The Effects of Institutional and Organizational Characteristics on...

The Effects of Institutional and Organizational Characteristics on Work Force Flexibility:Evidence from Call Centers in Three Liberal Market EconomiesAuthor(s): Danielle D. Van Jaarsveld, Hyunji Kwon and Ann C. FrostSource: Industrial and Labor Relations Review, Vol. 62, No. 4, The Globalization of ServiceWork: Comparative Institutional Perspectives on Call Centers (Jul., 2009), pp. 573-601Published by: Sage Publications, Inc.Stable URL: http://www.jstor.org/stable/25594527Accessed: 05-08-2016 19:49 UTC

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THE EFFECTS OF INSTITUTIONAL AND ORGANIZATIONAL CHARACTERISTICS ON

WORK FORCE FLEXIBILITY: EVIDENCE FROM CALL CENTERS IN THREE LIBERAL MARKET ECONOMIES

DANIELLE D. VAN JAARSVELD, HYUNJI KWON, and ANN C. FROST*

This comparative study examines survey data from 464 call centers in the United States, 167 in the United Kingdom, and 387 in Canada to explore two questions: whether institutional differences shape employers' choices of ways to improve work force flex ibility, both numerical and functional; and whether strategies for numerical flexibility and functional flexibility are related. The results suggest that institutional differences across these liberal market economies?specifically, in dismissal regulations and union strength?did affect how employers chose to achieve work force flexibility. For example, the use of part-time workers was more common in countries with more stringent rules regulating dismissals. Organizational characteristics also mattered, with outsourced firms being more likely than in-house firms to use part-time workers. Evidence also suggests that managers used numerical flexibility and functional flexibility strategies as substitutes: higher employee job discretion was associated with both lower dismissal rates and a lower likelihood of temporary use.

*\m arket deregulation and technological -" * advancements are intensifying com petitive pressures on firms. In response, firms are increasingly searching for ways to enhance work force flexibility?their capacity to alter task allocation and labor force size in

response to demand fluctuations (Kalleberg 2001). Firms in liberal market economies are freer than those in coordinated economies

*Danielle D. van Jaarsveld is Assistant Professor at the Sauder School of Business, University of British Columbia; Hyunji Kwon is a Lecturer at the Department of Management, King's College London; and Ann C. Frost is Associate Professor at the Richard Ivey School of Business, University of Western Ontario.

This research was funded by the Social Sciences and Humanities Research Council of Canada (SSHRC), the Inter-University Research Center on Globalization and

Work (CRIMT), the Russell Sage Foundation, the Alfred P. Sloan Foundation, and the Call Center Research Foundation (U.K.). The authors thank Florence Lee

to select from among work force flexibility strategies, because they face fewer legal limita tions on such choices (Hakim 1990). Given that countries with liberal market economies share similar institutions, it is often assumed that firms operating in these countries will adopt similar work force flexibility strategies. Yet, few studies have empirically tested this assumption. Moreover, there is a tendency to focus on the broad similarities between countries within the liberal market category

and David Walker for valuable research assistance. For helpful feedback on earlier versions of this paper, they thank Rosemary Batt, David Holman, Alexander Colvin, Steven Frenkel, and Yoshio Yanadori.

A data appendix with additional results, and cop ies of the computer programs to generate the results presented in the paper, are available from Danielle van Jaarsveld, Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC, V6T 1Z2, [email protected].

Industrial and Labor Relations Review, Vol. 62, No. 4 (July 2009). ? by Cornell University. 0019-7939/00/6204 $01.00

573


574 INDUSTRIAL AND LABOR RELATIONS REVIEW

despite the existence of some salient differ ences.

These institutional differences may play an important role in shaping the type of work force flexibility strategies firms choose to pursue. In this paper, we focus on two com mon work force flexibility strategies firms use: numerical flexibility and functional flex ibility (Atkinson 1984).1 Numerical flexibility refers to the adjustment of work force size in response to changes in demand, skill re quirements, and employment needs through hiring, dismissal, or the use of non-standard contracts (Cappelli and Neumark 2004; Kal leberg 2001). Non-standard employment contracts diverge from standard full-time employment, and include temporary, con tract, and part-time employment (Atkinson 1984; Davis-Blake, Broschak, and George 2003; Houseman 2001; Kalleberg, Reskin, and Hudson 2000). In contrast to numerical flexibility, functional flexibility refers to work design choices?the ability either to transfer labor from one task to another or to change the scope of individual tasks (Atkinson 1984; Cappelli and Neumark 2004).

