Keywords

Introduction

The COVID-19 pandemic illuminated the pivotal role of supply chain management in social, economic, demographic, regulatory, and market dynamics both nationally and globally (Arvis et al., 2023; Ferguson & Lahiri, 2021). From newsrooms to board rooms, the responsibility of supply chain management in facilitating the flow of resources, information, and products to service demand for goods or attenuate critical supply issues is conclusively evident (Henrich et al., 2022; Langley et al., 2021). Indeed, the ability of supply chain managers to design supply chains agile enough to absorb some of the otherwise detrimental effects of the increased volatility that characterize the contemporary marketplace is nothing short of phenomenal, if not incredible (Henrich et al., 2022; Pricewaterhouse Coopers, 2023). Much of the ability to manage the supply chain effectively has been facilitated through the use of advanced technologies that harness data that is analyzed to support decisions-making. While this data collection confers a significant competitive advantage, issues related to information privacy in supply chain management have now arisen. So, why is data collection important in supply chain management? What are the sources of information privacy issues in supply chains? Before delving into these questions, we define supply chain management.

Overview of Supply Chain Management

Supply chain management refers to the effective and efficient management of the flow of information, resources, currency, and physical products within and between organizational actants. These actants, often a collection of firms, seek to connect the demand for products and services to their sources of supply through a “chain” of activities conducted via interdependent processes. The supply chain has been described as “…the interconnected journey that raw materials, components, and goods take before their assembly and sale to customers” (McKinsey and Company, 2022). The Association for Supply Chain Management (ASCM) (2023) defines the supply chain as “the global network used to deliver products and services from raw materials to end customers through an engineered flow of information, physical distribution and cash.” Concurrently, the Council of Supply Chain Management Professionals (CSCMP) explains supply chain management as an action that:

Encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third party service providers, and customers. In essence, supply chain management integrates supply and demand management within and across companies” (CSCMP, 2023)

As noted, logistics management is a component of supply chain management. As defined by the CSCMP (2023), logistics management is “that part of supply chain management that plans, implements, and controls the efficient, effective, forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers’ requirements.” Logistics management activity typically includes inbound and outbound transportation management, fleet management, warehousing, materials handling, order fulfillment, logistics network design, inventory management, supply and demand planning, and the management of third-party logistics services providers (CSCMP, 2023). A component of logistics management, order fulfillment, is the process, which ensures that customers that are served by the supply chain receive their orders in a timely and accurate manner; ensuring customer expectations are met (Fawcett & Fawcett, 2013).

Order Fulfillment in Supply Chain Management

Customer orders, or the anticipation of them, initiate supply chain activity. Order fulfillment is therefore of central importance in supply chain operations, and involves generating, packing, delivering, and servicing customer orders (Croxton, 2003). Often the only method by which customers interact with selling firms, the order fulfillment process is a mechanism through which selling firms pursue customer service level targets, thereby directly affecting the buyer experience in various ways (Croxton, 2003; Fawcett & Fawcett, 2013). The way in which the order fulfillment process is managed can determine, among other outcomes, whether orders are picked accurately from a warehouse, whether products are in stock at a store, or whether deliveries are on time to a customer’s home. As such, a deepened understanding of customer needs and preferences when designing the order fulfillment process, can significantly enhance its effectiveness and its ability to be responsive to evolving consumer expectations (Croxton, 2003; Langley et al., 2021). Simultaneously, supply chain managers must ensure that this responsiveness is facilitated in as cost-efficient a manner as possible (Langley et al., 2021). Order fulfillment operations that are managed to be both efficient and effective can facilitate faster or more convenient deliveries to customers, as well as reduced order-to-cash cycle times for selling firms (Croxton, 2003; Langley et al., 2021). Figure 12.1 displays the relative activities that occur within the areas of supply chain management, logistics management, and order fulfillment.

Fig. 12.1
A chart depicts supply chain management, logistics management, and order fulfillment. Supply chain management includes planning, sourcing, procurement, manufacturing, and logistics. Logistic management includes logistics network design, demand planning, inventory management, and ware housing.

Supply chain, logistics, and order fulfillment activities

Full size image

Well-managed order fulfillment operations can be the differentiating factor among firms in highly competitive marketplaces where customers can switch easily between sellers (Henrich et al., 2022; Langley et al., 2021). In retail markets, differences in order fulfillment operations have distinguished market leaders from other competitors (Devari et al., 2017). This distinction exists because the speed, convenience, and service levels associated with the handling of orders after they are placed with retailers are now among the primary factors that customers consider when deciding from whom to purchase (Pricewaterhouse Coopers, 2023; Tsai & Tiwasing, 2021). Further, retail market leaders must now operate order fulfillment processes that support both traditional in-store engagement and increased consumer e-commerce activity, a practice referred to as omnichannel retailing (Langley et al., 2021). Omnichannel retailing integrates the sales, operations, and order fulfillment processes of both online and in-store operations (Dohrmann et al., 2022). The advent of omnichannel retailing has served to increase the complexity of the order fulfillment process in retail supply chains because of the need for retailers to coordinate activities within and between channels (Fawcett & Fawcett, 2013; Pricewaterhouse Coopers, 2023) This innovation has increased the complexity associated with retail supply chains, leading to the need to leverage the capabilities facilitated by using advanced technologies (Dohrmann et al. 2022; LaBombard et al., 2019).

