needs and enhance its technical skill set. As such, the firm will have a lesser need for client feedback. Finally, lower involvement may also reduce the likelihood that clients identify new ideas worth sharing with the firm (Beatty, Kahle, and Homer 1988).

Furthermore, the slower speed of the adoption of successive generations reduces the likelihood that clients will observe product attributes or performance characteristics that have not already been made public by other clients that have adopted the product. In the absence of something new to contribute, the motivation to provide feedback also declines (Pink 2009). Thus, we propose the following:

P_5a: An increase in CURs positively affects client feedback, but at a decreasing rate.

We expect individualism to influence the relationship between CURs and client feedback. If the individual and group distinction is not blurred, clients will wait for group consensus to interact with and provide feedback to the seller firm. The flow of ideas between the client and the firm is less open and restrained in high-individualism cultures. Thus, the diminishing positive effect of CURs on client feedback is mitigated by higher levels of individualism.

Furthermore, subordinates tend to be separated from their superiors in countries with higher power distance. Reporting structures and deference to higher positions limit managers’ opportunity or ability to provide frequent or valuable feedback to the firm. Therefore, higher power distance mitigates the diminishing positive effect of CURs on client feedback. Finally, countries with higher levels of masculinity, thus exhibiting higher goal orientation and self-focus, hamper the frequency of clients’ external feedback to the firm, thus mitigating the diminishing positive effect of CURs on client feedback. In line with these arguments, we propose the following:

P_5b: The diminishing positive effect of CURs on client feedback is (i) mitigated by higher individualism, (ii) mitigated by higher power distance, and (iii) mitigated by higher levels of masculinity.

We also explored the remaining Hofstede dimensions but did not find strong conceptual arguments in the literature to expect any effect of these dimensions. The uncertainty avoidance dimension captures the extent to which a society is tolerant of uncertainty and ambiguity. More than risk avoidance, this dimension explains society's propensity to deal with anxiety and distrust in the face of the unknown. People from such a society desire to know the truth; this dimension does not have an influence on consumption, relational ties, or WOM spread and therefore does not fit our study context. The long-term/short-term orientation dimension explains the extent to which a society is ready for the future. Long-term-oriented societies believe that the world is in flux and that one should always be ready for the future; conversely, a short-term orientation uses the past as a moral compass, with society following accordingly (Hofstede and Minkov 2010). This dimension is directed more by life philosophies and educational values, so it also does not fit our study’s framework.

Research Methodology

Data and Measures

We complemented the literature review with empirical data to investigate our topic of interest. We worked with a seller firm to carry out the interviews in an unstructured manner and interviewed managers about the association between MGS adoption decisions and clients’ engagement with the firm. These interviews provided qualitative information on issues managers face in sales of MGSs and their experiences based on feedback and interactions with the clients. The need for further exploration came from our managerial interviews. The seller firm shared the adoption and usage data across four innovation generations for its cloud-based products and services from clients based in multiple countries over many years. The firm pioneered and assimilated innovations in diverse but related fields of AI, blockchain, Internet of Things, and enhanced security, integrating a variety of data types (structured and unstructured) and enabling real-time analytics, text mining, deep learning, and machine learning apps within its hybrid clouds to increase value for clients. Each innovation generation has different variants of cloud-based services for servers, storage, backup, enterprise, desktop services, and AI-based insights for medium-sized to large firms that clients can independently subscribe to or adopt. We asked six managers from the seller firms to share their experiences in marketing multiple generations across different countries. We also obtained at least one manager from each of the 42 randomly selected client firms to share information on their MGS adoption decisions and the associated engagement with the seller firm, which resulted in 68 interviews from the client firms.

