Abstract
Interfaces, interactions, and time are commonly understood as foundational constructs in the field of organizational frontlines, but their current definitions are fragmented and disconnected. We propose a set of propositions drawn from theorizing the foundational constructs to facilitate systematic studies of frontline phenomena and promote integration across studies. We also advance the concept of the frontline nexus, a constellation of relations that interconnect the foundational constructs, and propose that the nexus is the appropriate unit of analysis for the study of organizational frontlines. To specify a system that embeds the frontline nexus and situates its foundational constructs, we discuss the role of agency, technology, learning, and privacy as key structural characteristics. In response to changing frontlines, we offer 11 propositions to advance research and practice of organizational frontlines. These propositions encourage researchers to explore the potential of new technologies, investigate the role of frontline actors in shaping interaction outcomes, examine the impact of learning and development initiatives, and consider the ethical implications of frontline actions.
Emerging consensus views the organizational frontline as a pivotal point of contact between customers and firms, 1 where value is created and co-created and also extracted and exchanged (Barann et al. 2022; De Keyser et al. 2020; Singh et al. 2017). The frontlines perspective in customer service emphasizes touchpoints along the customer journey, from pre-purchase to post-purchase. Taken together, these interactions determine customer outcomes, like satisfaction and loyalty, that predict a firm’s long-term profitability. Touchpoints may be controlled by the firm, the customer, or both, and are characterized by various qualities, such as participation and context, including physical and social factors (De Keyser et al. 2020). Intelligent experience engines fueled by AI are increasingly deployed to help understand, shape, customize, and optimize frontline interactions in order to enhance customer experience, lift satisfaction, and win loyalty. A growing number of service organizations, such as Comcast, HomeServe, and Starbucks, are already operating at this frontier to develop new forms of human and machine collaboration at the frontlines (Bannon, 2023; Edelman & Abraham, 2022). Growing recognition that frontline effectiveness is a key factor in customer outcomes, value creation, and firm profitability makes the evolution of frontlines a lever of strategic significance (Huang & Rust, 2021; Singh et al., 2021; Verhoef et al., 2009).
Scholars from various disciplines study the frontlines phenomena, but their approaches differ based on their research interests. However, they agree that interfaces, interactions, and time are the fundamental concepts in the field (Grewal et al. 2022; De Keyser et al. 2020; Singh et al. 2017). These concepts have broad definitions, with interfaces referring to the features of artifacts that enable communication between customers and firms, interactions representing the characteristics of communications that take place during customer-firm contacts, and time encompassing the characteristics of temporality, such as the speed and duration of the communication. Although interfaces and interactions receive the most attention, time is also considered an essential element in frontline dynamics. As aptly stated by Singh et al. (2019, p. 91), “frontline interactions are inextricably bound, embedded, and connected in time, with the past and future impacting the present.” Yet, none of these foundational concepts has been developed theoretically for systematic use in research and practice, and current approaches rely on intuitive, ad-hoc conceptions. This varied use of foundational concepts, which lack theoretical grounding, is a hindrance to a systematic and meaningful body of work, given that these concepts form the building blocks of the organizational frontlines field.
In addtion, while interfaces, interactions, and time are acknowledged as foundational concepts of frontline research, few researchers recognize that frontline phenomena are explained, not by standalone foundational concepts, but rather by a frontline nexus that intersects these foundational concepts. The frontline nexus involves jointly considering how interfaces, interactions, and time interweave and shape outcomes. Configuring the foundational concepts in different ways generates variety in the frontline nexus and thus produces different outcomes in different frontline situations. The joint effects of interfaces, interactions, and time are understudied in frontline research, and the narrow focus on individual elements, common in studies today, overlooks the “systemic interplay among them,” and misses the “interdependencies” that unfold over time (Luyen et al., 2022, p. 284). Therefore, to achieve a coherent and meaningful study of the ebbs and flows in service interactions, scholars and practitioners must begin with the frontline nexus as the unit of analysis and consider all three foundational constructs simultaneously. Inattention to the nexus as the unit of analysis in organizational frontlines studies has led to a literature that is conceptually fragmented, empirically disconnected, and lacking generalizable principles for theory and practice.
Yet another overlooked but crucial issue involves theorizing that the frontline nexus is embedded within a broader ecosystem. This ecosystem has a significant impact on frontline dynamics, either allowing natural evolution or shaping their ebb and flow. The relationship between the embedding ecosystem and the frontline nexus is similar to that of structure and process, where structures govern norms, resources, and rules, and processes involve actions, activities, and mechanisms that entities employ. Focusing solely on processes without considering underlying structures can result in overlooked substantive mechanisms, endogeneity biases, and unreliable insights (Thornton, Ocasio, and Lounsbury, 2012). Recognizing the importance of the ecosystem in which the frontline nexus operates is essential for understanding its dynamics, developing robust theories, and informing practice.
This study aims to address challenges and highlight opportunities in organizational frontlines by developing a frontline theory that (a) advances the foundational constructs of frontline inquiry, (b) develops the frontline nexus as a unit of analysis, and (c) outlines key features of the embedding system—specifically, agency, technology, learning, and privacy—that impact frontline dynamics. Figure 1 illustrates the dynamic interaction among these key elements of a frontline theory. The foundational concepts of the frontline nexus are at the center of this figure, and this nexus is embedded within an ecosystem that is characterized by four distinct subsystems. In drawing propositions from our theoretical work, we adopt a strategic perspective to emphasize the active role of frontline entities—organizations, employees, customers—in shaping the frontlines-in-change. Tables 1-3 summarize these propositions along with our proposed definitions and directions for future research and strategy in the field of organizational frontlines. We begin with a discussion of the foundational concepts. Conceptual framework for the foundational constructs and embedding ecosystem of the frontline nexus. Conceptualizing “Time” as a Foundational Construct for Frontline Research and Practice. Conceptualizing “Interface” as a Foundational Construct for Frontline Research and Practice. Conceptualizing “Interaction” as a Foundational Construct for Frontline Research and Practice.
Foundational Concepts of Frontlines Research and Practice
To advance the foundational concepts for the study of frontlines, we build on key published articles from multiple disciplines. The selection criteria include saliency, recency, and diversity. Saliency in terms of the article’s attention to the interfaces, interactions, or time; recency in terms of publication within the last 10 years, and diversity in terms of the variety of conceptualizations across the selected articles. 2 Each study typically takes a somewhat different approach to the foundational concepts; none examines all of the foundation concepts simultaneously. Our objective is to systematically integrate the advances from different studies and thereby bring coherence to the conceptualization of interfaces, interactions, and time. From each article, we extract the meaning ascribed to the focal concept in a way that is faithful to the original contribution. From the varied concept meanings, we identify common characteristics that are relevant for advancing the conceptual definition of the focal concept. For each foundation concept, we present both the conceptual definition proposed in this study and the conventional definition prevalent in the current literature. Then, building on the current literature, we develop specific propositions to draw out the implications for theory and practice. We begin our development by theorizing time in frontline phenomena because it has been understudied and underscores the dynamic nature of each of the foundational concepts that follows.