Order Fulfillment in Retail Supply Chains

Urbanization, mobile technology access, product variety, and improved technology-enabled delivery services have played a role in the growth of consumer engagement in e-commerce (Wolff et al., 2020). In omnichannel retailing, customers engage with retailers and their products across a multiplicity of both digital and physical touchpoints that can include retail stores, various types of other pick-up locations, and websites (Langley et al., 2021; Pricewaterhouse Coopers, 2023).

The goal of omnichannel retailing is to provide customers with a unified and synchronized experience, allowing customers the choice of purchasing a product through any channel, thus enabling the reception and return of products through those channels (Dohrmann et al., 2022; Langley et al., 2021). An example would entail a customer purchasing a product on a retailer’s website and then being presented with the option to either pick the order up at one of the retailer’s store locations, or have it delivered to their home. If that same customer opted to pick the order up in the store but then changed their mind, they could return the item without ever leaving home by using the online channel.

Successful omnichannel retailing necessitates intricate planning of inventory levels across many retailer and logistics service provider facilities and assets, including stores, warehouses, and delivery vehicles. Delivery of goods must not compromise on the speed, convenience, and consistency in service levels among channels—consumers have an expectation of efficiency conducted at a high level of professionalism (Dohrmann et al., 2022; Langley et al., 2021). For example, customers’ propensity to buy online and pick-up in-store (BOPIS) has now become a standard shopping behavior (Dohrmann et al., 2022; Pricewaterhouse Coopers, 2023) and requires sophisticated coordination of order processing, inventory sharing, and transportation planning activities (Langley et al., 2021). This sophisticated coordination points to the pivotal role that logistics management, through the order fulfillment process, plays in ensuring omnichannel retailing supply chain effectiveness and efficiency (Devari et al., 2017; Dohrmann et al., 2022). Notably, the coordination of the order processing, storage, and movement of physical products within and between channels in omnichannel retailing is facilitated by technology-enabled order fulfillment (Alicke et al., 2016; Borgi et al., 2017; Dohrmann et al., 2022). These enabling technologies simultaneously provide value while engendering potential information privacy issues.

Organizational Uses of Technology in Retail Fulfillment

The advent of digitization across the supply chain has led to the availability of large-scale data sets with information on numerous dimensions of supply chain operations. These data sets can comprise information on purchase transactions, contract terms and conditions, traffic volumes, customer and facility addresses and locations, and competitor pricing for instance (Borgi et al., 2017; DHL, 2023). In retail, technology and the data management associated with its use can support order fulfillment activities in any of three ways (Dohrmann et al., 2022; LaBombard et al., 2019). First, technology can facilitate insights by generating new data sets or analyzing existing ones. An example of this system would be the generation of a data set that details the differences in the average on-time delivery performance between two different product categories offered by a retailer online. Second, technology supports fulfillment operations when applied to facilitate the automation of order fulfillment tasks and processes. Automation refers to the use of technology to automate tasks that were previously conducted manually to improve fulfillment process efficiency. An example would be the use of robots to carry out order picking in a warehouse, a task traditionally completed by human employees. Third, technology can be used to engage in the monitoring of order fulfillment activities to improve worker productivity and safety, or any of a variety of other desirable outcomes. An example of this process would be the use of advanced surveillance technologies to record employee truck loading and unloading performance in real time.

Indeed, the visibility facilitated by monitoring, tracking, and surveillance technologies has been of paramount importance in retailers and logistics service providers’ ability to address the various complexities brought on by e-commerce growth and the resulting surge in omnichannel retailing activity (LaBombard et al., 2019). The ability of retail supply chain managers to have a comprehensive view of their fulfillment operations in real time and leverage that ability to make operations and resource allocation decisions speedily, with increased confidence, is enabled using advanced technologies (Cantor, 2016; LaBombard et al., 2019). These technologies can range from applications that improve frontline worker picking operations, to those that provide decision support in the selection of optimal logistics network designs (Alicke et al., 2016; Langley et al., 2021; Winkenbach, 2018).

Given that two of the more significantly affected stakeholders in the rapid adoption of advanced technologies in retail fulfillment operations are customers and the employees engaged in the order fulfillment process, it is important to understand how technologies are leveraged during the order fulfillment process to gather data from each of these stakeholder groups.

Customer Data Collected During Order Fulfillment

As customers become more connected to retailers through digital technologies, vast amounts of consumer supply chain-related data are generated. This data is then utilized to develop more customer-centric order fulfillment operations, which subsequently facilitate the development of innovative and customized products and services (Mahoney & Dauer, 2021). Benefits can accrue to retailers through leveraging technologies to better understand the retail customer. For example, logistics service providers and the retailers they serve can have access to customer delivery addresses, their purchase history, and their web browsing activity (DHL, 2023; Winkenbach, 2018). This data is used to comprehend both human behavior and operational characteristics, which then serves as input to improve various fulfillment operations activities. These activities include customer expectations regarding delivery service features; customer product preferences as to what, where, and when they order; fluctuations in traffic conditions during delivery; and real-time transportation equipment availability (Pricewaterhouse Coopers, 2023; Winkenbach, 2018).

Retailers collect various forms of customer data which can include sentimental data, attitudinal data, behavioral data, engagement data, personal data, and demographic data. Sentimental data indicate what customers say they will do, like a customer signaling in a comment online that they will purchase twice as much of a product if it is available in a different color. Attitudinal data show customer emotions and perceptions regarding their experiences, like a feedback survey regarding package delivery or a review page discussing order delivery tracking experiences. Behavioral data reveals what customers do, like the average order value for a newly launched product or the return rate for that product. Engagement data predicts customer behaviors across various retail channels and helps determine how customers respond to and share experiences regarding engagement with a specific retailer on social media. Personal data suggests customer-specific information that can include name, birthday, website login details, or credit card details. Demographic data describes customers by population-based factors like age, race, and sex.