How do firms decide among the various work force flexibility options that are avail able to them? We seek to clarify the role institutional differences play in this decision by addressing two questions. First, how do employers in different institutional settings create work force flexibility? Second, do institutions shape employers' strategies for increasing work force flexibility through the use of non-standard employment contracts, employee dismissals, and work design choic es? We address these two questions through a study of call centers located in three liberal market economies: the United States, the United Kingdom, and Canada.

We use a comparative research design in this paper for four reasons. First, these three countries are classified as liberal market economies, and from a comparative interna tional perspective they are perceived as hav ing similar labor market institutions (Card, Lemieux, and Riddell 2004; Hall and Soskice

TWork force flexibility also includes financial (or wage) and temporal flexibility. However, they are beyond the scope of this paper (Blyton 1992).

2001; OECD 1996). Our analysis sheds light on the limitations of this conventional group ing by assessing the extent to which small institutional differences matter and influ ence decisions about work force flexibility. Second, a close focus on the differing institu tional contexts of these three countries holds

promise for revealing whether institutional characteristics influence employer choices for work force flexibility. Third, most of the comparative research on work force flexibil ity is based on cross-industry-level aggregate data (Cappelli and Neumark 2004; Olsen and Kalleberg 2004). That level of aggregation limits explanations about why employers choose one type of work force flexibility over another, or some combination of work force flexibility strategies (Kalleberg 2001; Olsen and Kalleberg 2004). By comparing these strategies in a single sector, we can effectively control for alternative explanations. Finally, much of the research on numerical and functional flexibility has been conducted in manufacturing settings (Cappelli and Neu mark 2004). That research may be usefully complemented by a similar investigation conducted in a very different setting, a high turnover service sector environment where temporary layoffs are rare and non-standard employment contracts abound.

Institutional Influences on Numerical Flexibility

National institutions, business strategy, and the competitive environment shape employment practices (Belanger, Berg gren, Bjorkman, and Kohler 1999; Dore 1973; Godard 2002). Yet, assuming similar effects are present across countries without taking differences in national institutional environments into consideration can be misleading (Godard 2009; Godard 2007; Wood and Godard 1999). In this paper, we evaluate whether "small differences" in employment protections shape decisions about work force flexibility by studying firms operating in countries with relatively similar labor market institutions: the United States, Canada, and the United Kingdom (See Card and Freeman 1993; Card et al. 2004; Wood and Godard 1999).


NUMERICAL FLEXIBILITY IN U.S. AND CANADIAN CALL CENTERS 575

Firms in liberal market economies have many choices in how to configure work force flexibility. We focus on two primary mecha nisms employers commonly use to regulate the size and composition of their work force in call centers?employee dismissals and non standard employment contracts?because these strategies are sensitive to institutional differences and decisions about them are within management's domain.

Dismissing employees provides employ ers with a means to adjust work force size. In times of weaker demand or in cyclical downturns, employers in many sectors use temporary layoffs (with eventual recall) or dismissals (as in cases of restructuring or downsizing). Call center employers rarely use temporary layoffs, mainly because the high levels of employee churn and turnover reported by call center employers enable them to reduce work force size through at trition.2 They may turn to dismissals as a way to "fine-tune" work force size. Also, when there is pressure to fill seats quickly to sup port a newly launched campaign, and little time to invest in a thorough recruitment and selection process, call center employers may use dismissals to eliminate workers who have proven to be poor selection choices.

These employers' second main resort for regulating the size and composition of the work force is non-standard contracts?pri marily, contracts for part-time, temporary, and contract employment. These contracts enable employers to match employment level and skill composition with market demands, and can also serve as a screening mecha nism to determine whether an employee is worthy of a full-time, permanent contract (Houseman 2001). The flexibility afforded employers by non-standard contracts exceeds that available from the standard full-time permanent contract.

How employers choose between these two mechanisms?dismissal and the use of non standard employment contracts?or use them together may be shaped by the institutional environment.

2This is consistent with Montgomery's (1991) find ing that lay-off rates were over three times higher in

manufacturing establishments (23.0%) than in non manufacturing establishments (6.4%).

Dismissal

In general, the dismissal process is less regulated in liberal market economies than in coordinated economies. Yet, dismissal regulations differ across countries within this category. Particularly relevant to employer decisions about work force flexibility is the cost associated with dismissing a full-time employee (Olsen and Kalleberg 2004). In the United States, employment-at-will laws, which cover the great majority of U.S.-based call centers, permit the employer or the employee to end the employment relation ship at any time (Autor, Donohue III, and Schwab 2004; Morriss 1994). In principle, it is unnecessary for a U.S. employer covered by employment-at-will to establish cause in order to terminate an employee (Colvin 2006). Some exceptions to the at-will rule exist (See Autor etal. 2004; Block and Roberts 2000; Colvin 2006), but in general, U.S.-based employers are not required to establish cause or give employees any advance notice before terminating them.

