Frontline Service Employee Compliance With Customer Special Requests

Abstract

As companies struggle to deliver excellent service, many find they need to understand and plan for a diverse array of customer requests. Some requests are unexpected and require employees to go beyond their usual job duties. These requests may be classified as special requests. Knowing how and when to comply with these requests is critical to the firm and the employee, given that failing to comply could negatively affect customer satisfaction, while complying may produce unwanted consequences for the firm. We use grounded theory and content analysis of critical incident special requests from frontline employees to develop a framework and classification scheme that categorizes customer special requests and employee assessments of these requests. Customer special requests were classified into four types of customer deficiencies—physical resources, knowledge, financial, and time. Employee assessments were categorized as positive compliance factors (motivations and ability) or deterrents to compliance, including policy or legal, potential risk, and lack of resources. These findings contribute to theory, as they represent the first effort to categorize customer special requests and employee responses to them. Companies need to be better informed about the types of requests employees receive so that employees can make the most appropriate decisions.

Keywords

employee compliance employee decision making customer special requests employee motivations customer deficiencies

While employee service delivery has always been critical to the survival of the firm, today’s customers expect service providers to meet their customized needs and requests as never before (Karatepe 2006). In fact, Bitner, Booms, and Mohr (1994, p. 100) identified “employee response to customer needs and requests” as the most frequently mentioned factor relative to satisfactory service encounters, accounting for nearly one half of all satisfactory encounters. Some of these requests push employees to adapt their service delivery and/or to go beyond their typical service tasks. We call these requests “special requests.” The importance of customer special requests is not lost on firms. Special requests are so ubiquitous that many organizations have forms customers can complete or provide specific information online relative to such requests or needs. Indeed, many organizations, such as the Ritz-Carlton, are known for their capacity to meet unconventional customer requests (Berry and Bendapudi 2003). Googling the term “customer special requests in services” produces over 750 million hits. The industries most prominent in the search were airlines, hotels, and restaurants.

While service researchers have identified employee fulfillment of customer requests and needs as highly relevant for maintaining customer relationships (Ployhart, Weekley, and Ramsey 2009; Wang, Beatty, and Liu 2012), Strativity Group (2008) found that only 29% of executives believe that employees have the tools and authority necessary to solve customer problems and to delight customers. Further, the employee decision-making process relative to compliance with customers’ special requests has received almost no research attention. Thus, there seems to be a disconnection between the importance of this topic and the research attention given to it. While it is impossible to train service employees on every possible customer request (Rafiq and Ahmed 1998), understanding the kinds of special requests customers make and how employees respond to them is a topic of utmost importance to the field.

Many of these employee responses may be considered extra-role service delivery or customer-oriented behaviors (COBs), which have received some research attention (Bettencourt, Gwinner, and Meuter 2001; Maxham and Netemeyer 2003; Podsakoff and MacKenzie 1997). However, when the behaviors are based on a customer request, they are not voluntarily performed by the employee; instead, they are prompted by a customer’s request and an employee’s decision. This specific perspective on COBs is the focus of this research.

We define a customer special request as a customer request that falls outside of the frontline employee’s normal or expected job duties. Thus, these requests require the employee to engage in careful consideration and evaluation. Even the perception of whether the requested activity is inside or outside of the employee’s job duties is subjective. These perceptions also likely vary across employees and between management and employees. Further, customers often do not know whether the request falls inside or outside of the employee’s or the company’s typical parameters.¹ While service researchers have touched on the topic of customer requests in the past (e.g., Beatty et al. 1996; Bitner, Booms, and Mohr 1994), Wang, Beatty, and Lui (2012, p. 76), among others, call for further study of this topic.

Given the lack of research on this topic and a strong need for firms to understand customer special requests and how employees make decisions relative to these requests, we focus on four major questions: (1) What is the nature of these customer special requests and how can they be meaningfully categorized so as to be helpful to marketers? (2) When faced with these requests, what factors encourage or deter employee compliance? (3) Which types of requests are employees most likely to comply with and why? and (4) How do customer special requests and subsequent employee responses converge or vary across two typical service companies in different industries?

To accomplish our goals, our article proceeds as follows. First, we provide a background to the topic, including an overview of relevant literature including customer-oriented extra-role behaviors and employee decision making. Then, we describe our two-stage data collection. Next, we describe the procedures involved in the coding and analysis of our critical incident technique (CIT) data, using grounded theory (Strauss and Corbin 1998), and present our developed framework and classification of special requests based on customer deficiencies and employee assessment of these requests relative to complying or not. Then, we report on how types of customer deficiency requests affect employee assessments and compare findings for the two companies. Finally, we address contributions to theory, implications for management, and limitations and future research.

Background

Extra-Role and Customer-Oriented Behaviors

In the marketing literature, customer requests are usually viewed as part of a customer orientation strategy (Hartline, Maxham, and McKee 2000). As such, customer requests are often encompassed within terms such as customer needs (Kelley 1992) or service customization (Bitner, Booms, and Tetreault 1990); however, no work appears to have categorized types of customer requests. Further, responding to a customer’s request that falls outside of typical job expectations may be viewed as a type of extra-role behavior. Extra-role behavior is defined as employee behavior that goes beyond role prescriptions, is not formally rewarded, and is not punished if not performed (Van Dyne and LePine 1998). Several researchers argue that most of the work done on extra-role behaviors and organizational citizenship behaviors (OCBs), which are closely related, address employee prosocial or helping behaviors toward the firm and/or other employees but not toward the customer (MacKenzie, Podsakoff, and Ahearne 1998; Podsakoff and MacKenzie 1997).

In an exception to much of the OCB work, Podsakoff and MacKenzie (1997) coined the term customer-oriented behaviors (COBs) to reflect customer-oriented employee extra-role behaviors. Also, MacKenzie, Podsakoff, and Ahearne (1998) noted several studies that provide evidence as to the important effects of COBs on unit performance, such as Kizilos and Cummings’ (1996) study showing that employee customer-centered behaviors were positively related to sales. Further, researchers have conceptualized three customer-oriented behaviors: loyalty, participation, and service delivery (Bettencourt, Gwinner, and Meuter 2001). For example, Maxham and Netemeyer (2003), studying service delivery extra-role behaviors in service recovery, found that employee views of organizational justice and shared values positively influenced customer perceptions of employee behaviors. Additionally, Bettencourt, Gwinner, and Meuter (2001) found that service orientation and empathy were the best predictors of contact employees’ service delivery OCBs across several service categories. These studies encourage further study of COBs and focusing on this topic relative to customer special requests should thus fill an important gap.