The adoption data shared by the firm suggest significant variation in MGD, MGFs, and MGT (see Web Appendix A, Figures W2a–2c). Some clients adopt each innovation generation and are relatively more predictable in all three metrics. These clients seem to maintain a similar adoption time relative to the launch of a new generation and thus stay in the same adopter segment across generations given the timing of their adoption decisions. For example, a client may upgrade to the new generation soon after launch and act as an adopter in the innovator's segment for each generation. These clients have stronger relationships with the firm and higher levels of usage, references, and feedback. However, if usage within an innovation generation is high but upgrade decisions of clients are not aligned with the launch schedule of the MGS supplier, we may observe greater variation in MGD and MGT but greater consistency in MGFs. Such clients exhibit more resistance to MGS adoption and delay the adoption decision or selectively leapfrog over successive innovation generations depending on their needs or usage behavior. They may exhibit adoption behavior like the “innovator” adopter segment in one generation but the “early majority” adopter segment in the next, and so on. These clients have moderate relationships with the firm and inconsistent levels of usage, references, and feedback. We also observe clients with consistently low levels of MGD and MGFs but high MGT. Such clients exhibit the most resistance to adoption; they may not adopt the first few innovation generations, may wait for the technology to mature, or may show evidence of leapfrogging behavior. They thus fall into the “late majority” adopter segment. Within each innovation generation, their adoption of various product variants may also be low. These variations in the attitudinal and behavioral characteristics of clients require different levels of marketing visits and calls by the seller firm. These clients have weak relationships with the firm and have low levels of usage, references, and feedback.

However, some clients experienced more or less failures given the nature of the product and services in the nascent stage of the product life cycle (see Web Appendix A. Figure W2d). The clients are also spread across countries and operate in different economic and cultural environments. These cultural and economic conditions across countries may also moderate the effect of the variation in adoption decisions on client engagement. As such, we need to control for marketing efforts, which also affect client engagement. Thus, understanding the factors influencing the relationship between the MGS adoption metrics and the client engagement client usage revenue would help the firm improve its marketing strategy and increase returns to investment.

We obtained data from a large global information technology (IT) firm that serves business clients and offers a broad assortment of software, hardware, and cloud-based products and services. Our data include client- and firm-level records across four innovation generations for cloud-based products and services from 13 countries (7 developed and 6 emerging) from April 2008 to July 2016.⁶ The countries are Argentina, Australia, Brazil, Canada, China, Colombia, France, Germany, India, Mexico, New Zealand, the United Kingdom, and the United States. The number of employees in the client's organization varies from 100 to 1,000 (midmarket segment), and the annual revenues are from $25 million to $960 million, with some exceptions. We obtain data on 52,360 clients across four innovation generations.

A research team led by one of the coauthors conducted unstructured in-depth interviews with managers from the six large global IT multinational enterprises in the high-tech industry serving business clients on a broad range of software, hardware, and cloud-based IT services. The team interviewed 42 managers from developed markets (4 France, 8 Germany, 4 United Kingdom, and 26 United States) over a 48-day period. In addition, we interviewed 26 managers from emerging markets (4 Argentina, 4 Brazil, 6 China, 6 India, and 6 Mexico) over a 32-day period.

We obtained and organized the following three types of data in a standard panel structure (see Table 2):

Client technological adoption data: We acquired data on the timing of adoption of a new MGS. The firm usually follows a set launch schedule to introduce a new MGS every two years. It also stops selling the previous generation as soon as the new one is available. MGD measures the cumulative number of innovation generations adopted. MGFs measure the number of products and services a client adopts within a generation. MGT measures the average adoption time across all generations, including clients that adopt for the first time, upgrade updates from the prior generation, or leapfrog from older generations. For example, some clients wait to adopt and subsequently leapfrog to the latest generation (e.g., from generation 1 to generation 3). For first-time adopters, we compute MGT as the time since the launch of the adopted innovation generation. For the remaining two types of adopters, MGT is the time since their last adoption. Because the firm encourages regular upgrades by halting marketing and sales of the prior generation upon the launch of the new generation, this measure does not suffer from censoring bias. We use the average MGT across all adopted generations for each client in the model.

Client technological usage data: We also obtained data on the usage of MGSs and the incidences of failures for each client in each quarter. We collected data on CUR (in thousands of dollars), incidences of quarterly client references by a client, and incidences of quarterly client feedback.

Firm marketing data and controls: We obtained quarterly data on all firm-initiated marketing visits and marketing calls for both cloud-based and non-cloud-based products and services. Client characteristics such as the number of employees and the industry type served as control variables. We also collected data on Hofstede's cultural factors for the countries in our sample.

Table 2.

Operational Measures of Variables.