Subjective Time and Clock Time
Clock time, indicated by its constant pace and repeatable duration (e.g., 24/7 sequence) is a key feature of dynamic accounts of frontline phenomena (Grewal et al. 2022; Ancona et al., 2001). However, another way to conceptualize time is subjective time (George, 2000; Hornik, 1984; Kruglanski et al., 2016; Wittmann & Paulus, 2008; Geiger et al., 2021; Kunisch et al. 2021). Specifically, we define subjective time as the perceived experience of the pace, duration, and movement of communication acts between customers and firms (and their agents) in goal pursuit. Table 1 presents the proposed definition relative to the conventional definition of clock time. It also provides an overview of insights from selected studies that guide our development of subjective time as well as propositions we develop below to guide future work.
Subjective time manifests in at least three interrelated ways: time perception, time awareness, and time orientation (Wittmann and Paulus 2016). Time perception refers to an individual’s ability to accurately estimate time duration, time awareness to the subjective impression of time passing relatively fast or slow, and time orientation to the individual’s sense of the past, present, and future. Time perception accuracy is influenced by factors such as motivational states, emotions, and novelty. Time seems to fly when an activity is enjoyable or interesting (people underestimate actual duration), but drags when a task is tedious or boring (people overestimate actual duration). The relationship between sensory load and time processing explains why novel events tend to slow subjective time and fearful situations tend to stretch subjective time. The concept of mental effort as a determinant of time experience is consistent with the idea of “fluency,” or the subjective experience of the ease or difficulty of a mental task (Oppenheimer, 2008). Time awareness is illustrated by Kruglanski et al.'s (2016) theory of locomotion, which posits that individual differences in the proclivity for movement can explain why people in similar situations may react differently to the flow of clock time. Time orientation, or the individual’s sense of the past, present, and future, also plays a significant role in shaping how people experience time. The theory of temporal focus posits that the tendency to focus on the past, present, or future is an individual-difference variable (Shipp et al., 2021), and this variation in temporal focus explains why different individuals in similar situations behave differently. Practitioners often use these principles, typically without realizing it, to manage subjective time in customer interactions.
Time becomes strategically important when a customer’s perception of time diverges from their expectations for a particular type of interaction, and they attribute this dissonance to the firm. Working with these insights, we develop propositions for subjective time as a lever for frontline effectiveness with three specific strategies—aligning, coordinating, and personalizing.
Aligning Subjective Time
Situational stimuli can alter the perception of time and override individual differences by appealing to universal emotions, motivations, and needs. One approach to managing customers' subjective time is to align the pace, duration, and movement of service actions with individuals’ proclivities (Wittmann and Paulus, 2008). For example, Bergh et al.'s (2016) study blends two insights: shopping is a chore for most people, and speeding up the shopping journey improves customer satisfaction for high and low locomotors alike (see Table 1). Bergh et al. arranged a store’s layout and signage to provide situational cues that increased locomotion speed and amplified customers' perceived movement; customers were able to locate items without delays or distractions (e.g., avoiding “treasure hunt” shopping). By altering the speed of goal pursuit, the study of locomotion moves from an individual-difference analysis to a strategic decision to creatively position situational cues that evoke positive emotions during goal pursuit.
In another illustration, Matthews and Meck’s (2016) study of the investment processing principle suggests that people may enjoy investing time in activities that they find relevant and meaningful, even if this lengthens the duration. This counters the common belief that customers are time-misers who adhere to an efficiency rule and prefer shorter shopping times and faster goal pursuit. Dagogo-Jack et al. (2020) applied this insight to duration metrics and situational cues that guide customers' use of time as a resource (e.g., “this article is an 8-minute read”). They showed that, in some situations, customers do not follow the time efficiency rule, because they enjoy spending more time-consuming content that is vivid, clear, and salient. Likewise, Lee et al. (2012) note that service providers often focus on shrinking duration, but in doing so, they miss opportunities to capture customers' attention with vivid activities that they might find useful and enjoyable. Theme parks are adept in the use of situational cues to manage customers' subjective experience of waiting time. Thus, we propose (see Table 1):
Coordinating Subjective Time
Table 1 also illustrates strategies for coordinating subjective time across the customer journey. Shipp and Jansen (2021) rely on subjective time mechanisms to explain how customers and frontline agents perceive and interpret time during goal pursuit. They identify attending, preparing, and comprehending as key processes that explain variations in the subjective experience of time. Attending refers to selecting cues that direct attention, preparing to prime mental images about the future to inform present actions; and comprehending to sequencing past, present, and future experiences into a coherent story. Kunisch et al. (2021) propose particularity and plurality to characterize the way individuals, groups, and organizations experience time. Particularity refers to the situated enactment of time and the affective dynamics evoked by different situations. Plurality refers to the synchronicity of multiple rhythms that constitute the experience of time and encompasses nonlinear dynamics. These dimensions are compatible with dramaturgical and improv approaches for performative frontline interactions. In this context, particularity means flexibly guiding the pace, duration, and movement of frontline performance to evoke positive emotions; and plurality means orchestrating the rhythms of internal resources, processes, and teams to synchronize with the customer’s rhythm at each touchpoint. Grewal et al. (2022) discuss communications between retailers and customers but do not conceptualize the proposed dynamics. A frontline theory would necessitate the incorporation of both interaction dynamics and time-activity dynamics to grasp how the pace, duration, and movement can be strategically adjusted to provide coherence and rhythm to customer-firm interactions across the entirety of the customer journey.
Recent literature provides frameworks for coordinating subjective time, including the One-Voice strategy (Singh et al. 2021), multiformat communications (Moffett et al. 2021), and omnichannel marketing (Cui Tony et al., 2021). These frameworks approach the coordination of subjective time across touchpoints differently, but they all share the goal of elevating the customer’s experience by aligning the firm’s actions. The One-Voice strategy addresses the challenge of the diverse interfaces that customers use across touchpoints. The use of different interfaces in different interactions may result in information loss and disjointed communication, which degrades the customer’s subjective experience of time. Omnichannel marketing focuses on optimizing the subjective experience by ensuring “synergistic management of all customer touchpoints” (Cui Tony et al., 2021, p.104). These studies demonstrate that managing subjective time across a customer’s journey is a priority for firms. Thus, we propose (cf. Table 1):
Personalizing Subjective Time
Technology, particularly artificial intelligence (AI), can help firms manage subjective time by predicting and automating tasks, enhancing the skills of frontline employees, detecting emotions that indicate misalignments, and intervening to prevent or mitigate misalignments. For example, in food services, where delivery speed is a key factor in customer satisfaction, Domino’s Pizza offers an app that allows customers to track their orders from preparation to baking to real-time delivery. This eliminates uncertainty around order processing and makes the subjective experience of time less tedious. Technology can also make waiting time less onerous by reducing opportunity costs. Hu, Xu, and Qu (2021) found that when customers join a virtual queue, which allows them to continue with other tasks instead of waiting in a physical queue, there are fewer complaints (24.7%) and higher customer satisfaction (10.8%). Technology can also be used to alter time perceptions by providing cognitive distractions that are novel or informative, such as trivia quizzes or educational videos in waiting rooms, as noted earlier.