In contrast to the United States, regulations governing the dismissal process are stricter in both Canada and the United Kingdom (Avraam, Hurka, Spooner, and Wydajewski 2004; Colvin 2006). In Canada, an employer terminating a full-time employee needs to give either notice or pay in lieu of notice where just cause for dismissal is absent (Har ris 1990). The handling of dismissals in the United Kingdom resembles the Canadian approach, although the required notice period is shorter (Avraam et al. 2004; Fudge 2007). Thus, dismissing an employee can be a more expensive undertaking for employers in both Canada and the United Kingdom than in the United States, and the result could be lower dismissal rates. For example, Colvin's (2006) study of establishments in the chemicals, building maintenance, and clean ing industries in Ontario and Pennsylvania found 49.8% higher dismissal rates in the U.S.-based firms than in the Canadian firms.

As we discuss further below, our study ex amines not only the rate of use of dismissals (as well as of other practices), but also their incidence, that is, whether any at all occur at a given establishment during a given period.



We would expect incidence of dismissals, like rates, to be higher in U.S. call centers than in Canadian and U.K. call centers.

The varieties of capitalism literature em phasizes the similarities between countries with liberal market economies, but small institutional differences in labor and em ployment laws between countries within the liberal market category lead us to believe that employers in these three countries will diverge in the degree to which they use dismissal as a means to increase work force

flexibility. Specifically:

Hypothesis 1: Both the incidence of dismissals and the rate of use of dismissals will be higher in U.S.-based call centers than in either Canadian or U.K. call centers.

The Use of Non-Standard Employment Contracts

The use of non-standard employment ar rangements also offers employers a way to increase work force flexibility. We examine two forms of non-standard employment arrangements: part-time and temporary. Cross-national differences in the use of non-standard contracts may emerge as a result of differences in dismissal and non standard employment regulations. For example, Autor (2003), in a study to explain the growing use of temporary employment in the United States, found that limitations on employment-at-will encouraged firms to place increasing reliance on non-standard work arrangements. Consistent with this finding, Olsen and Kalleberg (2004) found that firms in Norway relied more heavily on non-standard employment contracts than did U.S.-based firms. The authors explained this finding as a reaction to the restrictive ness of Norwegian labor regulations and the difficulties firms encountered when trying to dismiss full-time employees (Olsen and Kalleberg 2004).

Moreover, critics suggest that the weaker employment protections for non-standard workers compared to full-time employees in liberal market economies encourage employers to hire them. Indeed, empirical evidence has shown that in all three countries included in this study, non-standard workers

have less employment law protection and less access to benefits such as health insurance and pensions than do full-time employees.

The definition of part-time work is, to some degree, country-specific. In both the United States and Canada, the definition of part-time is based on the number of hours an individual works per week. In the United States, employees who work less than 35 hours per week with one employer are considered part-time (GAO 2006), whereas in Canada, the threshold is less than 30 hours per week (Marshall 2000). In the United Kingdom, the part-time criterion is less specific than in Canada and the United States and is based on both hours of work (less than 30 hours per week) and an individual's perception of his or her employment classification (Wall ing 2007).

The composition of the part-time work force varies in each country, with the work force in the United Kingdom consisting of a higher share of part-timers (23.27%) com pared to Canada (18.7%) and the United States (12.56%) (OECD 2009b). In the United States and Canada, part-timers are eligible for benefits, but many encounter dif ficulties meeting the required hours-worked threshold (GAO 2006; Vosko, Zukewich, and Cranford 2003). In the United Kingdom, legislative initiatives have recently been intro duced requiring employers to treat part-time and full-time employees in a similar manner with respect to working conditions (Bowlus and Grogan 2009).

Temporary employment includes workers hired on a temporary basis by employers, and those placed at a client firm by a temporary agency (GAO 2004). The legal definition of a temporary agency worker is similar in the United States and Canada. In the United States, temporary agency workers are not gen erally considered employees, while in Canada they are. In the United Kingdom, there is no legal definition of agency temporaries, and they are not classified as employees (Davidov

3In 2008, the United Kingdom started to consider whether to adopt a directive that would require employ ers to treat agency temporaries the same way they treat full-time staff.