Variable	Definition	M	SD
MGD	Cumulative number of innovation generations adopted by a client	2.98	1.00
MGFs	Number of product or service features adopted by a client within an innovation generation	3.88	1.35
MGT	Average number of quarters between client adoptions of innovation generations	8.76	1.85
FAILURE	Incidences of product or service failure for a client (Kumar, Bhagwat, and Zhang 2015)	3.09	1.12
EMPL	Average number of employees of a client in the period preceding 4 quarters	596.59	233.33
SREV	Average sales revenue of a client (in millions of dollars) in a period preceding 4 quarters	256.07	15.60
MCALL	Average quarterly number of marketing calls for cloud products over preceding 4 quarters	8.78	2.54
MVISIT	Average quarterly number of marketing visits for cloud products over preceding 4 quarters	4.27	1.79
ECON	Gross domestic product per capita, purchasing power parity ($) in a period obtained from the World Bank	24,218	14,275
CUR	CUR (in thousands of dollars) in a period (Gupta, Lehmann, and Stuart 2004)	117.86	6.29
Successful client references	Incidences of quarterly SCRs of firm's products by a client. (Jin and Su 2009; Ryu and Feick 2007; Senecal and Nantel 2004)	3.67	.20
Valuable client feedback	Incidences of quarterly client feedback (Kumar 2018; Venkatesan 2017; Venkatesan, Kumar, and Reinartz 2022)	23.85	1.14
Relative benefit score	Relative benefit score assigned by the firm in a period (Cannon and Homburg 2001)	49.81	15.41
Client-initiated contacts	Quarterly number of client-initiated contacts for new generation (Ganesan 1994)	7.33	3.35
IVR	Hofstede’s measure of culture—indulgence vs. constraint (Hofstede, Hofstede, and Minkov 2010)	60.9	21.4
Individualism	Hofstede’s measure of culture—individualism vs. collectivism (Hofstede, Hofstede, and Minkov 2010)	58.6	27.6
Power distance	Hofstede’s measure of culture—power distance (Hofstede, Hofstede, and Minkov 2010)	53.7	20.7
Masculinity	Hofstede’s measure of culture—masculinity vs. feminism (Hofstede, Hofstede, and Minkov 2010)	59.1	7.7

Notes: All means and SDs are over the preceding four quarters.

Model Specification and Estimation

We model the impact of a client's MGS adoption behavior on its direct engagement (usage). MGS adoption behavior influences usage, encouraging client references and feedback (indirect engagement). We model a client's usage as a function of adoption metrics and their interactions, failures (FAILURE), the interaction of adoption metrics with failure, and marketing efforts. As our primary interest is in tying the MGS adoption metrics to CUR, we need to control for the variation in marketing efforts across clients on the observed adoption behavior. We include log transformations of the adoption metrics to account for potential nonlinear effects on client direct engagement behavior. We account for the heterogeneity across clients by including sales revenue and the number of employees in the client firms, in addition to a random-effects specification. We correct for potential endogeneity of adoption decisions by incorporating the residuals from the endogeneity correction Equations B1a–1c (for details, see Web Appendix B). We also account for the potential endogeneity of marketing visits and calls by incorporating the residuals from the endogeneity correction Equations B1d and B1e. Finally, we add dummy variables for industry type (INDTYP) and country (COUNT). The interaction terms with the moderators test for the potential moderating roles of failures and cultural differences.