Employee Decision Making

A number of factors may influence an employee’s willingness to comply with requests outside of his or her normal job expectations. Wang, Beatty, and Liu (2012) identified four categories of factors affecting employee compliance with a “fuzzy return request” (a retail return somewhat outside of policy limits). They identified employee factors (customer orientation and conflict avoidance), customer interaction styles (affiliative vs. dominant style), the return legitimacy of the request (Autry, Hill, and O’Brien 2007), and organizational factors (employee flexibility and expectations of punishment). Wang and her colleagues found that all of these factors influenced employee compliance with fuzzy return requests relative to either the compliance process or the outcome, except expectations of punishment. However, this study focused on a narrow treatment of one specific special request—retail returns.

Chebat and Kollias (2000) found that adaptability affected employee performance (in-role and extra role) more strongly than self-efficacy and job satisfaction, stressing the importance of employees’ freedom to adapt their actions to customer needs (or requests). Wilder, Collier, and Barnes (2014) also addressed the importance of frontline employees adapting or customizing a service to meet customer needs in a competitive marketplace. Further, Bone and Mowen (2010) argue that companies need to train employees on the handling of discretionary decisions.

Finally, Gudergan and Lings (2010) address the decision-making process of frontline employees relative to effort expended in Business To Business (B2B) service delivery. They addressed three influential factors: perceived risk of the situation, the individual’s risk-taking tendency, and their compliance tendency, defined as tendency to deliver valuable and satisfying services to customers and meet service agreement levels (formal and informal).

Thus, effort, adaptability or script flexibility, and risk concerns add to the constructs outlined by Wang, Beatty, and Liu (2012) to represent important constructs that may be relevant to this study. Finally, D. E. Bowen and Lawler (1992) suggest that there are times when creative rule breaking (i.e., going beyond job expectations) is good, but there are also times when empowerment can create major problems for the organization. They argue that firms need to carefully evaluate how much empowerment is given to employees. We now turn to our method.

Method

Exploratory Efforts and Company Descriptions

We completed a series of studies moving from more qualitatively rich to a more quantitatively rich exploration (see Figure 1 for the steps in the data collection). We spoke with employees, supervisors, and senior management at two large firms in the Southeastern United States—an oil-change company and a pest-control company. Both companies have excellent customer service and can be classified as group two companies based on J. Bowen’s (1990) classification. That is, both services involve moderate customization, moderate importance of employees, and a moderate commitment of time.

Figure 1.

Data collection steps.

The first company is an oil-change service provider conducting on-site service at over 190 corporate owned and franchise locations across 12 states. The second company ranks in the top 10 in size within the pest-control industry, with over 1,200 employees and 32 district offices operating across four states.

Collecting data from employees in two different firms, albeit in the same classification group, allows for an ability to generalize to these types of firms as well as a consideration of differences. Some notable differences exist between the service firms. Employees of the oil-change company must efficiently change the oil of automobiles while also inspecting the mechanical operation of the engine, transmission, and/or tires. They also engage in other limited automobile repair or maintenance work. They work at a specific location, and customers come to them so the service is performed in the employees’ work context with supervisors nearby, while the pest-control employees work in the customer’s home. Further, the oil-change employees have less direct contact with customers and generally do not have ongoing relationships with customers, while one-on-one contractual relationships exist in the pest-control interactions.

The topic warrants exploratory research as the first step, given our desire to understand a phenomenon that has received limited attention (Lincoln and Guba 1985; Strauss and Corbin 1998). In addition, depth interviews allow for a view of the topic from the employee’s perspective. In the exploratory phase, we first conducted depth interviews with six executives (three from each company) and read stacks of company newsletters and customer “love letters.” These efforts allowed us to understand the companies’ values and policies relative to the topic, resulting in the following observations.

In both companies, management made many references to being “family” and treating customers and employees as family. At both companies, management talked about employing people who will care about their customers and who share the company’s values.

Technicians are instructed to treat every customer as you would a family member. If you would recommend it to your mom, you should recommend it to your customer. Do unto others as you would have them do unto you. (Director of Human Resources, Oil-change company)

Further, no policies or procedures exist for most customer special requests at the companies studied due to the wide range of request possibilities. Management at each company talked about employees making their own judgment calls. These companies allow their employees a great deal of flexibility in handling customer requests and encourage employees to address customers’ needs and requests within the constraints of company policy and legality.

We empower our technicians to make that call. We train them to be efficient and effective, and to communicate with their customers … there is some subjectivity to this work. (Director of Business Development, Pest-control company)

In addition, both companies encourage and reward employees who go above and beyond to help customers. At the pest-control company, a company-wide newsletter recognizes employees for exceptional service, while at the oil-change company numerous awards are given to employees who show outstanding levels of customer service.

The company newsletter includes a section entitled “Applause” which features letters from customers received by the company, outlining extraordinary acts of kindness and service. This serves to emphasize the importance of service and highlights individual employees for their successes every month. (Vice president of Sales and Service, Pest-control company)

Clearly, these organizations encourage their employees to go the extra mile, and employees feel their bosses will condone their actions as long as they fit within a loose set of parameters defined by the companies. Thus, these companies appear similar relative to their customer-oriented, service-driven culture. Employees feel empowered to make decisions based on their best judgment as to what is good for the company and their customers—perhaps somewhat more so in the pest-control context than in the oil-change context.

Next, two of the researchers conducted 18 depth interviews with frontline customer-contact personnel in both companies to begin to understand this topic. All participants were male, and the average age was 37, with an age range between 18 and 56. The average length of employment was 6 years, ranging from 3 months to 21 years. The interviews followed a semistructured format in which the interview guide served as a starting point. The questions included obtaining details of prior special request incidents and the employee or supervisor behaviors, reactions, and emotions in regard to the requests. Participants set the course of the interview by telling stories of their customer encounters, with the interviewers speaking up as necessary to ensure coverage of the relevant points. The audiotaped interviews lasted on average 30 minutes and were transcribed.

Using grounded theory, the themes emerged directly from the data (Strauss and Corbin 1998). After transcription, each researcher coded the interviews following the coding schema set forth by Corbin and Strauss (2008). Thus, the researchers initially read and coded all interviews independently and used these materials to develop broad content dimensions and categories. Then, the researchers compared the dimensions and categories each developed. Differences were discussed and resolved, and the dimensions and categories were condensed for further coding. Based on the developed themes and subthemes, the researchers again analyzed and coded the interviews. Finally, we used member checks to ensure the trustworthiness of the findings (Lincoln and Guba 1985). This step involved contacting several respondents after the interviews to verify the ideas drawn from the interviews, with no inconsistencies identified.