2004) .3 In contrast to part-time work, Canada has a higher share of temporary workers in its work force (13.16%) than do the United States (4.21%) and United Kingdom (5.51%), which have similar levels of temporary work ers in the overall work force (OECD 2009a).

In Canada and the United States, tempo rary workers have access to minimum levels of employment protection but, like part-timers, can find it difficult to meet the working time thresholds to access these benefits. The involvement of a temporary agency in the employment relationship can complicate agency temporaries' access to benefits (Vosko 2000). In the United Kingdom, consistent with EU directives, temporary agency work is in the process of becoming more regulated, with legislation designed to encourage em ployers to treat temporary agency workers in the same manner as full-time employees (BERR 2008; Green 2008; McColgan 2000).

However, at the time of the survey, temporary agency workers were weakly protected by U.K. employment regulations.

U.S. employers appear to have more flex ibility to dismiss employees than do Canadian and U.K. employers. If the pattern found by Olsen and Kalleberg (2004) is replicated here, employers' exposure to stricter dis missal regulations in the United Kingdom and Canada can be expected to predispose them, more than their U.S. counterparts, to increase numerical flexibility through alter native means such as the use of non-standard employment contracts. Tending to bear out this prediction is Autor's (2003) finding: in a U.S.-based study of temporary employment, the introduction of stronger dismissal protec tions had the unintended consequence of promoting the growth of temporary employ ment. We would hypothesize, in general, that in liberal market countries, employers subject to relatively strong dismissal regulations will

make relatively heavy use of part-time and temporary workers, as a means of avoiding the higher costs associated with dismissing full-time employees. Therefore, we expect higher percentages of call centers using temporary and part-time workers in Canada and the United Kingdom than in the United States, and we expect the extent of use to be higher in Canada and the United Kingdom

than in the United States.

Hypothesis 2a: Call centers in the United Kingdom and Canada will be more likely to hire part-timers and will use them to a greater extent than those in the United States

Hypothesis 2b: Call centers in the United Kingdom and Canada will be more likely to hire temporary workers and will use them to a greater extent than those in the United States.

Unionization and Numerical Flexibility At the time the data were collected for this

study, Canada (29.9%; 2005) and the United Kingdom (28.8%; 2004) had similar union density levels, but U.S. union density was much lower (12.4%; 2003) (OECD 2009c). The declining U.S. labor movement and the weakness of the National Labor Relations Act's enforcement have undermined the power of unions in the United States to constrain managerial decisions (Keefe and Batt 1999). In contrast, labor legislation in Canada continues to support unionization by, for example, facilitating new organizing efforts through quick resolution of unfair labor practices; moreover, in several juris dictions, it requires provincial labor boards to impose first contracts where employers fail to bargain in good faith (Godard 2003;

Wood and Godard 1999). In the United Kingdom, voluntary union recognition is common and legislative supports initiated by the Labour government beginning in 1999 were introduced to strengthen union rights weakened by previous parliamentary regimes (Brown and Nash 2008; Godard 2009).

At the workplace level, union presence is another factor that may influence manage ment's ability to dismiss employees and, in turn, influence a firm's reliance on non standard contracts. Unions in liberal mar ket economies have played a central role in protectingjob security (Freeman and Medoff 1984; Addison 1986; Meltz 1989). Studies undertaken in the United States, Canada, and the United Kingdom consistently find that union presence is significantly related to lower dismissal rates (Shaw, Delery, Jenkins, and Gupta 1998; Cully, Woodland, O'Reilly,



and Dix 1999). If, on average, dismissal rates are lower in union workplaces than in non union workplaces, we expect this pattern to be consistent across all three countries.

Hypothesis 3: Union presence will be negatively related to the incidence and extent of dismissals.

Finally, union presence may also influ ence the use of non-standard employment. Historically, unions view the hiring of non standard workers in any form as contravening the union goal of protecting the job security of their membership (Freeman and Medoff 1984). However, the proliferation of flexible employment relationships and the need to increase union membership have induced

many unions to move away from traditional full-time employment as the sole structure for employment relationships. Unions may re spond to increased reliance on non-standard workers in different ways, with some unions viewing non-standard workers as a threat to the job security of full-time members and others embracing them as a new source of members (Appelbaum and Gregory 1988; Carre and Joshi 1997).