\begin{aligned} \ln (1 + CU R_{ijt}) = & α_{0_{ij}} + α_{1} \ln (1 + MG D_{ijt}) + α_{2} \ln (1 + MG F_{ijt}) + α_{3} \ln (1 + MG T_{ijt}) + α_{4} FAILUR E_{ijt - 1} \\ + α_{5} \ln (1 + MG D_{ijt}) \times FAILUR E_{ijt} + α_{6} \ln (1 + MG F_{ijt}) \times FAILUR E_{ijt} + α_{7} \ln (1 + MG T_{ijt}) \times FAILUR E_{ijt} \\ + α_{8} IV R_{j} + α_{9} \ln (1 + MG D_{ijt}) \times IV R_{j} + α_{10} \ln (1 + MG F_{ijt}) \times IV R_{j} + α_{11} \ln (1 + MG T_{ijt}) \times IV R_{j} \\ + α_{12} \ln (1 + MG D_{ijt}) \times \ln (1 + MG F_{ijt}) + α_{13} \ln (1 + MG D_{ijt}) \times \ln (1 + MG T_{ijt}) + α_{14} \ln (1 + MG F_{ijt}) \times \ln (1 + MG T_{ijt}) \\ + α_{15} \ln (1 + MG D_{ijt}) \times \ln (1 + MG F_{ijt}) \times \ln (1 + MG D_{ijt}) + α_{16} EMP L_{ijt} + α_{17} SRE V_{ijt} + α_{18} MVISI T_{ijt} \\ + α_{19} MCAL L_{ijt} + α_{20} ECO N_{jt} + α_{20} INDTY P_{ij} + α_{21} \sum_{j = 1}^{J - 1} COUN T_{j} + α_{22} CU R_{ijt - 1} + α_{23} ECMV R_{ijt} \\ + α_{24} ECMC R_{ijt} + α_{25} ECMG D_{ijt} + α_{26} ECMG F_{ijt} + α_{27} ECMG T_{ijt} + ε_{1 ijt}, \end{aligned}

(1)

where α represents parameters; α_0ij are the random effects; ε_1ijt is the error term; and ECMGD_ijt, ECMGF_ijt, ECMGT_ijt, ECMVRijt, and ECMCR_ijt are the endogeneity-corrected residuals (see Web Appendix B for details). Subscripts i, j, and t refer to client, country, and quarter, respectively; all other variables are as defined in Table 2.

Next, we model the impact of client usage on the two types of indirect engagement, controlling for various client characteristics. The seller firm periodically evaluates clients’ ability and motivation to provide new client references and assigns each client a relative benefit score. Thus, the relative benefit score reflects the voluntary response to help the seller firm acquire new clients in a period. Clients may initiate contact with the seller firm for various reasons, including to discuss unfulfilled needs, participate in the new product development process, or provide feedback to improve existing products (Cannon and Homburg 2001). The seller firm also monitors the number of client-initiated contacts each client introduces. Thus, client-initiated contacts reflects the bidirectional communication between a seller firm and a client firm in a period (Ganesan 1994).

We include the relative benefit score of each client as a driver of client references and the number of client-initiated contacts for the new generation as a driver of client feedback to satisfy the condition of having a unique variable in each equation and jointly estimate Equations 2 and 3 using panel data analysis. We also include the number of employees and the sales revenue of clients in a given year and industry and country dummies⁷ as control variables. We estimate Equations 2 and 3 jointly using seemingly unrelated regression:

\begin{aligned} \ln (1 + SC R_{ijt}) = & δ_{0_{ij}} + δ_{1} \ln (1 + SC R_{ijt - 1}) + δ_{2} CU R_{ijt} \\ + δ_{3} ID V_{j} + δ_{4} CU R_{ijt} \times ID V_{j} + δ_{5} PD I_{j} \\ + δ_{6} CU R_{ijt} \times PD I_{j} + δ_{7} MA S_{j} \\ + δ_{8} CU R_{ijt} \times MA S_{j} + δ_{9} EMP L_{ijt} \\ + δ_{10} SRE V_{ijt} + δ_{11} RB S_{ijt - 1} \\ + δ_{12} INDTY P_{ij} + δ_{13} \sum_{j = 1}^{J - 1} COUN T_{j} \\ + ε_{2 ijt}, \end{aligned}

(2)

\begin{aligned} \ln (1 + VC F_{ijt}) = & θ_{0_{ij}} + θ_{1} \ln (1 + VC F_{ijt - 1}) + θ_{2} CU R_{ijt} \\ + θ_{3} ID V_{j} + θ_{4} CU R_{ijt} \times ID V_{j} + θ_{5} PD I_{j} \\ + θ_{6} CU R_{ijt} \times PD I_{j} \\ + θ_{7} MA S_{j} + θ_{8} CU R_{ijt} \times MA S_{j} \\ + θ_{9} EMP L_{ijt} + θ_{10} SRE V_{ijt} + θ_{11} CI C_{ijt - 1} \\ + θ_{12} INDTY P_{ij} + θ_{13} \sum_{j = 1}^{J - 1} COUN T_{j} \\ + ε_{3 ijt}, \end{aligned}