Specific to retail fulfillment operations, the collection of these various forms of customer-related data can serve to facilitate improvements to both instore and online product layouts based on behavioral mapping technologies or improved target marketing using interactive voice assistant technologies, which analyze customer speech and commands (Dohrmann et al., 2022; Winkenbach, 2018). Some retailers also use internet of things (IOT) technology sensors to determine the location and status of shipped orders or to calculate the remaining number of inventory units for a product across their network of facilities, thereby ascertaining in-stock levels. In both cases, this is information that can be communicated with customers, leading to higher levels of customer service. Further, to improve their last-mile delivery operations, some retailers are leveraging customer transactions, delivery addresses, delivery routes, purchase histories, and other forms of personal information records, to improve service design and efficiency (DHL, 2023; Winkenbach, 2018). Retailers therefore use customer data to sense, motivate, and serve customers during the order fulfillment process by meeting their expectations regarding time, convenience, and place of product order and receipt (Mahoney & Dauer, 2021). The benefits of collecting the aforementioned types of customer data also include the ability of retailers to use it to identify key customer segments, attract and retain high-value customers, improve customer onboarding to increase repeat purchases, increase personalization of the online and in-store shopping experience, prevent customer churn through augmented customer engagement, and enhance responsiveness to customer expectations based on collected data (Eikelmann et al., 2023; Mahoney & Dauer, 2021).

Employee Data Collected During Order Fulfillment

Both retailers and logistics service providers are using advanced technologies to harness and assess data on various dimensions of employee’s state of being and performance. Employees in fulfillment operations include those individuals who are engaged in warehousing, transportation, and store operations activities or those who assume corporate management and analyst roles in retail distribution. Data collected on these employees is levied by employers to improve employees’ state of well-being, their job performance, or fulfillment process efficiency. For instance, interactive artificial intelligence can be leveraged to automate order processing workflow components, allowing workers to pursue more complex tasks when situations require (Dohrmann et al., 2022). Performance data can also be used to reallocate or reassign tasks in real time (Dohrmann et al., 2022). Tracking technologies are also used to capture delivery vehicle driver behavior as well as data on vehicle speed, position, braking intensity, and environmental conditions (Acharya & Mekker, 2021; Winkenbach, 2018). Elements of these data can be used to send warnings to drivers in real-time, to avoid pending hazardous or unsafe incidents (Acharya & Mekker, 2021).

While from an organizational perspective, prior research has found that organizations “that are open perform better” (Tapscott & Ticoll, 2003)—open referring to the ability to observe various aspects of both customer and employee behavior. There are, however, developing issues regarding the collection of employee data. Indeed, there is a correlation between observability and performance, the premise being that both managers and employees need to see working activity to determine what needs to improve (Bernstein, 2012; Tapscott & Ticoll, 2003). Notably, the ability to observe, or transparency, has been shown to facilitate the presence of two key tenets of organizational productivity: organizational learning and operational control (Bernstein, 2012; Deming, 1986). Technology is a leading factor in facilitating observability through the acquisition of process and employee data. As such, technology is also a major mechanism through which privacy-related issues in the supply chain are actualized.

Privacy Concerns

In retail supply chains, the collection of the aforementioned various forms of process, employee, and customer during the order fulfillment process, using data and the widespread implementation of advanced technologies is a driver of privacy now being a central issue in supply chain management (Dohrmann et al., 2022; Eikelmann et al., 2023; Sheng, 2019). The harnessing of data at the individual level in real-time requires that retail supply chain managers be highly cognizant of the fact that both employees and customers perceive such practices to be invasive, which can impact their workplace or shopping experience (Cantor, 2016). The collection and analysis of granular-level employee and customer-generated data that supply chain digitization and advanced technology deployment have facilitated has given rise to concerns regarding data protection, security, and privacy in omnichannel retail fulfillment operations (Cantor, 2016; Dohrmann et al., 2022; Winkenbach, 2018). Due to the multiplicity of functional areas, such as tasks, channels, customer decisions, purchase actions as well as other contextual factors, fulfillment operations must take heed of them all. The rationale is simple. There is no overarching conceptualization of privacy because the definition of the term is contingent on both individual and contextual factors (Cottrill & Thakuriah, 2015). Yet, the common theme regarding privacy that drives much deliberation revolves around concerns as to who owns the control and flow of information (Cottrill & Thakuriah, 2015).

In retail fulfillment operations, the perception of the loss of privacy can manifest itself in various forms, depending on the dimension of privacy being considered. For instance, warehouse employees may determine that employers collecting data on their order pick rates is reasonable, but then find that the harnessing of data regarding their health conditions is invasive. Likewise, retail customers may find the collection of their browsing behavior while shopping online to be admissible but deem the analysis of their shopping related driving patterns to be intrusive. While both retailers and logistics service providers make the case that the harnessing and analysis of customer and employee data can confer benefits to both individuals and the fulfillment process, trade-offs exist concerning the privacy loss-related cost individuals or firms may bear associated with the collection of that data. Table 12.1 displays several of the advanced technologies being used in retail fulfillment operations, describes them, and then provides examples of some of the potential privacy implications associated with their use.