Digital technologies also allow firms to personalize the subjective experience of time at scale. For example, digital technologies can quickly identify and authenticate customers with methods such as a registered telephone number, user name, voiceprint, or faceprint, so firms can anticipate the next step in a journey and route the customer to an agent they have had a positive experience with in the past. Emotion-AI tools can analyze customers' vocal or facial signals to detect negative emotional states and alert agents to take action to improve the experience. For example, Skyscanner, a metasearch engine for travel options, uses Sightcorp’s F.A.C.E. software to sense emotional states from facial expressions and recommends travel options that promote positive emotions. Likewise, HomeServe created an AI-powered digital assistant, Charlie, that is learning to handle customer claims, schedule repairs, and reduce processing times. Similarly, Vedantu, an online tutoring platform, uses Entropik’s emotion-AI technology to tag specific emotions at different points on the user journey. If the app detects a drop in engagement, it generates personalized nudges in real-time to improve performance. Wearable sensing technologies can also be used to manage subjective time for consumers dealing with chronic diseases or mental health episodes (de Fazio et al. 2021). Thus, as noted in Table 1:
Interfaces as Spaces
To interact, firms and customers need shared interfaces that facilitate communication and coordination throughout the customer journey. These interfaces are typically a combination of human (e.g., frontline employees) and machine assets (e.g., smartphones). Most past research has defined interfaces as artifacts (e.g., devices, agents, robots) that enable communications between a customer and service provider. In the theoretical development that follows, we adopt a strategic perspective of interfaces as shared spaces constituted by assemblages of invented, repurposed, and existing artifacts that enhance and/or constrain the flow of communication acts between customers and firms in pursuit of mutual goals. That is, interfaces create spaces for interactions; and the attributes of these spaces impact interaction outcomes. Table 2, drawn from the literature, illustrates the artifacts versus spaces distinction and the ways users shape frontline interfaces to communicate on their terms. Table 2 also summarizes the key propositions that we develop below and their implications for future work. Next, we show that interfaces are (a) assemblages of artifacts, (b) means for communication acts, and (c) value-creating technologies.
Interfaces as Assemblages of Artifacts
Table 2 specifies the conventional notion of an artifact as a physical or digital entity that possesses distinct features and capabilities that enable firms and customers to communicate (Hoffman and Novak, 2018; Ramaswamy and Ozcan 2018). The quality of communication outcomes is contingent upon the features that the artifacts possess. In general, more advanced communication features result in superior outcomes, but the corresponding complexity and costs associated with such features necessitate tradeoffs that firms and customers must weigh. The strategic significance of interfaces is underscored by this need to choose artifact features that balance communication quality and operational efficiency.
Numerous studies have identified specific interface features and interaction attributes, but there is little agreement in these specifications. For Singh et al. (2021), assemblage features fall on a human-to-machine continuum which yields interactions that vary in terms of cost, speed, quality, agency, and socio-emotional attributes. Privacy and permission are also salient. Bolton et al. (2018) refer to two clusters of interface features—digital density (low to high information flow features including content, complexity, and speed) and physical complexity (low to high sensory richness including functionality, ambiance, and cues), and one interaction attribute, social presence, that captures experiential attributes (low to high frequency, closeness, and conviviality). Grewal et al. (2022) identify one cluster of interface features: convenience (e.g., low to high accessibility, flexibility, and ubiquity) and one cluster of interaction attributes: social presence (low to high connectivity, networking, and conversationality). Patricio, Fisk, and Cunha (2008) highlight three interaction attributes: autonomy, learning, and social functionality. Other researchers focus only on interface features. For example, De Keyser et al. (2020) classify touchpoints in terms of three variables: control (firm control vs. customer control), nature—the way the brand is represented (physical, digital), and journey stage (pre-purchase, purchase, post-purchase). Moffett et al. (2021) argue that a variety of interfaces can be classified parsimoniously in terms of the cues (e.g., proximal, visual, verbal, and textual) and channels (e.g., synchronicity and revisability) that they permit.
Connecting artifacts in an assemblage enhances the interactive capabilities of interfaces and increases communication richness. When people or devices are connected, they can gather, exchange, and analyze data in real-time, and that information can be used to shape interactions. Hoffman and Novak (2018) identify the rise of the Internet of Things (IoT) as a turning point in firm-customer interactions. The smartphone is a prime example of this trend. 3 Hoffman and Novak’s exemplar of an assemblage involves four connected artifacts: a hub that controls color-changing LED lightbulbs (Philips), a smartphone app that allows personalized control of devices, a voice-activated virtual assistant (Alexa), and a remote-controlled pet toy (Rolling Ball). Devices in this assemblage collect, share, and process usage and user data, which enables interfaces to acquire emergent properties that can enhance, expand, or limit user experiences. Ramaswamy and Ozcan (2018) expand the networked assemblage idea by envisioning systems of connected people, devices, and processes that can engage in autonomous interactions, so that some interfaces “have a mind of their own” in specific situations. This conceptualization implies that interfaces may be designed with the potential to comprehend the status of an interaction and initiate independent actions to move it forward.
Hoffman and Novack’s (2018) research highlights consistent differences in interface usage. For instance, they present a case study of a couple in which one partner makes extensive use of assemblage functions, such as voice commands to synchronize home lighting with music playlists to create a particular ambiance, while the other partner uses minimal voice commands and ignores most of the interface’s interactive capabilities. Hoffman and Novack propose agentic and communal roles to explain these differences. Communal users conform to or reject the emerging properties of the assemblage, while agentic users actively shape the assemblage by expanding or limiting its functions, such as adding or removing artifacts. Service firms can use these insights to train their frontline agents and motivate customers to adopt an agentic approach to interface use. The variations in interface usage on both the firm and customer sides emphasize the importance of agency in deploying interfaces in order to improve the quality of subsequent interactions.
Table 2 describes various instances where users adopted an agentic approach to shape interaction spaces. For example, in Bucher and Langley’s (2016) study, a hospital ward serves as a communication portal to enable surgeons, nurses, and patients to pilot new surgical techniques for fast-track colon-resection surgery. Kim et al. (2021) show how a loyalty program links psychological, design, and operational elements to establish a flexible space for connecting a firm with its customers, thereby improving acquisition, onboarding, expansion, and retention actions. In Hilken et al. (2020), an assemblage of augmented reality (AR) artifacts enables new social spaces for peer-to-peer communications that allow individuals to share and virtually enhance physical objects (e.g., a bedroom) as they make purchase decisions (e.g., choice of paint). In Aversa et al.’s (2021, p. 2) analysis, amateur motor clubs established in the UK during the early 1920s acted as artifacts that created new spaces for leisure and automobile enthusiasts to transform “spaces into places” infused with social meanings and values. Thus (see Table 2):
Interfaces as Means of Communication Acts
A customer journey typically includes multiple interaction episodes, which often involve different interfaces. For example, purchasing a new computer may involve searching for information online, visiting a physical store to view the product, making an online purchase, and then having a chat session to assist with setting up the device. In such situations, flexibility in establishing a shared space with customers is crucial for the firm’s success. Developing a typology of potential spaces is an essential first step in achieving this strategic flexibility.