To date, research on the relationship between union presence and non-standard employment contracts has yielded mixed results. Several studies have found evidence that union presence is negatively related to the use of temporary workers (Abraham 1990; Gramm and Schnell 2001; Houseman 2001;Lautsch 1996),part-timers (Houseman 2001), and independent contractors (Gramm and Schnell 2001). However, some studies have found that unions are positively related to use of temporary workers (Davis-Blake and Uzzi 1993) and of contractors (Abra ham 1990); the unions in these cases view non-standard workers as reinforcing the job security of full-timers by serving as buffers. Other studies have found that non-standard

employment arrangements and unions are not related (Abraham and Taylor 1996). These mixed findings could reflect different approaches to measuring union presence (Vidal and Tigges 2009). Union presence on its own is not enough

to influence the hiring of non-standard work ers by firms. Union strength, reflected by union density at both the national level and

the industry level, influences the ability of local unions to shape managerial decisions.

With the exception of historically regulated industries such as telecommunications, in

which call centers inherited unionization, even unions in coordinated economies are encountering difficulties organizing call centers. In liberal market economies, low levels of union density indirectly may limit a union's ability to influence decisions about hiring non-standard workers in an environ ment where non-standard contracts present a strategic staffing strategy for employers to address significant cost pressures, frequent fluctuations in demand variability, and high levels of absenteeism that are common fea tures of call center work (Batt and Moynihan 2002). The increase in wages and ben efits for full-time employees associated with union presence may create an incentive for unionized employers to hire non-standard workers to reduce labor costs (Houseman 2001). Unions also may tolerate the use of non-standard workers to protect full-time positions. Therefore, across unionized call centers in all three countries, we expect to find a positive relationship between union presence and the incidence and extent of use of both part-time and temporary workers.

Hypothesis 4a: Union presence will be positively associated with the incidence of and extent of reliance on part-time arrangements.

Hypothesis 4b: Union presence will be positively associated with the incidence of and extent of reliance on temporary arrangements.

Outsourcing and Work Force Flexibility We also examine the effect of outsourc

ing on managerial decisions about work force flexibility. Our sample also includes outsourced call centers performing work that, before it was transferred to an external contractor, was handled by a firm's internal work force (Barker and Christensen 1998; Davis-Blake and Uzzi 1993; Kalleberg and Marsden 2005). Firms outsource work to subcontractors for several reasons: to reduce

costs (in some cases by sidestepping obliga tions associated with collective bargaining agreements) (Abraham and Taylor 1996; Bardhan and Kroll 2003; Dossani and Kenney



2007; Erickcek, Houseman, and Kalleberg 2002; Pfeffer and Baron 1994); to access a high-quality work force and adequate infra structure (Bottini, Ernst, and Luebker 2007; Casaburi, Gattai, and Minerva 2008; Razzolini and Vannoni 2007); to shield regular, full-time workers from market fluctuations (Cappelli and Neumark 2004; Doeringer and Piore 1971); and to offload work that is outside the firm's core competencies. Whether a call center is an in-house or outsourced operation may affect decisions about strate gies to achieve work force flexibility. Firms that retain customer service in-house may be

motivated by the desire to maintain control over customer service quality. Conversely, outsourced call centers are obligated to meet the expectations of client firms, and the terms of these agreements (such as call handling time requirements) are explained in detail in service-level agreements. With roughly 70% of the operating expenditures for call centers consisting of labor costs and the relatively low skill levels of many outsourced call center jobs, outsourced call centers are especially sensitive to the cost of labor. Moreover, outsourced call centers may

maintain lower wage levels in an effort to re duce labor expenditures, especially in the case of lower-skilled jobs (Berlinski 2008; Erickcek et al. 2002). The desire to constrain labor expenditures may lead to increased reliance on non-standard employment contracts as a way to lower costs and meet service demands. However, lower wage levels can reduce the quality of the applicant pool. Under pres sure to meet client demands stipulated in service-level agreements to handle specific call volume, outsourced call centers are more likely to hire less qualified candidates, a practice that will, in turn, increase turnover. In a study of cleaners and security guards, Berlinski (2008) explained the lower wages earned by employees of outsourced firms compared to in-house employees as a function of less productive employees being attracted to lower-wage positions. This explanation

may generalize to outsourced call centers. Outsourced call centers are also likely to handle "spillover" work from in-house firms, as these firms often choose to outsource the most variable parts of their business in order

to handle variations in call volume and main tain a stable workload for their work force (Abraham and Taylor 1996). Outsourced call centers, in some circumstances, need to shrink their work force quickly at the end of campaigns farmed out to them by in-house firms, and these demand fluctuations will also increase dismissal rates.