Table 12.1 Privacy implications of technology use in retail fulfillment
Full size table

These examples are just several among a plethora of potential privacy scenarios that can exist across the vast array of activities and processes that characterize retail order fulfillment operations (Dohrmann et al., 2022; Eikelmann et al., 2023; Fawcett & Fawcett, 2013; Sheng, 2019). Yet, among various dimensions of privacy, work environment privacy and information privacy are highly salient in retail order fulfillment operations (Bhave et al., 2020). In the following sections, we will focus our discussion on the implications of these dimensions of privacy, as applicable to either employees or customers in retail order fulfillment operations.

Employee Privacy in Retail Supply Chain Order Fulfillment

Collecting information on employees can significantly benefit employers. These benefits can accrue in recruiting and hiring efforts, in supporting effective and efficient operations performance, and in minimizing exposure to risk and legal issues. Employers such as retailers and third-party logistics service providers presuppose it to be advantageous to maintain extensive sets of information on their employees (Bhave et al., 2020). Indeed, corporations do have the right to amass information about their workforce, such as data pertaining to employee capability, performance, and ethics. They may do so to implement programs that reduce counterproductive or undesirable work behaviors (Bhave et al., 2020). Managerial visibility, or transparency, to such information, has proven to be accretive to retail order fulfillment performance, as employees of varied levels of experience and capabilities engage in increasingly complex fulfillment process tasks at stores, distribution centers, and transportation depots. These tasks are essential to realizing customer service level targets and operating efficiency goals.

Transparency, or the ability to accurately observe fulfillment operations activities, procedural approaches, work behaviors, and task performance at a granular level, can result in improved organizational learning and operational control for retailers and logistics service providers alike (Bernstein, 2012; Deming, 1986; Miller, 2018). Extant literature indicates that transparency has been found to improve a work unit’s access to expertise, experience, and stored knowledge, thereby increasing the propensity for knowledge transfer and shared understanding. Transparency is also an antecedent to accelerated organizational learning curves as well as to increased strength in the ties that govern knowledge exchange between networks of actors (Bernstein, 2012; Miller, 2018).

The ability to observe can also enable operational control through the availability of more detailed, comprehensive, accurate, and real-time data on employees and processes across the retail supply chain, thus, improving both hierarchical and peer control of fulfillment operations (Bernstein, 2012; Dohrmann et al., 2022). The value proposition of increased transparency across retail supply chains has resulted in supply chain managers engaging in process and facility redesign aimed at facilitating increased observability, most often supported through using advanced surveillance and data capturing technologies in stores, distribution centers, and transportation depots (Bernstein, 2012; Dohrmann et al., 2022; Sheng, 2019). Surveillance and data collection significantly contribute to the ensuant privacy issues that employees bring attention to and, at times, engage in litigation to address. Indeed, in retail order fulfillment operations, the importance of ensuring that employee and customer data are safe is paramount now, making privacy one of the central points of interest in omnichannel retail and order fulfillment operations (Dohrmann et al., 2022; Pricewaterhouse Coopers, 2023).

Employers such as retailers and third-party logistics service providers presuppose it to be advantageous to collect extensive sets of information on their employees (Miller, 2018). Indeed, corporations do have the right to collect information about their workforce, such as data pertaining to employee capability, performance, and ethics. They may do so to implement programs that reduce counterproductive or undesirable work behaviors (Bhave et al., 2020). In retail order fulfillment operations, the benefits discussed above can be realized through (1) a retailer or logistics service provider conducting psychometric analysis on job applicants to determine their cognitive fit with the responsibilities associated with a high-intensity role within a fulfillment center; (2) a retailer or logistics service provider collecting data on the individual performance rates of employees that load and unload containers at a transloading facility, being intent on using that data to identify constraints to improving its cross-docking operations speed; and (3) a retailer or logistics service provider using an inward-facing camera to record a truck driver during a traffic accident and thereafter use the footage as evidence to avoid punitive actions in litigation proceedings (see Fig. 12.2).

Fig. 12.2
An inward-facing camera captured a photograph of a truck driver eating meals inside his truck.

(Image credit Alamy Stock Photo/Olaf Doering)

Inward-facing camera monitors an employee

Full size image

Employees themselves can also profit from the collection of their personal data. Among these benefits include the ability for job performance-related data to be used in the allocation of economic incentives, career advancement opportunities, and other related rewards (Miller, 2018). Personal data can also be used to improve health and safety conditions, like the monitoring of biologically vital information to preempt medical hazards or the suggesting of task-related movement best practices to improve the personal safety of workers (Guillot, 2019). Further, recorded data can be used as material to support employee claims or cases during litigation proceedings (Wendt, 2023). While each of these examples points to the use of data for purposes that appear to be accretive to both the employer and its employees, the appearance of the sensor, surveillance, voice recognition, and other technologies to track, listen to, and record workers in real time while on the job has led to employees and other stakeholders to voice concern over the privacy implications of corporate surveillance programs (Sheng, 2019).

Indeed, industry research has found that most employees in distribution facilities have both reservations and concerns about these tracking and monitoring technologies despite understanding the benefits they confer (Dohrmann et al., 2022; Miller, 2018). While the use of these tracking and monitoring technologies has indeed led to efficiency gains in order fulfillment operations, an unintended consequence of their implementation is a growing concern regarding both corporate and individual privacy in the workplace (Bhave et al., 2020; LaBombard et al., 2019). Given the fact that these large-scale datasets are susceptible to data breaches, they can be distributed to third parties, especially in instances where employers fail to adequately document or notify employees that data collection activity is occurring (Bhave et al., 2020; Iyer, 2023). This dynamic has resulted in growing tensions regarding workplace privacy because employers require information on employees who are increasingly cognizant of their individual rights (Bhave et al., 2020; Iyer, 2023).