Three distinct types of spaces can be identified, each configured with different assemblages and varying functionalities (Wedel et al. 2020). The first type includes physical spaces, which can be either natural or purpose-built to facilitate face-to-face interaction. These spaces are characterized by their structural and facility features. The second type involves virtual spaces, which are created by digital interfaces—assemblages of artifacts that flow digital signals over networks to create a 24/7 space for one-to-many (provider-customer) and peer-to-peer (customer-to-customer) communications. These interfaces use augmented reality (AR) or virtual reality (VR) technologies to augment or simulate physical spaces and provide vivid sensory qualities, telepresence, and user control. VR interfaces create 3D spaces that simulate face-to-face communication, while AR interfaces overlay sensory information onto real-world images to create an enriched, highly experiential perception of reality. The metaverse extends these spaces to fuse physical and virtual realities to produce engaging, socially interactive environments that users find compelling. The third type of space is cyberspace, a distributed digital space. In cyberspace, interactions take place through digital interfaces, and the experience is largely mediated by the interface’s design and functionality. Understanding the different types of spaces and the assemblages that configure them is essential for designing effective interfaces that can support different types of interactions (Hollebeek et al. 2021).
Spaces enabled by interfaces can be evaluated based on the effectiveness and efficiency of communication acts for achieving customer and firm goals. Prior research shows that factors such as media accessibility, richness, interactivity, and synchronicity impact communication effectiveness (Yadav and Pavlou 2014; Moffett et al. 2021). The contemporary insight is that communications that mimic face-to-face exchanges are more effective, because they offer fluid and unrestricted use of cues for querying, clarifying, and responding in a turn-by-turn conversation. Although humans in face-to-face interactions are considered the gold standard in communications, technology-driven interfaces offer efficiencies, conveniences, and richness that are difficult to match, making them both efficient and effective.
Matching interface functions with customer and firm goals at different stages of the customer journey can enhance efficiency without compromising effectiveness (Moffett et al. 2021). For example, in the later stages of the journey, customers may have a greater need for querying and clarifying, while in the earlier stages, the focus may be on gathering information and obtaining user reviews. In this case, human interfaces may be better suited for later stages, while machine interfaces may assist with efficient and effective communications in the early stages. Service providers must consider factors such as the availability and functionality of various interfaces, the need for communication continuity, and the unpredictability of customer goals to optimize communication efficiency and effectiveness (Reinartz et al. 2005). For example, Melumad & Pham, 2020 found that users with a hedonic relationship with their smartphones could alleviate stress by utilizing the device’s features instead of relying on human interaction. Table 3 underscores that the firms can manage interface selection challenges by organizing frontlines to shift flexibly between interfaces in order to communicate effectively and efficiently with customers and achieve their goals. Thus, we propose (see Table 2):
Interface Technologies as Enablers
Technologies that connect artifacts in assemblages with emergent properties are strategic tools for creating customer value. By designing for user agency, allowing for flexible and agile transitions across interfaces, ensuring seamless communication for goal pursuit, and incorporating data analytics and AI capabilities to overcome barriers to firm-customer interactions, interface technologies enable a service provider to gain a competitive advantage through superior customer-centricity and problem-solving. The individual technologies embedded in the assemblage are key enablers of this competitive advantage.
For instance, interface technologies play a crucial role in delivering customer-preferred spaces that facilitate effective goal pursuit. In a study comparing telephone, email, and mail interfaces, Godfrey et al. (2011) found that firms need to balance functionality and costs in their interface choices. For effective communications, the authors found a trade-off between email and mail contacts—doing both can be detrimental, and ignoring customers' preferences can be detrimental as well. However, Reinartz et al. (2005) suggest that sometimes interfaces can have synergistic effects that eliminate the need for trade-offs. To optimize interface choices, service providers must understand customers' expectations and goals and consider carryover traces from past exchanges at each touchpoint. However, firms may be constrained from imposing their interface choice as it is difficult to anticipate customers' goals and situations. Thus, designing interfaces with a wide range of functional capabilities to match users' goals and circumstances is crucial. Providing broad and flexible interfaces at each touchpoint and seamlessly transitioning between interfaces during goal pursuit serves customers’ needs; but this flexibility is operationally challenging and has been largely overlooked in frontline theory and practice (Godfrey et al., 2011; Reinartz et al., 2005). Thus, we propose (see Table 2):
Interactions as Relational Dynamics
The study of interactions between customers and frontline agents is a central area of interest in organizational frontlines research (Solomon et al. 1985). In our development, we conceptualize interactions as relational dynamics of communications acts that occur between customers and organizational agents as they work towards their respective goals. The term relational refers to the social process of building a connection and developing mutual understanding, while dynamics refers to the patterns and changes that occur over time.
Researchers from different fields have identified many factors that can either facilitate or hinder social processes in these interactions. For instance, Lechner and Mathmann (2021) highlight the importance of authenticity and empathy, while Fiske et al. (2002) emphasize the significance of both competence and warmth. Other factors that have been identified as significant include customer orientation (Brady and Cronin 2010), information and technology (Ahearne et al., 2021), homophily (Ertug et al. 2022), linguistic matching (Ludwig et al. 2013), and problem-solving (Marinova et al. 2018). By taking these factors into account and understanding the relational dynamics at play during customer-agent interactions, organizations can develop more effective strategies to create positive interactions that benefit both parties.
Table 3 summarizes selected studies that examine interactions in terms of relational dynamics, and the propositions we offer for future work. In building on these studies, we propose new directions for the study of interactions in frontlines research and practice. First, we propose three factors—confidence, continuity, and customization—that secure a common ground for relational dynamics. These factors play a crucial role in creating a sense of trust, understanding, and shared purpose between customers and frontline agents. Second, we develop the processes of shrinking, reaffirming, and growing the common ground as key mechanisms of relational dynamics. These processes shape the nature and flow of interactions and can be used to manage relational dynamics in various situations. Third, we discuss the optimal mode, level, and intensity of relational dynamics and highlight the importance of moderation and trade-offs. Each factor offers a strategic lever for managerial action and is the focus of our propositions.
Note that conceptualizing interactions as relational dynamics does not necessarily mean that every touchpoint in a customer journey should focus on fostering relational dynamics or that such dynamics even require human-to-human contact. Relational dynamics can extend beyond individual touchpoints, and while the social processes of building the common ground are inherently humanistic, our concept allows for the use of human and machine intelligence in novel configurations to promote effective relational dynamics.