During the exploratory stage, one theme (or dimension) that emerged was that customer special requests could be categorized based on a customer’s primary deficiency, which is defined as “a lack of something that is needed; the state of not having enough of something necessary” by the Merriam-Webster dictionary (2014). Specifically, customers turn to employees for help with problems that they cannot easily solve themselves due to lack of resources (physical, financial, or time deficiencies) or a lack of knowledge about the service or potential solutions to the problem (knowledge deficiencies).

Then, employees assess the requests and make decisions about whether they should or will comply with the request based on their motivation and abilities as well as other relevant situational deterrents. The positive factors uncovered include the internal motivations of an employee (e.g., desire to help the customer) and the employee’s assessment of his ability to comply. This categorization is similar to the MOA framework (motivation, opportunity, and ability) developed by MacInnis, Moorman, and Jaworski (1991). While motivation and ability are manifested in the data, opportunity did not arise. Conversely, we ascertained that employees did not comply when organizational and/or situational deterrents were present (e.g., policy or legality, availability of resources, as well as perceptions of risk).

Next, we turned to larger scale data collection, still using the same set of research questions, using frontline employees from these same firms to facilitate an assessment of the classifications initially developed and the prevalence of these categories.

Primary Data Collections

For our primary data collection, we surveyed frontline employees from the two companies, focusing on their retrospective accounts of customer special requests and their responses to these requests. This approach utilizes CIT, which was subsequently analyzed with content analysis (Gremler 2004). Using this method, we obtained detailed accounts of customer requests and employee responses, which were subsequently coded to reveal basic categories in the data. These categories or themes were consistent with the initial categories developed from the depth interviews.

Data collection for each company was handled differently due to differences in company operations that presented some challenges. Computers were not available in the oil-change operation for employees to use. Further, these employees expressed interest in verbally recounting incidents of customer special requests, which allowed them to recount what they felt to be a more complete story with greater ease. Therefore, for oil-change employees, data collection involved individual interviews with employees that were recorded and later transcribed; respondents also filled out some parts of the survey themselves, such as classification questions. Many of the pest-control technicians, however, preferred an online survey. To increase response rates, technicians were given the option of taking the survey online or by paper and pencil. The pest-control offices provided computer access for their employees to take part in an online survey or a paper survey if desired. Employees detailed their incidents of customer special requests, answering the same questions as those included in the oil-change data collection. Using the methods most preferred by the firms and employees ensured very high response rates (i.e., 88% in both companies), which helped offset concerns relative to different data collection methods. Support exists in the literature for comparison of data collected by different means by demonstrating that little difference may exist between methods such as online, interview, and paper-and-pencil surveys (e.g., Sethuraman, Kerin, and Cron 2005). Further, in a comparison of responses across methods, no appreciable differences were detected in the data.

Table 1 presents the demographics of the two samples. In the case of the oil-change firm, the sample was all male. The oil-change sample is predominantly between the ages of 30 and 49 years (42%) and is highly representative of the 19- to 29-year, age-group (51%). The pest-control employees were also all male, with the majority of employees between the age of 30 and 49 (60%). Years of experience with the firms range from less than 2 years to greater than 10 years. Further, the frequencies with which employees receive these types of requests were concentrated in the few times a week to the few times a month range in both companies, while some pest-control employees indicated they receive these types of requests daily (19%). Finally, in regard to the gender of the customer making the request, interestingly, employees recall complying more often with requests made by females than by males (p < .01, based on a χ² analysis) in both data sets.

Table 1.

Sample Characteristics for Primary Data Collection.

Employee Characteristics	Oil Change		Pest Control
Employee Characteristics	n = 99		n = 184
Gender
Male	100%		100%
Female	0%		0%
Race
White/Caucasian	82%		90%
African American	14%		6%
Other	4%		4%
Age
19–29 years	51%		20%
30–49 years	42%		60%
≥50 years	7%		20%
Length of employment
<2 years	53%		27%
2–5 years	22%		36%
6–10 years	2%		21%
>10 years	23%		16%
Customer special request
Frequency of special requests
Every day	0%		19%
A few times a week	48%		21%
A few times a month	45%		26%
Every few months	6%		22%
Hardly ever	1%		12%
Customer gender	Comp.	NonComp.	Comp.	NonComp.
Male	(34) 47%	(39) 53%	(55) 44%	(70) 56%
Female	(45) 75%	(15) 25%	(89) 65%	(47) 35%
	χ²(1) = 9.89; p < .01		χ²(1) = 11.26; p < .01

Note. Comp = compliance situations; noncomp = noncompliance situations; customer request sample size = number of incidents.

Data collection in the oil-change firm

The first primary data collection was conducted by five graduate students. The students were trained thoroughly via mock interviews, and the researchers worked closely with the students to provide feedback during the interviewing process. Data were gathered in one-on-one interviews during in-store visits. Respondents were first personally interviewed (one on one) and then filled out additional questions on a paper survey (e.g., classification questions).

First, the researchers conducted six pretest interviews. Local supervisors identified frontline employees willing to participate. Then, both supervisors and employees from two different branch locations were interviewed to ensure there were no problems with the questions as well as to assess the work situations the interviewers would face.

After pretesting, student interviewers in groups of two or three made site visits to collect the data. The interviewers called on 33 corporate branch locations and attempted to interview all frontline employees working that day in the chosen locations.

The interviewers first provided each employee with the researchers’ definition of a special request along with an example.² Then, they asked the employee to think of and describe two recent memorable situations in which he had encountered such a request. In one case, the interviewer asked about a situation in which the employee had complied, with the second situation involving noncompliance. The interviewers randomly varied the order in which the two sets of questions were asked. For each event, the interviewers asked participants to describe the request, why they complied or not, how they felt about it, whether or not they discussed it with their supervisor (before or after making their decision), as well as other descriptive questions. Additionally, some descriptive data on the customer were obtained (e.g., gender).

Some employees could recall only one situation, and more often than not, this was a compliance situation. The employee responses were audio recorded and later transcribed for the open-ended portion of the survey. The final data set for the oil-change company consisted of 133 incidents (79 compliance and 54 noncompliance) collected from 99 frontline employees (technicians) in 33 branches of this company. The response rate was 88%, with 99 employees of the 112 asked, agreeing to participate.