(3)

where SCR_ijt is the number of successful client references by the client, VCF_ijt is the amount of valuable client feedback, RBS_ijt is the relative benefit score assigned by the firm to the client, and CIC_ijt is the number of client-initiated contacts for a new generation by the client. In addition, δ and θ represent parameters, δ_0ij, θ_0ij are the random effects, and ɛ_2ijt, and ɛ_3ijt are the error terms; all other variables are defined as in Table 2.

Model Results

We first present the descriptive statistics of the key constructs and model-free evidence. Table 2 and Web Appendix A (Figures W2 and W3 and Tables W1 and W2) present the operational measures, descriptive statistics, and plots of selected variables in our study. We then present the influence of adoption behavior on CUR and the influence of CUR on client references and client feedback, respectively. We explore the raw data on MGS adoption behavior and failure across clients.

We account for the potential endogeneity of the firm's marketing visits and calls to influence the current adoption decision and the clients’ past adoption decisions on the current decisions (see Web Appendix B). We first estimate the predicted value of each MGS adoption metric as a function of prior adoption decisions and prior usage revenue (see Web Appendix B, Equations B1a–1c). We correct for the potential endogeneity of marketing visits and calls by including information on marketing visits and calls for non-cloud-based products and services (see Web Appendix B, Equations B1d and B1e). We then estimate Equation 1 to investigate the impact of MGS adoption behavior on CUR subject to various levels of failures and cultural differences (see Table 3).

Table 3.

Parameter Estimates for Drivers of Client's Direct Engagement (CUR).

Variable	Propositions	Ln(1 + CUR_ijt)(Equation 1)
Variable	Propositions	Coef.	t-Value
Intercept		.481	8.43
Ln(1 + MGD_ijt)	P_1a	.382	8.56
Ln(1 + MGF_ijt)	P_1b	.438	6.58
Ln(1 + MGT_ijt)	P_1c	−.210	−4.63
FAILURE_ijt		−.069	−3.02
Ln(1 + MGD_ijt) × FAILURE_ijt	P_2a	−.028	−2.44
Ln(1 + MGF_ijt) × FAILURE_ijt	P_2b	−.034	−2.74
Ln(1 + MGT_ijt) × FAILURE_ijt	P_2c	−.008	−2.32
IVR_j		.350	−2.76
Ln(1 + MGD_ijt) × IVR_j	P_3a	.001	2.08
Ln(1 + MGF_ijt) × IVR_j	P_3b	.002	2.09
Ln(1 + MGT_ijt) × IVR_j	P_3c	.0015	2.17
Ln(1 + MGD_ijt) × Ln(1 + MGF_ijt)		.012	2.22
Ln(1 + MGD_ijt) × Ln(1 + MGT_ijt)		−3.17E-04	−2.41
Ln(1 + MGF_ijt) × Ln(1 + MGT_ijt)		−.004	−2.32
Ln(1 + MGD_ijt) × Ln(1 + MGF_ijt) × Ln(1 + MGT_ijt)		−.003	−2.42
EMPL_ijt		.0003	1.82
SREV_ijt		.0004	1.97
MCALL_ijt		.379	2.43
MVISIT_ijt		.247	2.19
ECON_jt		.002	2.22
CUR_ijt−1		.445	3.56
Adj. R²		.47
N		52,360

Notes: Boldface indicates p < .05. Models also include industry and country dummies.

The results provide support for P_1a and P_2a that service failures mitigate the diminishing positive effect of MGD on CUR (.382, p < .01), with both the main effect of product failure (−.069, p < .05) and the interaction effect of MGD and product failure (−.028, p < .05) being significant. The results also confirm P_1b and P_2b that service failures mitigate the diminishing positive effect of MGFs on CUR (.438, p < .01), with the main effect of product failure and the interaction effect of MGFs and product failure (−.034, p < .05) being significant. In addition, the results provide support for P_1c and P_2c that service failures worsen the increasing negative effect of MGT on CUR (−.210, p < .05), with both the main effect of product failure and the interaction effect of MGT and product failure (−.008, p < .05) being significant.