Relational Dynamics Factors
Effective communication practices that foster long-term customer relationships are characterized by rich content, fluid turn-taking, and a shared focus on achieving a common goal (Gutek 1995; Gwinner et al. 1998). Relational exchange theory highlights the significance of confidence, continuity, and customization as crucial factors in successful service relationships (Lee et al. 2020). Confidence refers to the trust and security that customers feel, while continuity refers to consistency and reciprocity in the relationship. Finally, customization pertains to personalization and tailoring experiences to meet the unique needs of individual customers. By prioritizing these factors, service providers can establish and maintain strong relationships with their customers, which encourages repeat business and helps to sustain a competitive advantage. 4
Frontline action is a strategic tool for establishing shared understanding and fostering long-term relationships with customers. For instance, Lai (2021) suggests that gaps in patient information can result in unintentional errors that erode shared understanding and hinder positive relational dynamics, ultimately impacting service quality and safety outcomes. To mitigate these risks, healthcare workers must take the initiative to expand and verify their knowledge of each patient’s status. In a B2B context, Lee et al. (2020) found that global teams often struggle to build relationships across different cultures. They found that implementing interaction scripts helped team members establish a shared understanding and positive relational dynamics that promoted respect, openness, and connectedness for “engagement within the team, fueling a cycle of growing positive relational dynamics” Lee et al. (2020, p. 116).
Expanding on this idea, Marinova et al.'s (2017) propose that smart technologies that capture, organize, and analyze multimodal data from customer interactions can help FLEs establish positive relational dynamics. Marinova et al. provide the example of wearable sensors, such as continuous glucose monitors that collect and analyze user health data in real-time and transmit alerts to the user and physician when remedial intervention is necessary. Sharing alerts and data creates a common ground that enables frontline actions to foster positive relational dynamics, which in turn motivates behavioral changes for positive outcomes. Thus (see Table 3):
Relational Dynamics Processes
The process of turn-taking in communication between customers and providers either strengthens, maintains, or weakens the quest for common ground. Both parties typically rely on interaction scripts acquired from experience, inferred from the service context or situational cues, or improvised to ensure continuity, confidence, and customization of interactions. If the relational dynamics fail to secure and expand a meaningful common ground, the interaction is likely to end and mutual goals may be abandoned. Steele (2021) provides a novel conceptualization of interactions as a motivated choreography aimed at achieving mutual intelligibility to realize mutual gains. Zorina and Dutton (2021) identified four modes of interaction in a digital innovation ecosystem—symbiotic generative, symbiotic mutualistic, parasitic complementary, and parasitic competitive—that are key to explaining innovation outcomes. October et al. (2018) analyzed audio recordings of caregiver conference calls to identify missed opportunities for alliance-building and vocal pauses that discouraged active turn-taking by patients in asymmetric relationships. Both proved detrimental to the relational dynamics between physicians and patients and eroded the common ground that underpinned their interaction. Smart technologies, such as always-on devices that capture more data from customers, promise to enhance service providers' understanding of customer needs and lead to more productive interactions.
Bailey et al.’s (2022) processual perspective on technology-enabled interactions addresses how humans and machines interact when connected in an assemblage. This perspective emphasizes emergent, dynamic, and entwined mechanisms. Emergent implies that the capacity to cultivate common ground is not solely determined by the artifacts that constitute it, but rather by the relations that connect them. Dynamic denotes that the ebb and flow of relations vary based on how different entities interact with each other. Entwined describes the idea that technology and social action are a constellation of relations that connect a customer and a service provider. Technology that connects entities both enables and constrains relations, and entities with different capabilities generate a variety of relational dynamics and therefore produce different outcomes. This processual perspective highlights the importance of relational dynamics in shaping the outcomes of technology-enabled interactions. By comprehending how different entities interact within an assemblage, service providers can improve their service delivery and offer more meaningful and personalized experiences for their customers.
In service settings, conversational agents such as voice assistants and chatbots have the potential to provide efficient and personalized customer service. However, Benner et al. (2021) show that conversational breakdowns can occur, leading to the erosion of common ground and ultimately service failure. The authors suggest that conversational agents can leverage real-time learning to develop customized recovery strategies for each customer. By analyzing past interactions in real-time, conversational agents can dynamically adapt their responses to fit the unique needs of each customer. This approach to recovery strategies offers valuable insights for improving customer experiences and maintaining positive relational dynamics.
GONG, a sales enablement platform, is a practical example of how the principles discussed above can be put into practice. The platform offers B2B sales teams real-time coaching based on five essential principles: automated multimodal data acquisition and tagging, providing a complete view of customer interactions, offering real-time insights for informed decision-making, basing operations on data-based evidence, and connecting salespeople with managers throughout the company (Brooks and Spelman, 2021). Although the primary focus is on coaching sales teams, the platform aims to provide real-time insights for establishing common ground with customers, de-risking deals, identifying potential deal-making opportunities, and driving customer engagement. GONG’s ability to provide a comprehensive view of customer interactions, coupled with its coaching capabilities, empowers salespeople to cultivate more meaningful and profitable relationships with their customers. Thus, we propose (see Table 3):
Relational Dynamics Trade-Offs
Recent research in relational dynamics suggests that strategies for building confidence, continuity, and customization depend on specific encounter, customer, and context details. For example, Marinova et al. (2018) found that, when dealing with airline customers seeking problem resolution under time pressure, service agents can demonstrate confidence by competently solving problems, continuity by promptly addressing issues, and customization by offering personalized solutions to meet customer needs. Likewise, Sandberg et al. (2022) found that to ensure positive patient outcomes, nursing home staff must balance patient security, consistency across encounters, and opportunities for patient autonomy. However, this requires difficult trade-offs. For instance, allowing patients to exercise autonomy by moving around freely can compromise their security, because staff cannot ensure reliable adherence to safety protocols. Trade-offs in confidence, continuity, and customization are common in service settings where resources (e.g., employees) and costs are constrained. Godfrey et al. (2011) address how frontline capabilities can mitigate these trade-offs and enhance service delivery.
Recent studies have demonstrated that AI-enabled technologies can enhance customer interactions. Specifically, Mechanical-AI can track and monitor past customer journeys to gain insights and improve future interactions, while Thinking-AI can personalize offerings to enhance customer confidence and continuity. Additionally, Feeling-AI can augment emotional labor by customizing interactions and preventing frontline employee burnout (Huang and Rust 2018; Davenport et al. 2020; Luo et al. 2021). Leveraging AI capabilities can help companies increase relationality in customer interactions and create more seamless and personalized experiences for their customers. Automating routine tasks with AI frees up employees to focus on higher-value activities that require creativity, problem-solving, and empathy; and ultimately this leads to better customer experiences and more satisfied employees.