Data collection in pest-control firm

Next, data collection with employees in the pest-control company occurred. After pretesting an online version of the same questions as asked in the oil-change interviews with three employees and two supervisors, a letter from management was sent to a subset of frontline employees (service representatives) asking them to participate in the survey and giving them a choice to participate online or with a paper survey. This subset was selected randomly from the population of frontline employees who work directly with residential customers. The goal was to obtain the same type of data as that obtained from the oil-change employees. After seeing a definition of special requests (identical to the one used with oil-change employees), respondents were asked to report on two recent memorable special customer request situations involving either compliance or noncompliance in detail (asked in random order). Respondents were less likely to think of a noncompliance situation than a compliance situation. They described the incident, how they responded to it, how they felt about it, and responded to other questions identical to the questions asked of oil-change employees.

Of the 210 requests, 184 frontline employees filled out the survey (124 online surveys and 60 paper surveys), with a response rate of 88%. The data included 153 compliance and 123 noncompliance situations. After removing 15 incidents (but not full surveys) due to missing data, the final sample included 144 compliance incidents and 117 noncompliance incidents.

Data Analysis

Categorization occurred first with the oil-change employee data, and then the process was repeated with the pest-control data, using the incident classification system recommended by Bitner, Booms, and Tetreault (1990). The two coders simultaneously went through an analytic induction procedure, each carefully reading the data and placing similar incidents into categories. The coders met to discuss the similarities and differences between each category created. After agreeing on the list of categories, the two coders met with the other coauthors to get feedback on the categories established and to reach final agreement.

The researchers then wrote detailed coding rules and definitions to allow precise categorization of the incidents. Two new coders then coded one fourth of the incidents independently, using the coding instructions as a guide. Then, these coders met with the two original coders to determine whether the incident classification was working and to resolve any conflicts. After slight refinements of the definitions and conflict resolution of incident placement, the coders coded the remaining data, with a third coder resolving any disagreements. Interjudge agreement was 90% for the compliance examples and 85% for the noncompliance examples. These reliabilities are similar to the averages in comparable critical incident studies in service research (see Gremler 2004). Reliability was further assessed using the index of reliability (I_r ) method proposed by Perreault and Leigh (1989). This index includes the reliability of the entire coding process rather than just the interjudge agreement between coders. Thus, the compliance examples and noncompliance examples were combined into a single, overall reliability estimate. Additionally, this assessment estimates the reliability “that might be expected given a true (population) level of reliability” (Perreault and Leigh 1989, p. 140). Finally, as I_r is a statistical estimate of the reliability of the coded observations, the confidence interval of this estimate is also calculated (Perreault and Leigh 1989). I_r is estimated as .93, with a confidence interval of [.89, .97].

Coding for the pest-control data followed a similar procedure, ascertaining first whether the categories were the same in this data set as in the oil-change data. Noting that the categories were essentially equivalent, categorization proceeded with this second set of data, with findings reported side-by-side and often compared. Interjudge agreement for this data set was 93% for the compliance examples and 87% for the noncompliance examples. I_r is estimated as .94, with a confidence interval of [.91, .97].

Findings

Framework

Based on both the exploratory depth interviews and the themes identified during the coding of the CIT data, several major dimensions and categories emerged: customer deficiencies (leading to the request); employee assessment of the request (addressed relative to positive factors [motivations and abilities] or negative factors [or deterrents]); and finally, the employee’s decision (comply or not). Figure 2 shows the process involved and the classifications created, which are elaborated on in the following sections.

Figure 2.

Special requests and employee assessment process and classification.

Customer Deficiency Factors

Four customer deficiencies (limitations or restrictions) formed the basis of customers’ special requests: physical resources/equipment, knowledge, financial, and time. Definitions of each deficiency along with examples appear in Table 2.

Table 2.

Definitions and Qualitative Examples of Customer Deficiency Factors.

	Compliance		Noncompliance
Customer Deficiencies	Oil Change	Pest Control	Oil Change	Pest Control
Physical resource: The customer makes a request due to a lack of physical resources (e.g., strength, expertise, and capability) or equipment	A customer requested that the technician inflate a swimming pool she was taking home	A customer asked her technician to change out the air conditioner filter under her house	A regular customer came into the location and asked for coolant	A customer requested that the technician help her move some furniture
Knowledge: A customer makes a request due to a lack of knowledge about the service. The customer is unaware of the service script, services offered, or some other aspect of the service	A customer was unaware of store hours and asked the store to stay open to service her car	A customer requested certain areas of her yard be treated that were outside the service contract area	A customer came in to have all her vehicle fluids checked and wanted to know if the technician would wash and detail her car as well	A customer wanted treatment put down around a structure that was not under coverage
Financial: A customer makes a request due to a lack of financial resources or lack of willingness to pay for the service	A customer did not have the money for a tow; he asked if an employee would come check his battery	A customer requested his check be held a few days because the funds were not yet available	A customer requested the employee put brakes on for him after store hours so he wouldn’t have to pay	A family friend called and asked the technician to do a treatment “on the side”
Time: A customer makes a request due to a lack of time or due to a scheduling conflict	A teacher requested that they pick up her car from work and change the oil	A customer requested a late arrival time, after 5 p.m. at her home	A customer wanted to drop his car off for service and get ride home instead of waiting	A realtor wanted to falsify the infestation report to the state so as to close the sale of property that day

Table 3 presents percentages of customer deficiency factors reported in both data sets by compliance and noncompliance, along with the results of a contingency assessment in which the three two-way assessments are tested, resulting in two significant χ²’s at p < .05. First, the significant customer deficiency by company finding suggests that deficiency frequencies vary across the two companies. Customers, like companies, have scarce resources. Customers may not own the equipment necessary for a task or have the physical strength to complete a task, and therefore may ask the service provider for help with the task. Physical resource requests was the number one mentioned request type for oil-change respondents (38.4%) and second most mentioned for pest-control respondents (34.9%; Row 1 of the data).

Table 3.

Customer Deficiencies by Compliance/Noncompliance.

	Employee Response
Customer Deficiencies	Oil Change			Pest Control
Customer Deficiencies	Compl.	Noncompl.	Overall (% of Total)	Compl.	Noncompl.	Overall (% of Total)
Physical resource	43 (84.3%)	8 (15.7%)	51 (38.4%)	74 (81.3%)	17 (18.8%)	91 (34.9%)
Knowledge	17 (44.7%)	21 (55.3%)	38 (28.6%)	44 (41.1%)	63 (58.9%)	107 (41.0%)
Financial	3 (10.7%)	25 (89.3%)	28 (21.1%)	8 (20.0%)	32 (80.0%)	40 (15.3%)
Time	16 (100%)	0 (0%)	16 (12.0%)	18 (78.0%)	5 (24.1%)	23 (8.8%)
N % of total	79 (59.4%)	54 (40.6%)	133	144 (55.2%)	117 (44.8%)	261

Note. Two-way (customer deficiencies by company): χ²(6) = 13.26; p < .05. Two-way (customer deficiencies by compliance): χ²(6) = 126.34; p < .01. Two-way (compliance by company): χ²(4) = 7.24; not significant.