Work environment privacy refers to perceptions of control over sensory stimuli in the work environment (Bhave et al., 2020). These stimuli can be visual, spatial, acoustic, or olfactory in nature. Work environment privacy addresses dimensions pertaining to control over employees’ interpersonal interactions in a workspace or access to employees’ presence. More definitively, and as detailed by Bhave et al. (2020), visual privacy refers to an employee being independent of optical stimuli and undesired notice by others. Acoustic privacy, which deals with auditory and sound-related dimensions of privacy, indicates the degree to which employees are isolated from noise, and whether they perceive that their verbal conversations or other forms of speech in the workplace are private. Spatial privacy, or privacy regarding personal space, notes the extent to which an employee perceives others entering the physical area surrounding them as being invasive. Though deliberated less regularly, olfactory privacy touches on the absence of undesired smells in the workplace. Much of the work in extant literature that addresses work environment privacy does so from the perspective of workspace layout and design, often integrating elements of visual, acoustic, and spatial privacy as well. The aforementioned dimensions of work environment privacy are applicable to the retail fulfillment context, and Table 12.2 provides some examples of the work environment privacy implications for employees that are associated with each of these dimensions.

Table 12.2 Work environment privacy dimensions in retail fulfillment
Full size table

Information privacy pertains to control over the acquisition, storage, use, and sharing of employee data, and addresses if and how this information is made available to others (Bhave et al., 2020). An issue of significance to employees in this arena centers on whether they are informed as to the purpose of data collection and its intended use. The nature of data being collected by firms varies and can include social media information mined for use during the recruitment and employee selection process. Most firms are weary of this dynamic because it can adversely impact firm reputation and subsequent recruiting efforts. Dependent on corporate policy, firms can also stockpile employee e-mail-related information or share performance and appraisal information beyond the focal employee and their immediate supervisor (Bhave et al., 2020). Further, the growing use of electronic performance monitoring and tracking systems by employers facilitates the collection of data that can include information on individual task performance or employee location in real time (Bernstein, 2012; Bhave et al., 2020). Differing perspectives between employees and employers exist regarding information privacy (Iyer, 2023). Employees are desirous of having control over their personal information and the level of access an employer has to that information. Conversely, employers expect to be able to have comprehensive information on their employees, in addition to a growing desire to have knowledge of their employees’ whereabouts at any given point in time (Bhave et al., 2020; Iyer, 2023).

Corporations leverage tracking and monitoring technologies to pursue safety, customer service, efficiency gains, and risk-management objectives. In contrast, their employees perceive the existence of significant privacy-related tradeoffs regarding the use of these technologies. From a safety perspective, working conditions are of paramount importance in order fulfillment operations activities. For example, during the COVID-19 pandemic, companies deployed computer vision technologies to ensure that employees in distribution centers and processing facilities adhered to the use of personal protective equipment stipulations (Dohrmann et al., 2022), an action that some employees found to be invasive and infringe on their personal rights. In another instance, various retailers, desirous of addressing working condition issues, directly or confidentially engage with the employees of their suppliers to collect information on the working conditions at those suppliers’ facilities. Moreover, those employees use mobile technologies to record the conditions in which they work (Sanders et al., 2019). This focus on human rights is of course commendable, but some employees at those facilities, and the companies for which they work, determine these actions to infringe on their privacy rights.

Additionally, numerous retailers and logistics service providers utilize surveillance cameras and artificial intelligence technologies to record and detect if employees in their facilities are adhering to ergonomic best practices to minimize the risks associated with personal injuries, in-facility vehicle speed violations, and non-compliance to stipulated walking paths within distribution and production facilities (Dohrmann et al., 2022). Indeed, musculoskeletal disorders (MSDs) occur due to ergonomic-related hazards in fulfillment operations facilities. In response, companies are equipping workers with wearable devices to record and observe employee performance patterns to minimize MSD occurrence risk through either workplace redesign or employee training programs that are based on the observed recorded behavior of employees (Dohrmann et al., 2022).

Of note, wearable technology devices, along with other sensor technologies, are also being used to collect and track employee biometric information. Companies are outfitting retail store, warehouse, and transportation employees with sensor technologies that can detect vital body data, including fatigue and stress levels, heart rates, alcohol levels, and other measures of physical fitness that display how employees are physically responding to the tasks that their respective roles demand. For example, a large retailer recently received a patent for an ultrasonic bracelet that would be able to detect warehouse workers’ locations within a facility and then monitor their interaction with task equipment using ultrasonic sound pulses (Sheng, 2019).

Additionally, employers are utilizing facial recognition and text analysis tools to identify employee emotions and sentiments when completing their tasks (Iyer, 2023). For example, a large retailer recently patented a system that facilitates listening in on both workers and customers to determine their sentiments and preferences (Sheng, 2019). The above scenarios represent only a handful of cases in which advanced technologies track and monitor employee behavior in various aspects of retail order fulfillment operations (Dohrmann et al., 2022).