While AI-enabled technologies have the potential to enhance relational dynamics, some customers may feel uncomfortable with machine-mediated interactions, and this can hinder adoption (Longoni et al. 2019; Dietvorst et al. 2018). The uncanny valley effect refers to the discomfort some customers experience when they interact with service robots that have human-like features, and the effect seems to increase as robots more precisely emulate human behavior (Mende et al. 2019; Puntoni et al. 2021). However, recent studies suggest that educating customers about the capabilities and limitations of AI technology can help alleviate negative first impressions of AI interfaces. Cukier (2021) contends that customer resistance to conversational AI bots often stems from unresolved ambiguities and concerns about explainability, value exchange, and transparency; bias and discrimination are also persistent concerns. Longoni et al. (2019) found that informing patients that an automated healthcare provider could deliver reliable personalized care was sufficient to overcome their resistance to automated care advice. Similarly, in a B2B context, Dietvorst et al. (2018) show that explaining the limits of algorithm accuracy and allowing for modifications significantly reduces algorithmic aversion. Thus (see Table 3):
Frontline Nexus at the Intersection
The three foundational constructs invariably operate together, and the nexus that results from this entwining is the proper unit of analysis for frontlines research. One approach to theorizing the frontline nexus is to conceptualize it as a coordination between context (or situation) and content (or action). According to the person-situation and situation-action interaction theories (Parsons and Shils, 2017), actions are not solely determined by the actor (person) or the situation in which they occur. Rather, they are the outcome of a complex interplay between the situation and the actor, and this dynamic interaction explains how actions and outcomes unfold over time. Building on these theories, our approach argues that interfaces create spaces (i.e., situations) in which interactions (i.e., actions) occur and emphasizes the dynamic coordination between interfaces and interactions that is required over time. This notion of frontline nexus as dynamic coordination of interfaces and interactions over time can be illustrated by a scenario. Imagine, for instance, a situation where a customer contacts a service agent via mobile phone to resolve an issue, but is only able to do so during restricted hours of 8 a.m. to 5 p.m. on workdays instead of 24/7 availability. This frustrates the customer, as they must take time out of their workday to make the call and may be on hold for an extended period before an agent can assist them. Furthermore, the agent may be unaware of the customer’s context (wait time) or problem, which can further exacerbate the issue. Using the proposed concept of frontline nexus, a context aware frontline agent could ask the customer to provide details, promise to research the problem, and get back by the afternoon using the customer’s preferred interface, perhaps an email that would be less disruptive than a call. Here, the agent creates new spaces, accelerates subjective time, and advances common ground, all of which foster positive relational dynamics. However, achieving effective coordination in service encounters can be challenging, as it requires agents to anticipate and dynamically exploit the nexus of interfaces, interactions, and time in order to nurture, recover, and grow a common ground for problem resolution. Agents must exercise agency in this process, which involves navigating the complex interplay between the different elements. Any changes made to one element can trigger further changes in the others, further complicating the coordination process.
By exercising agency and leveraging the principles of frontline nexus, agents can navigate the challenges of service encounters more effectively and establish more productive and satisfying relationships with customers. Bolton et al.'s (2021) study reflects this principle. They find that different interfaces stimulate different sensorial and cognitive priming effects, which affects the flow of interactions. There may also be a carry-over effect from one interaction to the next. For instance, stores create rich sensory experiences, while websites prioritize functionality and efficiency. As a result, customers who visit a store after browsing a website are more likely to focus on price and ease-of-use features when making purchase decisions rather than the store’s servicescapes. Here is where understanding the concept of frontline nexus can shift the relational dynamics. For instance, sales clerks in physical stores may need to create a new space to help the customer disengage from their previous online experiences, so that they can fully engage in the rich sensory experiences that stores offer. Bolton et al.'s study represents an important step toward understanding the frontline nexus and the complexity of customer-firm interactions that make the dynamic coordination of the elements so challenging in practice.
A process theory perspective on technology and organizing, inspired by sociology and management, offers an alternative approach to understanding how interfaces are embedded in complex constellations of relations that connect customers and firms. According to Bailey et al. (2022) and Waardenburg et al. (2022), interactions between entities are shaped by the dynamics of their relationships, and interface technologies cannot be separated from the network of relations in which they are implemented. In this view, technology is inseparable from relations, and the modern automobile provides a proof point. The vehicle serves as an interface that facilitates interactions between the customer/owner and multiple firms. Equipped with IoT sensors and machine learning algorithms, the car is connected to mapping software, performance and maintenance databases, weather forecasts, entertainment providers, and surrounding cars that can communicate over mesh networks (e.g., icy road ahead!), as well as the manufacturer/dealer network (Bailey et al. 2022). From this perspective, interactions are dynamic processes that depend on the automobile owner’s mastery of technological complexity and the relations between the various interfaces. How the technologies learn, adapt, and anticipate the next events from the intensity of the owner’s relations will in turn shape the emergence of new relations with the owner. The potential outcomes of ownership, including fewer accidents, improved fuel efficiency, decreased downtime due to proactive maintenance, and reduced emissions and dust particles, depend on the owner’s and firm’s mastery of the available interfaces and their relations (Bailey et al. 2022; Waardenburg et al. 2022; Gahler Markus et al., 2023). The process perspective provides a nuanced theorizing of the nexus of interfaces, interactions, and time by viewing technology as inseparable from the complex network of time-dependent relations in which it is embedded. This perspective emphasizes the need to comprehend how individuals and organizations purposefully and adeptly employ interface technologies to mold interactions.
Harvey et al.'s (2020) investigation into smart home automation technology reflects this process perspective. The IFTTT (If This, Then That) connectivity platform empowers users to create personalized “recipes” that enable diverse devices and apps to produce new interaction spaces. Recipes are created by connecting the devices’ application programming interfaces (API) using low-code or no-code programs that require little technical skill (e.g., send a message on my smartphone to turn up the home air conditioner when my car heads for home after work). Each recipe constitutes a unique nexus of co-created relations that enable interactions among various devices, software, and apps to achieve specific goals. After analyzing 13,905 distinct API recipes crafted by consumers, the authors developed a 3X3 classification system that distinguishes between the form of action enacted (supportive, advisory, or persuasive) and the type of value derived by the consumer (transformative, utilitarian, or hedonic value). Their study demonstrates that consumers pursuing different values create distinct relational patterns among interfaces and thereby shape new, emergent mechanisms at the nexus of interfaces, interactions, and time. To summarize, the frontline nexus represents a complex interplay of spaces, relational dynamics, and time that requires dynamic coordination. Treating these foundational elements as distinct constructs in isolation is inadequate. Therefore, we propose:
Embedding the Frontline Nexus: Toward Theorizing Organizational Frontlines
As noted, we propose four key elements of the embedding system that impact the dynamics of the frontline nexus: agency, technology, learning, and privacy (Figure 1). Agency pertains to decision-making authority, technology to digital infrastructure, learning to knowledge management, and privacy to data protection. Additional features of the embedding system are plausible, but these four characteristics are crucial. 5
Agency and Frontline Nexus
Agency refers to the capacity for independent action, whether taken individually or collectively, to construct spaces, alter relational dynamics, and modify subjective time. While both frontline employees and customers may possess the agency to shape spaces and interactions, the impact of their actions hinges on the creativity they employ and the rights they negotiate or assert. The heterogeneity of creativity and effort among individuals explains why some encounters with seemingly similar service issues, similar kinds of customers, and similarly trained service employees may result in vastly different customer experiences.