With service offerings becoming more customized (Mittal and Lassar 1996) and the growing empowerment of employees, service provision may be inconsistent, producing customer confusion as to the service provider’s duties (D. E. Bowen and Lawler 1992). Further, customers may not understand the range of services available or the firm’s limitations (Collier and Kimes 2013). Additionally, service contracts may be complex or ambiguous or the customer may not read the contract, causing him or her to ask for something outside the normal range or expectations (cf. Matzler and Waiguny 2005). Lack of knowledge was the most frequently mentioned request factor for pest-control respondents and the second most frequently mentioned factor for oil-change requests (41.0% for pest control; 28.6% for oil change). The higher lack of knowledge for pest-control versus oil-change respondents is the primary contributor to the deficiencies by company effect. These differences are probably due to the greater potential misunderstandings in the pest-control service versus the oil-change service due to the service contract and its more complex nature.

Money and time are also scarce resources for customers. For example, customers may make a time-related special request due to their busy schedule or they may ask for a financial break or special deal. Financially based requests represented 21.1% of the oil-change responses and 15.3% of the pest-control responses mentioned, while time-based requests represented the smallest number of requests mentioned (12.0% for oil-change; 8.8% for pest-control). Most likely the higher percentages of these requests in the oil-change responses were due to the setting—oil-change customers pay per visit, while pest-control customers are on a billing cycle; also oil-change customers may experience more time pressure than the pest-control customers because their issues may be less scheduled (e.g., a car breaking down).

Next, given the significant relationship between customer deficiencies and compliance, we examined the customer deficiency request factors that employees were most (and least) likely to comply with in each company.³ While compliance rates followed a consistent pattern across customer deficiencies in both companies, the big differences occurred in the stronger compliance for both employee data sets for time-based (fourth row across: oil = 100%, pest = 78.0%) and physical resource-based requests (first row across: oil = 84.3%, pest = 81.3%) and the stronger noncompliance for financially based (third row across: oil = 89.3%, pest = 80.0%) and knowledge-based requests (second row across: oil = 55.3%, pest = 58.9%). A pattern emerging here is that of lower compliance to financially based requests, which often involved nonlegitimate requests, and knowledge-based requests, which often involved requests against policy or illegal, beyond the employee’s abilities, or beyond the company’s scope.

Employee Assessments: Positive and Negative Factors

Next, the employee assessed the request,⁴ and if he complied, his explanations focused on the positive factors relative to complying, with two primary categories uncovered: (1) the employee’s desire to help and (2) ease or ability to comply. While desire to help is defined as goal-directed arousal, ability (or ease) refers to a person’s willingness and/or proficiency to perform the task (MacInnis, Moorman, and Jaworski 1991).

If he did not comply, explanations focused on the negative aspects or deterrents to complying. Three primary deterrents emerged: (1) the request was against policy or was illegal, (2) there was a lack of physical resources (time, physical ability, services offered, store layout, equipment available, etc.), and/or (3) the request was perceived to be risky or presented liability or safety issues for the customer, employee, or company.

Respondents sometimes mentioned several factors rather than just one—thus, the data in Table 4 illustrate frequencies, the percentage of times each category was mentioned across incidents (compliance or noncompliance), and when more than one category was mentioned.

Table 4.

Employee Assessment by Compliance by Company.

	Compliance		Noncompliance
Employee Positive Factors	Oil Change (n = 79)	Pest Control (n = 144)	Oil Change (n = 54)	Pest Control (n = 117)
Desire to help	68 (86.1%)	131 (91.0%)	—	—
Ease of complying	25 (31.6%)	35 (24.3%)	—	—
Both factors mentioned	14 (17.7%)	22 (15.3%)	—	—
Employee deterrents:
Policy/legal	—	—	31 (57.4%)	80 (68.4%)
Lack of resources	—	—	17 (31.5%)	29 (24.8%)
Risk	—	—	11 (20.4%)	29 (24.8%)
Multiple deterrents	—	—	5 (9.3%)	21 (17.9%)

Note. Percentages represent the frequency of respondents mentioning each variable across incidents.

Employees at both companies suggested that desire to help was the primary reason for complying (first row: oil change = 86.1%, pest control = 91.0%). This motivation reflects an employee’s desire to help the customer, the company, himself, or a combination of these factors. Employees appear to help customers due to their altruistic or helping tendencies (Van Dyne and LePine 1998) or their desire to do good for others (i.e., the golden rule). This is consistent with the values of the companies involved and the types of employees they hire. Only three employees mentioned wanting to help themselves, perhaps due to the nature of the companies, individuals, and that these jobs do not involve quotas or commissions. Also, self-reflection probably reduces the likelihood of admitting doing something for self-serving reasons. Further, even when desire to help the company came up, it was often in conjunction with a desire to help the customer. As an example comment, “The customer needed help because she was elderly, and I wanted her to know that our company is all about caring for our customers.” Consequently, in these situations, the employee wanted to help the customer while also promoting goodwill for the company. Several comments from the depth interviews illustrate this idea:

It would be kind of strange if nobody helped him out. And that’s how we came to the decision of, you know, you’re not really supposed to do it, on the other hand you don’t want to see the guy sitting up there in the middle of the road stuck, so I got him and my assistant manager and a couple of other guys we all got behind his car pushing him. (Jack, age 28; branch manager for oil-change company)

I’m a Christian and I believe that’s what Jesus would do, so … that’s why I do it. And it’s just one thing, you don’t even have to say anything just “Okay, I’ll do it.” (Ed, age 27, termite technician)

The other positive compliance factor was ease of compliance, which was mentioned in 31.6% of the compliance situations involving oil-change employees and 24.3% of the compliance situations involving pest-control employees. Employees discussed requests that could be easily performed. Employees said that they simply “could” do it or they had the ability to do it. The following quotes from the depth interviews indicate ability, while the second quote reflects the “giving or helping” spirit, noting that many respondents mentioned both factors:

A customer asked me to put her new car tag on for her since she didn’t have a wrench. It only takes a minute so I put it on for her. (Jeff, age 31, oil-change technician)

One time I had an older couple who had just gotten a new washer and dryer and the husband was in a wheelchair. He asked me to go under there, put the vent up there, so I spent 15 or 20 minutes doing that. I did it because I knew he couldn’t get under there by himself. I don’t mind doing stuff like that. (Bob, age 28, pest-control technician)

On the contrary, an employee may be deterred from complying with a customer special request if he assesses that there are problems with the request relative to company policy or legality, he believes that he does not have the resources necessary to perform the operation, or if he views safety or risk issues to himself, the firm, or the customer.