Hypothesis 5a: Outsourced call centers will be more likely to dismiss employees and will have higher dismissal rates than will in-house centers.

Hypothesis 5b: Outsourced call centers will be more likely to hire part-timers and will use them to a greater extent than will in-house centers.

Hypothesis 5c: Outsourced call centers will be more likely to hire temporary workers and will use them to a greater extent than will in-house centers.

The Relationship between Functional Flexibility and Numerical Flexibility

In addition to numerical flexibility, firms can increase work force flexibility through functional flexibility (for example, Appel baum and Batt 1994; Cappelli and Neumark 2004). Functional flexibility refers to a transformation in how work is organized and the expansion of work force skills through cross-training, and is characterized by the use of self-directed teams, quality improvement teams, and a greater extent of discretion granted to employees in how they complete their work.

Few studies have directly examined func tional flexibility in the context of other em ployment practices (Kalleberg 2001). The existing research evaluating the relationship between functional and numerical flexibility has yielded inconclusive results. For example, in a U.S.-based study of 694 U.S. manufactur ing establishments, Osterman (1994) found that the presence of high performance work practices reduced the likelihood that a firm would rely on contingent workers. Analyzing data from the National Employers Survey administered by the U.S. Census Bureau, Cappelli and Neumark (2004) found that internally flexible employment practices were associated with lower dismissal rates. In a 1992 survey of 875 U.S. establishments, Lautsch (1996) found that high performance work practices were positively related to the use of



temporary workers, whether hired directly or through agencies. However, this finding may reflect the decision to include only those establishments that used contingent workers. Union presence may also be a factor in this relationship, and in North America (Canada and the United States), some argue that the rigidity of promotion rules discourages firms from choosing functional flexibility as a means of improving work force flexibility (Conti 1992; Grenier, Giles, and Belanger 1997; Weinstein and Kochan 1995). Mixed findings also emerge from stud

ies undertaken in Canada and the United Kingdom. In Canada, Pinfield and Atkinson (1988) found that firms combined numeri cal flexibility and functional flexibility. In the United Kingdom, Cully et al.'s (1999) analysis of data from the 1998 Workplace Employee Relations Survey showed a negative relationship between numerical flexibility and functional flexibility. Similarly, in a cross-national study of paper mills in Finland and the United Kingdom, Penn, Lilja, and Scattergood (1992) found that numerical flexibility was negatively related to functional flexibility.

Clearly, whether employers substitute one form of flexibility for the other or use them in a complementary manner remains a sub ject of debate (Cappelli and Neumark 2004; Davis-Blake and Uzzi 1993; Kalleberg 2001). In the call center setting, we expect firms to use numerical flexibility and functional flex ibility as substitutes. The work performed in call centers varies tremendously by skill level. Where the tasks being performed by agents do not require much skill or training (for example, directory assistance), numeri cal flexibility is a common option for firms. Because of cost pressures in these call cen ters, employers will be unlikely to invest in functional flexibility. In centers where agents are performing more complicated tasks (for example, providing IT support), work force flexibility is more likely to come from func tional flexibility rather than from the use of non-standard employment arrangements or dismissals, which may be demoralizing to the core work force. Therefore, we test the following hypothesis in this paper:

Hypothesis 6: Numerical flexibility will be negatively related to functional flexibility in call centers.

Methods

Sample Our analysis is based on samples of call

centers in three liberal market economies: 464 in the United States, 387 in Canada, and 167 in the United Kingdom. The U.S. and Canadian data were collected by telephone survey by the Survey Research Institute at Cornell University in mid-2003 (U.S. data) and between February 2005 and July 2006 (Canadian data). In the U.K. survey, manag ers were surveyed between February and May 2004 both by telephone and, because many respondents expressed a preference for a paper-based survey, by mail. The response rate was 68% for the United States, 77% for Canada, and 40% for the United Kingdom.

The U.S. call centers are a stratified random sample from two sources: roughly 60% came from a Call Center Magazine list representing call centers across a broad array of industries and sectors, and the re maining 40% were taken from a Dun and Bradstreet listing of telecommunications establishments. Sources for the random sample for Canada are provincial call center association membership lists, Contact Center Canada (a federal sector council), and our own extensive research, in cooperation with provincial economic development officials.4 The sample includes a wide cross-section of industries from all ten Canadian provinces. The U.K. survey was administered to a sample of call centers from the membership of the national-level Call Center Association (CCA) (Wood, Holman, and Stride 2006). The three samples are comparable in

terms of industry type, with one noteworthy exception: the U.S. study oversamples tele communications call centers. Therefore, in

Provincial call center associations operate in British Columbia, Alberta, Saskatchewan, Manitoba, and New Brunswick. Economic development officials at the provincial level assisted us in Nova Scotia, Newfound land, and Prince Edward Island. Ontario has smaller regional call center associations, but lacks a provincial call center association.



what follows, this oversampling may affect interpretations of the influence of union presence in the United States, since union density in U.S. telecommunications firms exceeds that in U.S. call centers generally, as well as that across firms in the broader U.S. economy.