Agency also locates accountability. When the firm exerts agency, it is accountable to orchestrate the frontline nexus to ensure satisfactory interactions during customer journeys. Firms often assert agency by mandating predefined scripts for frontline employees and narrow communication protocols for customers, thereby achieving consistent interactions. Firms may maintain agentic control over the frontline nexus for other reasons, such as cost control or efficiency; but limiting frontline agency can impede effective problem-solving and compromise customer satisfaction. Due to the variety of consumer problems, situations, and goals, it is practically impossible for the firm to mandate specific solutions that will address all customers’ needs. Frontline employees require agency and resources to sense, in real-time, the unique circumstances of each customer and devise an appropriate and satisfying response. The annual Customer Rage surveys, 6 and other studies, indicate that many firms persistently fail in frontline interactions, and this may reflect an overreach of agentic control that leaves frontline employees unable to effectively sense and respond to individual customer situations.
Locating agency in frontline employees presents challenges for firms, particularly in managing the inherent tension between productivity and quality. This challenge has been studied extensively (Singh 2000; Marinova et al. 2017; Rust and Huang 2012). A productivity focus prioritizes efficiency and often involves limiting frontline employees’ agency in order to ensure conformity with organizational scripts. A quality focus prioritizes customization and typically gives frontline employees the autonomy to personalize solutions and services. In practice, frontline employees are expected to balance both goals by delivering high productivity and quality service in customer interactions, and this creates an agency problem that invites challenging trade-offs (Wirtz and Zeithaml 2018). Significant interdisciplinary work has focused on these problems, but there are still many unresolved questions that require further research and practical solutions (Subramony et al. 2021; Ostrom et al. 2021; Schneider and Bowen 2019).
Customers can also exert agency by demanding personalized attention to their problem-solving and goal pursuits. In the past, exerting agency was a cumbersome process for customers (e.g., writing to the CEO, seeking legal arbitration). Social media have significantly lowered the effort threshold and expanded the scope of customers’ agency (Gregoire and Mattila 2021). Social media platforms blur the lines between private (e.g., individual experiences of service failure) and public (e.g., shared experiences) realms, exposing organizational failures and promoting viral mechanisms that can lead to firestorms (Herhausen et al. 2019). These trends in lowering the effort threshold and amplifying the social potency of customer agency are expected to continue. So, firms face a new urgency to manage agency rights at the frontline nexus and rapidly solve customer problems to recover from failed customer interactions.
Technology and Frontline Nexus
Technology has revolutionized organizational sense and respond processes, enabling firms to capture data from customer interactions and gain insights from multimodal interactions both online and offline (Bitner et al. 2000; Murray et al. 2021). Additionally, autonomous learning and problem-solving technologies allow firms to act in a smart, agile, and efficient manner (Huang and Rust 2018). However, technology can also degrade customer interactions, especially when the technology is poorly integrated or intended solely to cut costs. The value of technology in the frontline nexus is limited by ineffective human-machine collaboration, flaws or biases in machine learning programs (e.g., unrepresentative training datasets), and data silos and legacy IT systems that restrict data sharing and invite customer resistance. While digital technologies offer numerous benefits, they also entail unintended consequences that harm customer interactions and limit the business value of these technologies.
Digital technology is not the sole purview of organizations; customers also deploy technology to shape their interactions with businesses (Labrecque et al. 2013). Peer-to-peer networks and recommendation systems provide customers with the power to select interfaces, drive interactions, dictate the pace of service encounters, and ultimately influence the relational dynamics and outcomes of their interactions (Wertenbroch et al. 2020; Puntoni et al. 2021). For example, a study by Ahearne et al. (2021) on buyer-seller negotiations shows that information asymmetry, which traditionally favored sellers, is declining. This is due to customers, who are less dependent on firms for information, shifting from rich face-to-face communications in frontline interactions to lean digital communications. As a result, customers can engage with sellers later in the purchase journey after they have narrowed down their options. Easy access to reliable information gives customers the ability to verify claims made by sellers during persuasion attempts, demand greater transparency and effective problem-solving, de-emphasize interpersonal and social factors, and assert dominance in the ebb and flow of interactions.
In B2B interactions, buyers are increasingly gaining control. McKinsey (2022) 7 reports that B2B customers now expect an always-on, personalized, omnichannel experience with an average of 10 communication channels, nine of which are technology-driven. Ahearne et al. (2021, p. 24) characterize this growing customer agency as a “new reality” that necessitates a “critical re-envisioning” of past theories based on outdated assumptions and the development of “new theories for contemporary buyer-seller interactions.”
Digital technologies have also empowered consumers in B2C settings. Alvarez et al. (2021, p. 625) note that customers actively engage in “relationship work” to shape and maintain their brand relationships. This involves continuously evaluating a brand against competitors, adjusting their needs as the brand implements pricing and delivery charges, and observing how the brand responds to their feedback and complaints. Previous literature examined customers' emerging marketplace power; but now ubiquitous connectivity, distributed information, and new AI technologies have accelerated the growth of customers' power and agency.
Learning and Frontline Nexus
Learning involves the dynamic construction of spaces, alteration of relational dynamics, and modification of subjective time by applying local and collective intelligence (Huang and Rust 2018; Marinova et al. 2017). Local intelligence involves capturing and analyzing contextualized, tacit, and heuristic data from individual frontline encounters. Collective intelligence involves capturing, processing, synthesizing, storing, and distributing vast amounts of frontline interaction data that can be used to serve individual customers or draw conclusions about trends across customers (Singh et al. 2021). These capabilities are organizational assets that contribute to effective learning (Argote and Miron-Spektor 2011).
Firms aim to personalize interactions, manage customer journeys, and apply precise segmentation and pricing strategies by learning from every customer interaction. Automated data capture, analysis, and execution make this easier in online interactions. Additionally, firms monitor offline behavior, using data such as credit and debit card transactions and public records to build customer profiles. Linking online and offline behavior offers rich learning opportunities. For example, Google acquires credit card transaction data from Mastercard to analyze the impact of online ads on offline shopping behavior. Retailers and supermarkets use technologies like smart carts and digital shelves to capture in-store behavior, which they combine with online behavior to provide a seamless omnichannel experience for customers (Inman and Nikolova, 2017). By leveraging online and offline data, firms can gain insights into their customers' behaviors and preferences, which allows firms to provide more personalized experiences and improve their competitive advantage.
Frontline employees generate local intelligence when they interact with customers to customize or coproduce service solutions. However, this knowledge is often tacit and can be lost if the employee is not available for subsequent encounters. To prevent this loss, firms capture local intelligence and convert it into collective intelligence, so that the learning can be disseminated to others who face similar situations. Extracting intelligence from frontline interactions is a deliberate process that requires time, effort, and resources, which frontline employees may lack due to productivity and efficiency demands (Marinova et al. 2017).