The results lend support to P_3a that cultural preferences for indulgence enhance the diminishing positive effect of MGD on CUR, with both the main effect (.350, p < .05) and the interaction effect of MGD and IVR (.001, p < .05) being significant. The results also confirm P_3b that IVR enhances the diminishing positive effect of MGFs on CUR, with the main and interaction effect of MGFs and IVR (.002, p < .05) being significant. In addition the results provide support for P_3c that IVR mitigates the increasing negative effect of MGT on CUR, with both the main effect and interaction effect of MGT and IVR (.0015, p < .05) being significant.

We find that CUR increases with both greater MGD and higher MGFs but at a decreasing rate (see Figure 2, Panels A and B). We find that CUR decreases with an increase in MGT but at a decreasing rate (see Figure 2, Panel C). We plot the relationship between MGS behavior and CUR by the mean value of failure and IVR in our sample and then simulate the relationship for two more scenarios: with twice and half the number of failures than the mean value. The results show that service failures mitigate and IVR enhances the relationships in Panels A–F of Figure 2.

Figure 2.

Impact of Failures and Culture (IVR) on the Relationship Between MGS Adoption Behavior and CUR.

We jointly estimate Equations 2–3 in STATA to investigate the impact of CUR on client references and client feedback (see Table 4). We included the unique variables of the relative benefit score of each client and the number of client-initiated contacts in Equations 2 and 3, respectively, to satisfy the requirements for a joint estimation. The results confirm P_4a that an increase in CUR is correlated with increases in client references, but at a decreasing rate (.005, p < .01). Clients with a higher relative benefit score (.002, p < .05) and higher client references in the previous quarter (.456, p < .01) provided higher client references. The results of Equation 3 suggest a strong impact of higher CUR (.010, p < .01) on client feedback. Clients with more client-initiated contacts (.450, p < .05) and more client feedback in the previous quarter (.582, p < .01) provided more client feedback. The results also provide support for P_{4b(i)−(iii)}, as the diminishing positive effect of CUR on client references is mitigated by individualism (−.00009, p < .05) and power distance (−.00007, p < .05) but enhanced by masculinity (.00006, p < .05).

Table 4.

Parameter Estimates for Drivers of Client's Indirect Engagement (Client References and Client Feedback).

Variable	Propositions	Ln(1 + SCR_ijt)(Equation 2)		Ln(1 + VCF_ijt)(Equation 3)
Variable	Propositions	Coef.	t-Value	Coef.	t-Value
Intercept		.047	2.32	.101	2.52
Ln(1 + SCR_ijt−1)		.4565	3.84
Ln(1 + VCF_ijt−1)				.582	3.93
CUR_ijt	P_4a; P_5a	.0051	3.47	.01	3.37
IDV_j		−.00150	2.88	−.007	2.74
CUR_ijt × IDV_j	P_4b(i); P_5b(i)	−.000009	2.21	−.00001	2.36
PDI_j		−.00135	2.76	−.016	2.65
CUR_ijt × PDI_j	P_4b(ii); P_5b(ii)	−.000007	2.57	−.00001	2.63
MAS_j		.00125	2.71	−.031	2.45
CUR_ijt × MAS_j	P_4b(iii); P_5b(iii)	.000006	2.32	−.00001	2.33
EMPL_{ijt − 1}		.0002585	1.99	.00004	1.85
SREV_ijt		.000244	1.88	.00009	1.89
RBS_ijt−1		.0025	2.13
CIC_{t − 1}				.45	2.24
Adj. R²		.57		.59
N		52,360		52,360

Notes: Boldface indicates p < .05. Models also include industry and country dummies. IDV = individualism; PDI = power distance; MAS = masculinity; RBS = relative benefit score, CIC = client-initiated contacts.

Moreover, the results confirm P_5a that an increase in CUR is correlated with increases in client feedback, but at a decreasing rate (.010, p < .05). Finally, the results provide support for P_{5b(i)–(iii)}, as the diminishing positive effect of CUR on client feedback is mitigated by individualism (−.00001, p < .05), power distance (−.0007, p < .05), and masculinity (−.000006, p < .05). Both the main effects and the interaction effects are significant.