While employers note that the use of these technologies can warn employees about, and even preempt, pending hazards and medical conditions, unsurprisingly employees across the various facets of order fulfillment operations have expressed an aversion to the use of some of these surveillance and tracking technologies (Wendt, 2023). These employees argue that these technologies can infringe on their privacy rights and often put them at risk of being the recipients of unfair, and sometimes unwarranted, punitive actions. Of note, employees and other stakeholders are anxious about how employers will collect and use biometric data (Guillot, 2019; Sheng, 2019) or use data that presents employee performance at a more detailed level. Further, there is substantial concern over the autonomous decision-making authority that these technologies will have in order fulfillment operations as they become more advanced and pervasively used (Dohrmann et al., 2022; Iyer, 2023). As these technologies proliferate, it is of utmost importance to understand how employee responses to their employers’ increased surveillance and monitoring activity will impact supply chain and order fulfillment operations.

Extant research on the response of employees to electronic performance monitoring in the workplace has determined that though the characteristics of the technologies themselves are important in influencing the reaction of individuals, context, personality, and other individual characteristics also influence the response to surveillance activity (Ravid et al., 2020). These individual characteristics can include locus of control, perception of control, trust in management, task difficulty, task complexity, individual skill, and aptitude (Ravid et al., 2020). Additionally, Ravid et al. (2020) found that personality, values, goal orientation, occupational and work characteristics, and organizational culture and climate each moderated the effect of electronic performance monitoring on employee responses. Differences in personality moderated the effects of monitoring and surveillance systems on individual reactions, such that individuals with lower levels of extraversion and emotional stability were less likely to have positive attitudes toward monitoring and surveillance activity. Individuals with higher levels of neuroticism were also less likely to perceive surveillance and monitoring activity in the workplace as being procedurally fair or legitimate.

Next, values also mitigated the effects of electronic performance monitoring on employee reactions. In studying whether individuals’ ethical orientations influenced their sensitivity toward potential privacy breaches within surveillance and monitoring systems, researchers found that ethical orientation influenced the perception of the invasiveness and appropriateness of such systems. One of these studies demonstrated that individuals with high levels of formalism had the strongest negative relationship between perceived privacy invasion and appropriateness of surveillance and monitoring systems (Alder, 2007). Of interest, individuals with high levels of utilitarianism were found to exhibit the strongest positive relationship between the perceived usefulness of surveillance and monitoring systems and organizational trust (Ravid et al., 2020).

Goal orientation was yet another moderator of the effect of electronic performance monitoring systems on employee response. Employee responses varied depending on the type of goal they found motivating (Ravid et al., 2020). For example, mastery goals, where the focus is on learning and personal improvement, differ significantly from performance goals, where the objective is to prove one’s ability and actively avoid the judgment of others. Interestingly, research focused on performance goal orientation found that employees with higher levels of avoid-performance goal orientation had more anxiety about evaluation apprehension and lower skill attainment when they believed their performance data would be reviewed at a future time. Alternatively, the study found that employees with higher levels of prove-performance orientation had increased levels of evaluation apprehension and lower skill attainment when they understood that their performance data would be reviewed in real time (Watson et al., 2013).

Occupational and work characteristics were other moderators of the effect of monitoring systems on employee response. Indeed, recent studies have shown that occupational types moderate the relationship between surveillance and monitoring systems and an employee’s trust in their management. Of note, those employees who engaged in jobs that were more manual in nature associated less trust in management with more surveillance and monitoring, while no such relationship existed for those employees in non-manual jobs. Another study found that individuals who reported having more empowering jobs were more likely to respond negatively to monitoring than those employees who perceived their jobs to have less autonomy.

The strength of the effect of electronic performance monitoring systems on employee response was also found to vary based on organizational culture and climate. Researchers found that the shared values and beliefs on which organizational culture is based can lead to the establishment of behavioral norms and expectations. Therefore, any corporate intervention that appears to not align with those shared values conjures negative reciprocity from employees. For instance, in highly bureaucratic cultures that are defined by clearly defined lines of authority and systems-based work, surveillance and monitoring systems were more welcomed. Further, it was also determined that the caring climate of a company moderated the relationship between the effect of electronic performance monitoring systems and employee response. For employees who perceived that they worked in a strong caring climate, the relationship between their attitude toward surveillance and monitoring systems and their intention to resist such systems was less negative than that observed for those employees who reported working in uncaring work climates (Ravid et al., 2020).

Customer Privacy in Retail Supply Chain Order Fulfillment

Retail customers can help improve order fulfillment process design by disclosing elements of their personal information and by agreeing that other forms of information about them can be collected. In retail fulfillment operations, retailers can utilize various forms of customer data to better manage inventory through enhanced demand forecasting and determining optimal inventory levels to hold based on extracted customer preferences. Additionally, customer demographic and behavioral data, like location and willingness to pay, can help develop optimal pricing strategies for value-add services like product put-away or assembly inside customer homes during delivery. Further, customer demographic and behavioral data can assist in designing more efficient delivery operations by segmenting customers by delivery speed, drop-off time window, and delivery vehicle type preferences, generating immense value for customers (Eikelmann et al., 2023; Mahoney & Dauer, 2021). Therefore, understanding retail customers’ propensity to disclose information is of paramount importance to retail order fulfillment operations. However, the scale to which technologies collect and share customer data can result in significant levels of customer concern (Dohrmann et al., 2022; Eikelmann et al., 2023).