Effective organizational learning systems integrate local and collective intelligence and generally perform four functions: monitoring customer behavior, identifying action implications, selecting a course of action, and coordinating execution. Marketing technology (Martech), which accounts for approximately 30% of corporate marketing budgets in North America, plays a critical role in performing these functions. 8 In 2021, the global Martech market was valued at $345 billion, with 10,383 applications spanning six primary categories and 49 distinct types 9 of organizational learning and coordination software. However, while Martech systems are well suited to identify emergent patterns via collective intelligence, human learning systems may be better suited for local intelligence that is sensitive to changing and novel patterns. Overcoming the challenges of coordinating local and collective intelligence and human and machine-mediated learning is necessary for effective frontline action.
Customers also deploy personal learning systems to improve their market power (Labrecque et al. 2013). Internet searches yield detailed product information and reviews, which reduce the traditional information asymmetry and aid decision-making. Customers long yearned for these advantages but were limited by the way market information was organized (e.g., firm-controlled) and distributed (e.g., privately held). Digital technologies have made market information more freely accessible, and the automobile industry is an example of this shift. Barley (2015, p. 15) found that customers typically acquire accurate data on dealers’ costs and inventory before arriving at the dealership. As a result, sales staff can no longer define the sales encounter as a negotiation and instead must assume the role of a price giver, encouraging customers to become price takers. Customers also benefit from the collective intelligence generated through crowd-sourced knowledge, such as reviews (Labrecque et al. 2013); and they are becoming increasingly savvy about firms’ learning and monetization motivations and the learning systems firms use to gain intelligence on customers. As a result, the frontline nexus is becoming a contested domain, and issues of privacy and transparency are gaining prominence.
Privacy and Frontline Nexus
Privacy is a crucial concern in the flow and use of data about spaces, relational dynamics, and subjective time (Quach et al. 2022; Okazaki et al. 2020). A firm’s learning would be curtailed without access to time-stamped interface and interaction data. However, both regulatory controls and customers' privacy concerns are evolving rapidly and likely to limit firms' access to interaction data. Loose regulatory oversight and assumed customer consent has so far provided firms with unfettered access to interaction data, but now firms must invest in data safeguards that adhere to new regulatory standards and address customers' privacy concerns if they want continued access to customer data. How this will shape the frontline nexus remains an open question that warrants attention.
The divergent interests of customers and firms create privacy conflicts (Wirtz et al. 2023). Firms want to gather as much customer data as possible to understand customers' wants and needs, purchasing habits, buying power, preferred interfaces, interaction styles, and brand awareness and evaluations. Firms prefer to gather and use this intelligence in private, out of sight, because it allows them to gain advantages in customer interactions. For example, when they know a customer has an immediate need and buying power, the firm is tempted to upsell the customer, which means the customer may buy more than they need; or the firm may raise prices, so customers pay more than they ordinarily would pay. Firms may also use intelligence on customers’ social networks, habits, and preferences to provoke needs, induce scarcity, and stir emotions that favor demand for their products/services while stunting price sensitivity. And to maintain short-term profitability, firms may be tempted to adopt weak customer privacy policies.
Customers’ privacy concerns include a range of fears; they do not want to become a crime victim, be denied services based on their status, receive excessive and intrusive messaging, or suffer embarrassment by public disclosure (Okzaki et al. 2020). Financial fraud is a particularly pressing concern because personal data such as license numbers, bank accounts and credit card information can cause significant financial harm in the hands of scammers. Customers expect their data to be secure, but frequent data breaches demonstrate that this is often not the case. Reported data breaches in 2022 totaled 1774 cases, and there were over 392 million victims; 83% of all US breaches involve personal identification data (Identity Theft Resource Center, 2023). In 2019, a single data breach at Facebook (now Meta) caused the personal information of over 500 million Facebook users to end up for sale on the dark web.
Regulators have attempted to resolve the conflicting interests of firms and consumers, and they recognize that consumers may be powerless or uninformed about how to prevent data collection and misuse of their data (Steinhoff and Martin 2022). The European Union enacted the General Data Protection Regulation in 2016, followed by the Digital Markets and Digital Services Acts in 2022. These regulations preserve consumers' fundamental rights to control what data are collected and how the data are used. Similar efforts have been made in Australia, Canada, the UK and other countries 10, as well as some US states (e.g., the California Consumer Privacy Act 2018). These regulatory initiatives go a long way toward reducing asymmetries between firms and consumers. Depending on how much control consumers exercise over firms' access to their data, the firm’s ability to coordinate and predict a customer’s needs, preferences, and journey status may be restricted; and this will also change the study of frontline phenomena.
Concluding Notes
This study aims to contribute to the theoretical understanding of organizational frontlines by advancing the conceptualization of three foundational constructs: time, interfaces, and interactions. We propose a definition for each construct based on original theorizing and demonstrate how this advances the current definition. In our theorizing, time is conceptualized as a subjective experience of the pace, duration, and movement of communicative acts, interfaces as communication spaces constructed out of assemblages, and interactions as relational dynamics of customer-firm communications that ebb and flow over time. We also argue that examining these constructs in isolation or holding one constant is insufficient for studying organizational frontlines. Instead, we propose the frontline nexus as the appropriate unit of analysis, which requires considering the interplay among the foundational constructs when taken together. Our theorizing emphasizes that the frontline nexus is a constellation of connections that entwine the foundational constructs in dynamic coordination over time. In turn, the frontline nexus is embedded in a system of higher-order elements such as agency, technology, learning, and privacy, which can operate as either passive or active elements to shape the dynamics of the frontline nexus. Taken together, our theoretical contributions offer clarity and coherence to the study of organizational frontlines and lay the groundwork for future empirical research.
Building upon our theoretical contribution, we offer 11 propositions to guide future research and practice in the field of organizational frontlines. Although our focus in developing these propositions is on managerial strategies to ensure positive customer outcomes, our conceptual contribution enables various approaches for expanding perspectives in organizational frontlines. For instance, researchers can develop propositions from a consumer behavior perspective that employs the conceptualization of foundational constructs and frontline nexus to examine how customers configure interfaces and participate in frontline interactions to achieve better outcomes. Alternatively, future studies can adopt technological, sociological, or institutional perspectives to provide diverse viewpoints that enhance the study of organizational frontlines. Regardless of the perspective taken, it is crucial to have shared and robust definitions of foundational constructs and frontline nexus and a consistent unit of analysis for frontline studies. Our contributions are primarily centered on the theorization of foundational definitions and the unit of analysis for frontlines studies and are agnostic to perspective. Thus, our contributions are relevant to multiple perspectives that make up the organizational frontlines field and advance the broader goal of enhancing the understanding of organizational frontlines.
Footnotes
Acknowledgments
We thank the Co-Editors of the special issue, Todd Arnold and Detelina Marinova, for their valuable input in shaping this contribution. We would also like to thank Tom Brown and Mike Brady for their insightful comments during the development process
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.