Measures

We analyze three types of numerical flex ibility, all measured at the establishment level. We measure the incidence of dismissal,

whether or not centers dismissed employees, across centers based on responses to the fol lowing question: "In the last year, what per cent of your permanent core employees were dismissed (excluding retirements, quits, and promotions)?" For this measure, we coded centers as 1 if they dismissed permanent core employees in the past year, and 0 otherwise.

We also measured dismissal rates based on this question. Within this dismissal measure are poten

tially two categories of dismissal: dismissal for cause, and dismissal related to manage ment's need to achieve work force flexibility in response to changes in the demand for labor. To help untangle these two distinct categories within our measure of dismissal, we took the following steps. First, in estimating the predictors of the incidence of dismissal in supplemental analysis not presented here, we constructed an indicator measure to capture centers that dismiss more than 10% of their employees annually, based on the logic that if a center is dismissing a significant number of people, the dismissals are more likely for layoff purposes than for cause. The inclusion of this variable did not significantly change our results, and so we decided to exclude this variable from the presentation of the final models. Second, in the dismissal analysis, we control for absenteeism, on the grounds that it is one of the most frequent reasons for dismissal for cause in the call center setting.

Similar to our dismissal analysis, our exami nation of the use of part-timers and temporary workers looks at both incidence and intensity of use (Houseman 2001; Olsen and Kalleberg 2004). First, we asked whether respondents used part-timers, temporary agency work

ers, direct-hire temporary workers, or any combination thereof. Second, we asked what percentage of the work force was covered by each type of contract. Based on the responses to these questions, we constructed measures to reflect the incidence of use?whether or not these non-standard work arrangements were in use at all?and intensity of use?the extent to which they were being used. We combined the two types of temporary workers (direct hire and agency) to create our measure of temporary contract use.

Three categories of independent variables are included in our analysis: institutional characteristics, organizational characteris tics, and measures of functional flexibility. To evaluate institutional characteristics, we included country dummies, with the value of 1 assigned to centers located in Canada and the United Kingdom, and the United States as reference (0). These country variables reflect national-level differences in employment regulations and industrial relations institu tions. We created two indicator variables to

measure organizational characteristics: (1) whether a call center was in-house, serving its own company's customers (coded 1), or outsourced, providing services to other com panies (coded 0); and (2) whether the call center was unionized (coded 1 for union, 0 for non-union).

To measure functional flexibility for the core work force, we asked firms about three aspects of flexible work design: reliance on offline and online teams, the degree of job discretion, and investment in employee train ing. The use of teams is an additive index representing the percentage of employees who were participating in a quality circle or problem-solving team and the percentage who were participating in a self-directed team (where people work together and jointly

make decisions about task assignments). The objective of organizing workers into teams is to enrich their work experience (Batt 2002). Job discretion consists of six items developed in previous call center studies assessing em ployee discretion during the course of their daily activities?both work tasks and interac tions with customers (Holman 2002; Wood et al. 2006). These items are measured on a five-point Likert scale from 1 (not at all)



to 5 (a great deal), and are combined into a mean index with a reliability coefficient of .74. Investment in training is a combina tion of the amount of initial training agents received and the amount of time it took for

agents to competently perform their job functions. The amount of training an indi vidual receives may be related to the level of discretion he or she has on the job. In call centers where agents receive less training and training that is narrow in scope, the job itself is very standardized. In centers where agents receive more training, they can be moved from a sales group to a service group, providing the employer with flexibility in day-to-day assignments.

We control for differences in organiza tional and work force characteristics that previous research has shown to have a signifi cant relationship with numerical flexibility: sector (telecommunications), the establish

ment's age (square root transformation), its size (natural log transformation) (Houseman 2001; Kalleberg and Reynolds 2000; Uzzi and Barsness 1998), whether it handles predominantly inbound or outbound calls, and the percentage of its work force with less than one year of tenure (Abraham and Taylor 1996; Houseman 2001; Kalleberg and Marsden 2005). In addition, we control for incentive pay: the percentage of total annual pay based on individual incentives.