Discussion

Continuously presenting clients with multiple generations of innovative products and services is the primary method by which firms in high-tech markets generate value for their clients. Likewise, clients can also bring value to the firm through direct purchases and by acting as references and providing valuable feedback. We contribute to the literature and provide implications for practice by linking these two aspects of value to and from clients in the context of multiple generations sold across countries.

Research Contributions

This study is the first to generate insights into the adoption of MGSs in B2B markets. Our empirical findings on the association between MGS adoption behavior and client engagement contribute to the innovation and customer engagement literature and offer new insights into engagement marketing. We explore how service failures and cultural differences moderate the relationships. Our intent was to understand the role of culture in the context of MGS adoption behavior related to client engagement, in an effort to explain which culture dimension plays a dominant role in the process. For example, the results show that MGD and MGFs are better linked with the CUR generated by offering innovative services in countries with indulgent cultures. This knowledge further integrates the international marketing and innovation domains, helping researchers explore the placement of innovative products internationally. This research opens up potential discussions on the client engagement process in international markets, more specifically in the B2B context. Our study, which draws from the theoretical principles of engagement literature and multigenerational innovation adoption literature, shows how a firm can enhance client engagement through repeated adoption of multigenerational technological innovation, higher usage and client references, and more valuable feedback.

Furthermore, this study adds insight into how MGS adoption can be understood by suggesting adoption metrics (i.e., MGD, MGFs, and MGT), particularly for technological products and services. This study shows how these metrics can assist firms in enhancing client engagement in various global markets. Such insights can help build theoretical and process frameworks to determine localization and standardization of firm-level strategic communication. Thus, this research extends the customer engagement concept, adds insights to innovation literature, and enriches international marketing literature.

Implications for Practice

This study has implications on how firms can (1) manage client engagement, (2) refine market entry strategy, (3) launch MGS, and (4) respond to failures, especially in an international market context. The study illustrates a methodology to link MGS adoption behavior with client engagement and explicates the drivers for managing successful client engagement. This research also shows an empirical relationship between MGD, MGFs, and MGT on direct engagement (CUR) and, through usage, on indirect engagement (client references and feedback). We use predicted values of CUR, coefficients from the models, and information on relevant variables to estimate the impact of MGS adoption metrics on client references and feedback. In our U.S. and Canada sample, on an average base of 3.67 client references per client, the adoption of one incremental innovation generation translates to an increase in client references by .25 per client. The adoption of an incremental product feature or an early adoption by one year translates to an increase in client references by .16 and .07 per client, respectively. Finally, on an average base of 23.85 client feedback per client, the adoption of one incremental innovation generation translates to an increase in client feedback by 1.47 per client. The adoption of an incremental product feature or an early adoption by one year translates to an increase in client feedback by .94 and .41 per client, respectively. The study showcases the role of culture as a moderator of various relationships in the proposed framework. Managers can use the study insights to manage client relationships across cultures, thereby generating better return on investments.

Second, with regard to refining firms’ market entry strategy, our results have implications for managing the sequence of MGS entry in multiple countries. Firms should work to target countries that are more collectivist to increase client references. To do so, they could use a sprinkler strategy to enter countries with high intelligence and more of a waterfall strategy in other countries. Similarly, they should expect differences in the efficacy of incentivized reference and feedback programs. Given the results of our study, we would expect greater efficacy by following a sprinkler strategy in launching market reference and feedback programs in countries that are high in collectivism, low in power distance, and high in masculinity. By contrast, countries with alternative profiles would be better suited for a waterfall strategy for launching market reference and feedback programs.

The study also informs managers about the impact of country culture on the relationship between the adoption of MGS and CUR. For example, the study suggests that countries with indulgent cultures should enhance the diminishing positive effect of MGD and MGFs on CUR. Managers can plan their innovative product entry strategy in the international markets accordingly.