Prior research has discovered that retail customers generally express concern around four factors related to privacy: unauthorized access, secondary use, errors, and collection (Aloysius et al., 2018). Researchers describe each of these factors. Unauthorized access refers to the extent to which customers are concerned about their personal information being available to unauthorized persons. Secondary use highlights the extent to which customers are concerned about the unjustified use of their information for purposes other than that for which it was initially intended. Errors are defined as the degree to which customers are concerned about both intentional and non-intentional errors that occur in the handling of their positional information. Collection describes the measure to which customers are worried about the amount of their personal information being collected by retailers (Aloysius et al., 2018). Indeed, the advent of advanced technologies able to capture significant volumes of consumer data bears significant information privacy implications for retail customers.

Retail customers’ perceptions of technology have a substantial effect on those technologies’ outcomes and use (Aloysius et al., 2018). Specific to retail stores, studies have found that customers do not always prefer the personalized services that advanced technologies facilitate due to their privacy concerns (Aloysius et al., 2018; Chellappa & Shivendu, 2010). Of note, retail customers’ privacy concerns adversely impact the wide-scale adoption of advanced retail and fulfillment technology (Aloysius et al., 2018; Venkatesh et al., 2017). Some of this concern is amplified when retail customers consider the data-harnessing capabilities inherent in the mobile devices on which they consistently conduct transactions (Cottrill & Thakuriah, 2015). Specific to online engagement, retail customers have expressed anxiety regarding data security in online shopping contexts, which has had an adverse effect on their willingness to disclose personal information (Aielloa et al., 2020). A study by Ingram (2017) revealed that 85% of consumers were unwilling to share their personal information if they had a concern about the use of that information by retailers and, further, 71% of these customers indicated that they would stop purchasing from a retailer if their information is gathered without their consent. This consumer sentiment is in no small way exacerbated by the continual and frequent news of data breaches and the mishandling of consumer data by large companies as of recent (Aielloa et al., 2020). While Chapter 6 discussed privacy concerns, the willingness to disclose personal information, the privacy paradox, and privacy calculus on users in general, this section applies these concepts to consumers in the omnichannel retail order fulfillment process.

Retail customers’ willingness to disclose information is a central concept in the study of customer privacy implications in the e-commerce and omnichannel retail contexts (Cottrill & Thakuriah, 2015; Eikelmann et al., 2023). Given this, various researchers have studied customers’ propensity to disclose information across a variety of retail contexts, understanding the significant effect it can have on order fulfillment process design and operations. These studies have found that customers’ willingness to disclose information can vary due to individual differences, consumer–company relationships, and retail setting contextual differences (Belk, 2013; Markos et al., 2017; Mothersbaugh et al., 2012; Li et al., 2015; Markos et al., 2018; Phelps et al., 2000). As retail customers determine which, and how much, information they are willing to share when engaging in the online purchasing journey, they make decision tradeoffs regarding the benefits and costs of doing so.

Privacy calculus theory conceptualizes the drivers and influences on how individuals compare the perceived risks and anticipated benefits associated with them divulging private data, or the collection and distribution of sensitive information. As a theory, privacy calculus has been utilized as a lens to interpret customers’ adoption or use of technologies in e-commerce social networking, and location-based mobile applications contexts (Leon et al., 2023). The theory posits that the trade-offs customers make in their privacy calculus can be contingent upon their cognitive resources, their attitude, cultural values, social norms, and various other situational and contextual factors (Leon et al., 2023). Privacy calculus theory is highly salient in omnichannel retail and order fulfillment operations. Retail customers do not only make decision tradeoffs when deciding the type and amount of personal information to disclose when engaged in the online purchasing journey; they also do so when deciding on order delivery modes and delivery service options.

The decision outcomes based on retail customers’ privacy calculus can impact order delivery operations in two substantive ways. First, the information that customers are willing to share while engaging in the online purchasing journey can determine the service levels offered by delivery service operations. For example, aggregated purchase frequencies, preferred delivery locations, channel preferences, delivery speed preferences, and various other forms of personal data captured online can be used to design distribution networks that deliver on customer expectations regarding, delivery time window, number of days to delivery, the frequency of delivery service, and the level of return services to offer. Second, given each of the aforementioned delivery service features is often enabled by various forms of customer-facing advanced distribution and mobility technologies, customers’ propensity to assess either high or low levels of information-related or spatial-related privacy concern is also a central dynamic in retail delivery operations.

Advanced technologies in delivery operations can take the form of autonomous delivery vehicles, autonomous ground vehicles with lockers, parcel lockers, droids, and drones, each mode resulting in varied levels of perceived privacy-related issues, which in turn have implications for delivery service planning and operations.

Smart lockers. Research on the use of smart lockers in retail fulfillment operations by Tsai and Tiwasing (2021) found that privacy security was an integral factor and service attribute in alleviating customer privacy concerns regarding control over their financial and personal information when using parcel lockers to access their delivered orders. Interestingly, prior studies also indicated that a benefit consumers associate with the use of smart lockers is the removal of human interaction during delivery transactions which facilitates the prevention of potentially sensitive information being collected and distributed by retail or delivery service provider personnel (Featherman & Pavlou, 2003; Tsai & Tiwasing, 2021; Wang et al., 2020).