The most prevalent negative factor, producing noncompliance, in both companies involved requests that employees considered to be in violation of company policy or possibly illegal (first row under deterrents: oil change = 57.4%, pest control = 68.4%).

A customer once asked me for a ride back into town when I was finished with his service. But we can’t have anyone in our trucks with us—it’s against company policy and we can get into a lot of trouble. (Nick, age 39, pest-control technician)

A customer wanted me to come to his house and change his oil for a cheaper price. He kept offering to pay me “under the table” for the work. (Rick, age 22, oil-change technician)

The second most predominant deterrent for compliance with special requests involved the employee’s inability to fulfill the request due to lack of resources. That is, the employee or the operation/location did not possess the physical resources (equipment, time, skills, etc.) necessary to complete the customer request. This deterrent was mentioned by 31.5% of oil-change employees and 24.8% of pest-control employees (second row under deterrents).

A customer once asked me to clean out his gutters before spraying around the area. I told him that I didn’t have a ladder or anything to clean them out with and I wouldn’t have enough time before my next appointment anyway. (Eric, age 36, pest-control technician)

The third noncompliance factor involved employees’ perceived risk relative to the request. This finding is consistent with Gudergan and Lings’ (2010) emphasis on the influence of risk on employee effort. These situations include cases where the employee felt there were potential liability issues to the company or a safety concern to themselves or the customer. Oil-change employees mentioned this factor in 20.4% of the noncompliance situations, while pest-control employees mentioned it 24.8% of the time (third row under deterrents).

A customer asked me to inspect their entire attic for rodents, but I had to deny inspecting the attic because it wasn’t floored. We can’t go into an unfloored attic space because it’s unsafe for us to be up there and could even hurt the customer or mess up our customer’s property. (Jason, age 41, pest-control technician)

Customer Deficiencies and Employee Assessments

Next, Table 5 illustrates the nature of the relationship between compliance factors and customer deficiencies. Noting a high number of expected cell sizes below five involving financial and time deficiencies, a contingency analysis including these cases would not be meaningful (McDonald 2014). Furthermore, since the physical resource and knowledge cases represented 80% of the total, a contingency assessment eliminating the cases involving financial and time cases was warranted. As shown in Table 5, the three two-way assessments were not significant. These findings indicate that relative to the two customer deficiency factors (physical resources and knowledge), there are no relationships between (1) customer deficiencies and positive compliance factors, (2) customer deficiency factors and company, or (3) compliance factors and company. These nonsignificant relationships support the generalizability of the findings across two different industry representatives.

Table 5.

Compliance Situations: Customer Deficiencies and Employee Positive Factors.

	Employee Positive Factors
Customer Deficiencies	Oil Change				Pest Control
Customer Deficiencies	Desire to Help	Ease	Both	Overall (% of Total)	Desire to Help	Ease	Both	Overall (% of Total)
Physical resource	24 (57.1%)	6 (14.3%)	12 (28.6%)	42 (53.2%)	56 (75.7%)	5 (6.7%)	13 (17.6%)	74 (51.4%)
Knowledge	11 (61.1%)	5 (27.8%)	2 (11.1%)	18 (22.8%)	33 (75.0%)	4 (9.1%)	7 (15.9%)	44 (30.5%)
Financial	3 (100.0%)	0 (0.0%)	0 (0.0%)	3 (3.8%)	5 (62.5%)	2 (25.0%)	1 (12.5%)	8 (5.6%)
Time	16 (100.0%)	0 (0.0%)	0 (0.0%)	16 (20.3%)	15 (83.3%)	2 (11.1%)	1 (5.6%)	18 (12.5%)
N % of total	54 (68.4%)	11 (13.9%)	14 (17.7%)	79	109 (75.7%)	13 (9.0%)	22 (15.3%)	144

Note. Run without financial and time: two-way (customer deficiencies by company): χ²(3) = 2.28, not significant; two-way (customer deficiencies by positive factors): χ²(4) = 3.34, not significant; two-way (positive factors by company): χ²(4) = 7.68, not significant.

In Table 6, for the noncompliance situations, we assess the relationship between employee deterrents and customer deficiency factors. In this analysis, we only eliminated the time cases, which represented 2% of the total cases. The three two-way contingency analyses were significant at p < .01.

Table 6.

Noncompliance Situations: Customer Deficiencies and Employee Deterrents.

	Employee Deterrents
Customer Deficiencies	Oil Change					Pest Control
Customer Deficiencies	Policy/Legal	Risk	Lack of Resources	Mult.	Overall (% of Total)	Policy/Legal	Risk	Lack of Resources	Mult.	Overall (% of Total)
Physical resource	3 (27.3%)	1 (9.1%)	3 (54.5%)	1 (9.1%)	8 (15.7%)	4 (23.5%)	2 (11.8%)	6 (35.3%)	5 (29.4%)	17 (14.5%)
Knowledge	5 (25.9%)	7 (25.9%)	6 (33.3%)	3 (14.9%)	21 (38.6%)	51 (81.0%)	4 (6.3%)	0 (0.0%)	8 (12.7%)	63 (53.8%)
Financial	17 (66.7%)	5 (20.0%)	1 (3.3%)	2 (10.0%)	25 (42.8%)	20 (62.5%)	2 (6.25%)	2 (6.25%)	8 (25.0%)	32 (27.4%)
Time	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	2 (40.0%)	1 (10.0%)	2 (40.0%)	0 (0.0%)	5 (4.3%)
N % of total	25 (42.9%)	13 (21.4%)	10 (24.3%)	6 (11.4%)	54	77 (65.8%)	9 (7.7%)	10 (8.5%)	21 (18.0%)	117

Note. Run without time: two-way (customer deficiencies by company): χ²(8) = 29.22, p < .01; two-way (customer deficiencies by deterrents): χ²(12) = 43.60, p < .01; two-way (deterrents by company): χ²(9) = 39.46, p < .01. Mult. = multiple deterrents mentioned.