Third, regarding an MGS launch strategy, we estimate the effect of MGS on CUR over the subsamples. The results suggest that the impact of a one-unit increase in MGD from no adoption to adoption is 1.8% in the US and Canadian markets, 3.2% in the Colombian and Mexican markets, and 2.7% in the French and German markets. We estimate the effect of MGS on CUR for a typical client in the United States and Canada. On an average base of $73,970 of CUR per client, the adoption of one incremental innovation generation (MGD) increases CUR by $8,150 per client. Conversely, an incremental product feature (MGFs) or a reduction in MGT by one year increases CUR by $5,200, or $2,310 per client, respectively. Thus, improvements in the three MGS adoption metrics translate to 11%, 7%, and 3% increases in CUR per client, respectively. For a typical client in Colombia and Mexico, the corresponding values are $4,820, $3,640, and $1,620, respectively, per client and translate to 14%, 9%, and 5% increases in CUR per client, respectively. We obtain similar results for the French and German markets. Thus, these findings have implications for enhancing MGD and MGFs over MGT to improve client engagement, and firms can use this approach to manage their relationships with global clients. Accordingly, a global firm can manage the launch of generations in various markets to obtain the best returns. For example, firms may be better off investing in promoting MGD and reducing MGT than enhancing MGFs for clients that adopt only a single innovation generation (e.g., with MGD = 1) even when the seller firm has launched subsequent innovation generations. These client acquisitions efforts will maximize both direct and indirect engagement if a client has adopted multiple MGSs but is lacking in client engagement (e.g., an early adopter with low usage rates, few client references, or a variable revenue stream). That is, a seller firm can enhance returns by investing in client retention to promote more MGFs and maximize both direct and indirect engagement. Thus, the results provide a better understanding of balancing client acquisition and client retention efforts for MGS.

Fourth, in terms of developing an effective failure recovery strategy, the study suggests that failure attenuates the relationship between MGD, MGFs, and MGT and CUR. Thus, a firm launching a new MGS should think not only about developing the best innovative product to attract clients but also about developing an effective failure recovery strategy. Effective failure recovery would also yield more successful client references and valuable client feedback, due to an increase in CUR. Finally, after launching MGSs, managers should consider the adoption time and its various features to achieve higher levels of client engagement.

Limitations and Future Research Directions

This study conceptualizes and tests the empirical relationship between MGS adoption behavior of B2B clients and their engagement with the firm. However, the study also suffers from several limitations that future research could address. First, the generalizability of these findings across firms, different markets, types of innovation, and products and services needs to be tested. For example, future research might examine data from B2C markets across varied product categories to validate our findings. Moreover, the adoption of MGSs is often part of an ecosystem. For example, Microsoft offers Teams, Office 365, and the Cloud. Future research might address whether the strength or “stickiness” of the ecosystem affects consumption and reference patterns. Second, our findings of diminishing returns show that firms are not optimally spending their marketing resources in their eagerness to sell MGS generations to their clients. Future research could examine this issue using field experiments. Researchers might test how less failures and optimal marketing expenditures can change the shape of the relationship between MGS adoption behavior and client engagement. Third, Hofstede's cultural dimensions do not account for within-country variation. Future research might assimilate the idea of cultural tightness and looseness by examining cultural differences using Schwartz's measures, thereby obtaining country-specific standard deviations for each cultural dimension. Finally, we limit the analyses to MGS; future research could test the generalizability with radical or incremental innovations.

Supplemental Material

sj-pdf-1-jig-10.1177_1069031X231151659 - Supplemental material for Consequences of Multigenerational Services Adoption Behavior: Global Client Engagement

Supplemental material, sj-pdf-1-jig-10.1177_1069031X231151659 for Consequences of Multigenerational Services Adoption Behavior: Global Client Engagement by Ashish Sood, Shaphali Gupta and V. Kumar in Journal of International Marketing

Footnotes

Acknowledgments

The authors thank the JIM review team for their comments and suggestions during the multiple rounds of the review process. They also thank a major international firm for providing us access to the data used in this study. In addition, they thank the special issue editors for their valuable feedback during the review process. They also thank several colleagues for their feedback, as well as the participants at the JIM TPGM postconference in February 2021 and at different university research seminars. Finally, the authors thank Renu for copy editing a previous version of the manuscript.

Special Issue Editors

Kelly Hewett, Cheryl Nakata, and Kay Peters

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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