Vehicle delivery. Extant research has found that customers can have varied levels of perceived data privacy the degree to which an individual is concerned about the collection and use of their data—when it comes to the use of connected, autonomous, or recording capability-equipped vehicles that are used in order delivery (Acharya & Mekker, 2021; Bridget, 2017). Further, customers have expressed levels of concern regarding the collection and use of shopping trip data generated by their personal vehicles or mobile devices, and that data being shared with retailers or other third parties (Acharya & Mekker, 2022a, b; Schmidt et al., 2016). This issue has recently come to the fore more vividly due to the growing practice of crowd-sourced delivery or “social transportation-driven” delivery services, where customers become temporary delivery service providers by picking up and dropping off the orders of other customers within the same social media network (Devari et al., 2017).

Drones. Of note, the use of drones (see Fig. 12.3) has garnered relatively more focus on privacy issues in fulfillment and last-mile delivery operations (Dohrmann et al., 2022; Scharf, 2019).

Fig. 12.3
A close-up photograph of a drone under surveillance.

(Image credit Alamy Stock Photo/Wavebreak Media)

Drone surveillance

Full size image

In the context of drone delivery, retail customers have expressed privacy-related concerns regarding the risks associated with drones being able to collect and record highly sensitive information regarding the personal lives of customers, often without the customers’ knowledge or consent (Iyer, 2023; Leon et al., 2023). Drone anxiety can be compounded by the fact that this technology is relatively new compared to other forms of order delivery technologies, and this adds an additional dimension to customer perceptions of potential privacy-related issues (Dohrmann et al., 2022; Leon et al., 2023). Privacy concern, which refers to the context-specific fears regarding the misuse, voluminous collection, unsecure storage, and unauthorized distribution of personal information, is highly salient in retail drone delivery operations (Dinev & Hart, 2006; Lankton et al., 2017). Yet, extant research has unveiled that the perceived usefulness of drone technology impacts what consumers comprehend to be the potential privacy harm associated with interactions with drones (Roca et al., 2009. Perceived usefulness refers to the degree to which a customer believes that using a particular system will enhance their performance in some way (Davis et al., 1989). In the context of drone technology, studies have revealed that customers can be more willing to overlook the perceived privacy risk associated with drone deliveries once they understand the benefits of those deliveries to be higher (Roca et al., 2009). Perceived privacy risk illustrates a combination of the perceived likelihood and impact of privacy harm (Choi et al., 2018; Li et al., 2016). Researchers have noticed that higher levels of perceived privacy risk negatively affect the drone delivery adoption intentions of retail customers (Leon et al., 2023; Yoo et al., 2018). Another highly salient concept in the context of order fulfillment by drone delivery is that of customer’s trust, defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other party will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (McKnight et al., 2002; Roca et al., 2009). In last-mile delivery operations trust becomes highly important in the perceived mitigation of privacy and safety risks as retailers and logistics service providers make deliveries to customers’ places of residence (Leon et al., 2023). Specific to drone delivery, prior studies have reported that higher levels of trust positively impacted customer intent to adopt drone delivery services (Leon et al., 2023; Zhang et al., 2019).

Summary

This chapter commenced by defining supply chain management, of which logistics management is a constituent, and then described order fulfillment activities within logistics activities. The focus was on the order fulfillment component of supply chain management, especially due to its central importance and high salience in omnichannel retailing. Noting that the effective and efficient management of retail customer orders through fulfillment operations is of central importance due to the rapid growth of e-commerce activity and the resulting emergence of omnichannel retailing, the analysis described how the increased complexity introduced by omnichannel retail supply chains is managed using advanced technology-enabled order fulfillment operations. The chapter detailed how the use of these advanced logistics technologies allows both retailers and logistics service providers to harness and analyze large-scale data sets that provide information on various human and process dimensions of retail fulfillment operations. Subsequently, this study surveyed the role that observation and transparency play in facilitating organizational productivity through facilitating increased organizational learning and operational control, describing their applicability in retail fulfillment operations. Importantly, the use of these technologies to gain transparency and operational efficiency gives rise to both employee-based and customer-based privacy issues in retail order fulfillment operations.

The chapter also discussed the applicability of work environment privacy and information privacy for both employees and customers engaged in retail order fulfillment operations and processes. As noted, several dimensions of work environment privacy exist, and they include visual, spatial, acoustic, and olfactory forms of privacy, each having ramifications in retail fulfillment operations contexts. The discussion also defined and debated information privacy and indicated its applicability to both employees and customers in retail fulfillment operations. For retail fulfillment operations employees, their propensity to accept tracking, surveillance, or monitoring technologies in the workplace can be contingent on their individual characteristics. These characteristics can be identified as personality, values, goal orientation, occupational and work characteristics, and organizational culture and climate.

In discussing information privacy as it relates to retail customers, the chapter singled out the four factors related to privacy that customers generally expressed concern over. These factors are unauthorized access, secondary use, errors, and collection. Additionally, privacy concerns impact customers’ willingness to disclose information to retailers. Various factors affecting customer willingness to disclose information include individual differences, consumer-company relationship, and retail setting contextual differences. Additionally, privacy calculus theory is applicable to retail customer information privacy perceptions regarding order delivery activity within retail fulfillment operations. There are two key impacts in this area, the first being that customer willingness to share information with retailers while engaging in the purchasing journey online can directly affect the knowledge retailers have and can use to customize and design delivery services that align with customer preferences. Second, the novelty of several advanced technologies used to deliver packages can be the source of customer privacy concerns. Among these technologies are smart parcel lockers and drones. This chapter concluded with thoughts about how perceived data privacy, privacy concern, perceived usefulness of technologies, perceived risk, and trust all play a role in the way retail customers apprehend privacy risk as it pertains to them interacting with advanced technologies in retail fulfillment delivery operations.