First, customer deficiencies and company are significantly related. Noting the overall columns, pest-control employees were more likely to reject requests based on lack of knowledge (second row under overall column: pest control = 53.8%, oil change = 38.6%), while the opposite was true relative to oil-change employees’ rejections of financial requests (third row under overall column: oil change = 42.8%, pest control = 27.4%). Thus, customers have less knowledge as to what is acceptable to ask of their pest-control provider, while customers in the oil-change setting are more likely to ask for price deals because they are paying per visit rather than on a contractual basis (typical of the pest-control context).

The customer deficiencies by deterrents is supported by the strong effect of customers’ knowledge deficiency requests relative to employees’ concerns as to policy and legality issues in the pest-control versus the oil-change responses (see policy/legal column by knowledge row: pest control = 81.0%, oil change = 25.9%). This may again be related to a greater need to understand the contractual parameters involved in a pest-control operation. On the other hand, for the oil-change employees, customers’ lack of knowledge requests led to more risk and lack of resource concerns (see risk columns by knowledge row: oil change = 25.9%, pest control = 6.3% and lack of resources column by knowledge row: oil change = 33.3%, pest control = 0%). Additionally, customers’ physical resource deficiencies produced less compliance based on employee lack of resources in the oil-change data versus the pest-control data (see the lack of resources column by the physical resource row: oil change = 54.5%, pest control = 35.3%), with these differences perhaps reflecting customers’ perceptions of greater equipment available in the operation (oil change) versus the field (pest control).

Finally, the significance of employee deterrents by company indicates the greater use of policy or legal deterrents by pest-control employees in refusing to comply versus oil-change employees (see policy/legal columns in bottom row: pest-control policy/legal = 65.8%, oil change = 42.9%). This finding is consistent with pest-control customers being less familiar with the contract. On the other hand, overall higher refusals exist for oil-change employees based on lack of resources (see lack of resources column in bottom row: oil change = 24.3%, pest control = 8.5%) and higher risk concerns (see risk column in bottom row: oil change = 21.4%, pest control = 7.7%). The risk concerns are most likely due to customers wanting employees to fix their automobiles in unsafe ways to save money. In these cases, the frontline employee had to take a stand against inherently unsafe requests by the customer (e.g., a customer brought his truck in to get his brakes checked out. The employee insisted that the customer get his brakes fixed because they were not safe, but the customer said that he did not have the money and wanted a quick fix of some type instead).

Discussion

Contributions to Theory

While customer special requests have received some research attention, most research has focused on the broader topics of employee engagement in extra-role helping behavior or prosocial organizational behaviors (Brief and Motowidlo 1996). Due to the lack of past research to guide us, we first conducted exploratory interviews and then followed up with content analysis of CIT data based on employees’ recollections of customer special request encounters. The consistency of categorizations across employee data from two different companies in different industries supports the potential generalizability of these findings.

Our primary contribution is a conceptualization of customer special requests from the perspective of frontline employees and a proposed framework of employee decision making in response to these requests. Based on employee perceptions, we categorized customer special requests as involving one of the four customer deficiencies—physical resources, knowledge, financial, and time—while employee assessments were categorized as either positive due to motivation and/or ability, leading to compliance with the request, or negative due to several deterrents, producing noncompliance.

The two positive factors (motivation and ability) produce employee discretionary behaviors, while the deterrents (policy/legal, potential risk, and lack of resources) inhibit discretionary behaviors (i.e., going beyond normal job duties). Interestingly, while Brief and Motowildo (1996) define prosocial employee behaviors as behaviors that go beyond the employee’s role requirements (similar to discretionary behaviors), they suggest that some of these behaviors are functional and beneficial to the company while others are dysfunctional and nonbeneficial. For example, one dysfunctional prosocial behavior is service sweethearting, defined as when “frontline employees give unauthorized free or discounted goods or services to a friend or acquaintance” (Brady, Voorhees, and Brusco 2012, p. 81). Our work offers an additional consideration. When the company empowers its employees, it trusts them to evaluate the customer’s request adequately and to use their best judgment to make the right decision for the company and the customer, even though sometimes there could be a conflict between what is right for the company versus what is right for the customer. The employee must make that call.

Another important finding is the strong relevance of employee desire to help, with ease of complying coming in a distant second. These motivation and ability concepts follow from the MOA processing theory work of MacInnis, Moorman, and Jaworski (1991), while opportunity did not arise. Opportunity in MacInnis, Moorman, and Jaworski’s view suggests that distractions do not impede an employee’s attention. Perhaps for employees, opportunity generally exists unless deterrents are present (i.e., lack of resources or against policy); thus, deterrents appear to represent lack of opportunity.

While the altruistic behavior of the employees appears to be a function of the type of companies and individuals studied, the strong showing of “desire to help” emphasizes the importance of helping behavior as a desirable psychological or psychosocial attribute in employees. As helping behavior involves understanding and responding to customer needs and requests, these behaviors put the organization in a positive light and satisfy customers (Bettencourt and Brown 1997).

Another important finding is that two customer deficiencies, physical resources and lack of knowledge, produced about two thirds of the customer requests in both companies. Further, knowledge deficiency was more prevalent in the pest-control company data than in the oil-change company data due most likely to the complexity of the service. Additionally, while some company differences arose, in general, customers’ lack of knowledge and their financial requests produced the most noncompliance. Given that customer deficiencies have not been a topic of discussion in past research in marketing and yet should be of critical importance to the field, this research suggests that this construct is worthy of further study. Anticipating customer deficiencies and responding to customers’ special requests around those deficiencies could serve as a competitive advantage for companies.

Implications for Practice

The employees surveyed in our study care about providing good customer service. They seem eager to help their customers. However, they often also chose not to comply with many special requests and these decisions were made for good reasons—because the employees saw the requests as against policy, illegal, involving too much risk, and/or because they did not have the resources to meet the request. In these cases, attempting to comply with a special request would likely have resulted in negative consequences.

Further, the strong prevalence of customers’ knowledge deficiency in both data sets, especially in the pest-control data, suggests that some customers lacked understanding of their contract with the firm or what to expect from employees. This finding is further verified in Table 6, which shows that the large majority of knowledge-based requests were refused by the pest-control employees due to policy or the legal implications of complying.

Thus, an implication of these findings is that employees need to be aware of and prepared for the diverse requests that they could and do receive in the field and be armed with appropriate responses. By understanding the basic categories of special requests received by customers, management may be able to devise better strategies to guide employee responses.

For example, in regard to customer knowledge deficiencies, management needs to educate customers as to what employees can and should do. In cases where a contract exists, the service provider needs to go over the specific details of coverage when the customer first engages the service provider as well as provide ongoing communication relative to coverage. In the oil-change context, signs and other forms of customer communication should clearly show what employees are expected to do in serving customers as well as what they are not allowed to do.

Across both companies, customers appear to ask frontline employees to perform activities that go beyond employee expectations on a rather frequent basis (from every day to a few times a week or month for the vast majority of employees). This, in fact, is the essence of employee empowerment—that employees can and should make decisions based on the circumstances and use their best judgment. Thus, the employee must diplomatically manage situations in which a judgment call is required. For example, when asked to do something outside the contract, the employee may comply that day but explain that it is outside the contract so that the customer understands the deviation and does not expect it in the future.

In the marketing literature, employee empowerment is touted as a way to produce happier employees with more decision-making authority and more room in which to bend the rules. However, empowerment can backfire (cf. D. E. Bowen and Lawler 1992). That is, customers may feel inconvenienced if they have to wait because the employee is going out of his way for another customer (Chan and Lam 2011), and employees may feel greater job stress due to empowerment producing increased expectations by the company and customer (Chan and Lam 2011; Hartline and Ferrell 1996).

The company also needs to be aware that differences in service provider activities across employees, branches, or times can produce problems, erroneous expectations, and unhappy customers, while making other customers happy or even delighted. Management must assess the costs versus the value of these discretionary actions as employees attempt to comply with customers’ special requests when appropriate.

Employee helping behaviors can produce greater customer satisfaction and more positive word of mouth (Maxham and Netemeyer 2003). Thus, a clear managerial implication is the importance of hiring individuals with a “helping mentality.” Perhaps the administration of personality assessments, such as Hogan, Hogan, and Busch’s (1984) Service Orientation Scale or Brown et al.’s (2002) Customer Orientation Scale or the underlying traits (e.g., sociability), may help companies find individuals with the desired characteristics.

Limitations and Future Research Directions

Limitations

This article does not address all possible factors relative to the topic of employee compliance with special requests. For example, as suggested by Brady, Voorhees, and Brusco’s (2012) sweethearting research or Reynolds and Beatty’s (1999) relationship research, an employee’s relationship or closeness with the customer can influence his compliance decision. Additionally, the degree of flexibility or empowerment the employee feels he has will come into play, along with who is making the request and how (cf. Wang, Beatty, and Liu 2012). For example, the higher compliance with female requests found here is interesting but may be a function of an all-male sample. Extending this study to female employees would allow for additional insights on this topic.

Further, given the methodology used here, we note several concerns. First, the findings are not likely to represent all encounters or provide an assessment of percentage of compliance versus noncompliance situations that occur in these settings. Second, employees’ self-reflections may tend to upwardly bias the number of altruistic motives reported.

Another limitation is the use of different methods. With one company data were collected via personal interviews, while with the other data were collected via online and paper surveys. While response rates were maximized by using the approaches best suited to the situations, the different methods of data collection could have affected the findings. Future studies should explore opportunities for high response rates concurrent with consistent data collection.

Future Research Directions

While this study provides the field with some understanding of the nature of customer special requests and employees’ assessments of these requests, we recognize that the antecedents to compliance decisions may be viewed relative to a broad set of issues such as the type and culture of the company; the training, psychological make-up and typical job duties of employees; the degree of empowerment given to employees and the level of supervision provided; the nature of the customer and the relationship between the customer and the employee; and finally, the approach used by the customer in making the request. While attempting to offer such an encompassing model is beyond the scope of this article, we encourage future exploration of these ideas as well as conceptualizing the possible short- and long-term consequences of compliance (or noncompliance) on employees, customers, and companies.

A question of interest here is—how can companies encourage employee customer-directed helping behavior? Interestingly, this topic has not received sufficient research attention, while employee-to-employee or employee-to-company helping behavior and helping behavior in general have received attention (cf. Batson 1987; Bendapudi, Singh, and Bendapudi 1996). Further, Lovelock, Patterson, and Walker (1991) point out that while on-the-job-training and incentives may help encourage employees to smile more on the job or to be more polite, a warm and agreeable personality cannot be taught. Southwest Airlines emphasizes the importance of hiring the right people over training or incentives, saying that a newly hired employee should have a “servant’s heart” (Makovsky 2013). Thus, the issue of how employees choose a job that best fits their psychological make-up seems important to understand.

Additionally, many times employee compliance appeared to revolve around employee compassion and the performance of a kind deed. These employees seem to feel compassionate toward helping both genders out, but especially females. Interestingly, while employee compassion toward customers has not received much research attention, compassionate love is an important company value emphasized by many successful companies (Barsade and O’Neil 2014). For example, while Zappos and Whole Foods address the principles of love, caring, and family relative to compassion within the company, compassion toward their customers is also an important part of their culture.

Future research could extend this work by studying how special requests affect employee feelings toward the company or toward the customers making the requests. Understanding these issues from a customer perspective would be useful, as well. Further, customer expectations are relevant to the topic, with different market segments likely to exhibit widely different expectations as to the acceptability of various special requests.

Additionally, it would be useful to compare management’s view of how employees should fulfill customer special requests with employees’ views of these requests. Often employees told us they did not inform their supervisors of their actions, fearing that their boss might not approve.

Obviously, these topics are influenced not only by the type of company and employees but also by the culture and subculture of the company and the company’s location. The data for this study were collected in the Southeastern United States. This area of the country is known for a strong faith-based culture, and many employees and company leaders referenced their strong Christian faith and belief in the Golden Rule in their treatment of customers. Thus, employees in this culture often see helping as simply the right thing to do. Further, the employees studied were not on commission or quota so they are not driven to “help themselves” by increasing sales. Thus, examining these topics with employees on commission or tip based, as well as in other geographic locales, cultures, and subcultures, would be beneficial. Also, researchers could extend this research to B2B settings or to more extended or critical service encounter situations (e.g., in hospitals or cancer treatment centers) where special requests may be more extreme.

Conclusion

In conclusion, customer special requests are a research area that has received little attention, despite their importance to customers and companies and their strong influence on the daily work and decision making of frontline service employees. The objective here was to create an initial structure relative to the types of customer special requests that arise and how employees assess these requests relative to complying or not. At the moment, they are confronted with such a request they must consider many questions simultaneously: What would the boss say? What would my peers do? Can I do it? Do I want to do it? Do I have the time and the equipment to do it? Is it legal and proper to do? Does it fit with company policy? Does it involve risk to me or the customer or the company? The frontline employee must quickly answer these questions and others in making his decision. Finally, a positive compassionate culture, such as the ones studied here, should facilitate and nurture employees’ genuine helping behaviors. We hope this study of frontline service employee compliance with customer special requests has provided some needed structure to this important topic and will encourage further research into this interesting topic.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Notes

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