Work force characteristics that may af fect decisions about numerical flexibility include the percentage of women in the core customer service work force and education levels (Batt 2002; Callaghan and Hartmann 1991). In measuring the average education level of the core work force, we constructed a dummy variable that represents education up to but not beyond age 16 (coded 1) and education beyond age 16 (coded 0).

In assessing the incidence of dismissal and of part-time and temporary employ ment contract use, we analyze the data us ing logistic regression, the recommended analytical approach for estimating models with dichotomous dependent variables. In evaluating the extent of use of dismissals, part-time workers, and temporary workers, we employed left-censored Tobit analysis, because our three dependent variables are

left-censored at zero, and Tobit corrects for the biased estimates OLS regression produces when used with censored data (Maddala 1992) .5 For each model predicting extent of use, we estimated full models with country interaction terms using the United States as an omitted variable to evaluate whether the main effects would hold across countries. We also estimated the extent of use models for each separate country sample to further examine within-country differences. The results are discussed in the next section.

Results

Descriptive Results

Several key patterns in numerical flex ibility emerge from the descriptive statistics.

We show statistically significant differences for the variables included in our analysis by country in Table la, and organizational characteristics (union versus non-union; out sourced versus in-house center) by country in Table lb. In Table 2, we present the means, standard deviations, and correlation matrix for all variables used in the study.

Turning first to the cross-national dif ferences in numerical flexibility, the aver age dismissal rate was significantly higher in U.S.-based call centers (9.17%) than in Canadian (6.09%) and U.K. (3.13%) call centers (p < .05; Table la, row 1). We also found some differences in the likelihood that non-standard work arrangements were used and in the extent of reliance on them. The incidence of part-time use was consistently high across all three countries, at 51.29%, 77.00%, and 79.04% for the United States, Canada, and the United Kingdom, respec tively. Call centers in the United Kingdom and Canada were much more likely to use part time workers than were those in the United

States (p < .05; Table la, row 5). Moreover, Canadian call centers relied on part-timers to a much greater extent (28.06%) than U.S. (16.03%) and U.K. call centers (18.76%)

5The tobit model assumes that a latent unobserved variable, y*, linearly depends on x. via a parameter p, which estimates the relationship between x. and y* (Maddalal992).



Table la. Mean Comparison by Country.

Country Variable Total U.S. Canada U.K. Comparison

1 Annual Dismissal Rate 7.06 9.17 6.09 3.13 a, b, c 2 % Part-Time Agentst 21.03 16.03 28.06 18.76 a, c 3 % Temporary Agents* 9.91 7.08 12.50 11.98 a, b 4 Dismissal Rate > 10% 28.62 38.22 22.47 14.97 a, b, c 5 Incidence of Use of Part-Timers (%) 65.62 51.29 77.00 79.04 a, b 6 Incidence of Use of Temps (%) 31.34 24.78 34.88 41.32 a, b 7 Union Presence 19.32 9.09 20.05 49.32 a, b, c 8 Ownership: In-House 72.10 86.21 54.78 73.05 a, b, c 9 % of Work Force in Teams 28.81 26.12 35.67 20.47 a, b, c 10 Investment in Training 24.43 21.56 25.26 30.57 a, b, c 11 Job Discretion 2.67 2.85 2.50 2.58 a, b 12 Education Level (up to age 16)ft 27.09 39.05 3.50 48.67 a, b, c

13 % Absent 5.89 5.75 5.62 6.90 b, c 14 Gender (% female) 67.40 65.67 69.19 68.04 a 15 % with < 1 Year Experience 26.68 24.69 27.79 29.90 b 16 Performance-Based Pay 9.63 14.01 6.71 3.23 a, b, c 17 Establishment Size^ 4.29 4.26 4.32 4.29 18 AgeofEstablishmentt+t 3.39 3.84 3.10 2.81 a, b, c 19 Sector: Telecom 33.01 52.59 19.38 10.18 a, b, c 20 Call Type: Inbound 81.83 85.13 76.23 85.63 a, c

Sample Size_1,018_464_387_167_ Note: Inter-country differences, all statistically significant at the 0.05 level, are indicated in the "Country Com

parison" column by a (U.S./Canada), b (U.S./U.K.), and c (Canada/U.K.). HTie "% part-time use" is the ratio of part-time agents to all employees/100 (range: 0-1) and the extent of

temporary use is a ratio of temporary agents to all employees/100 (range: 0-3). ftThe percentage of individuals who have education up to but not beyond age 16 (i.e., did not complete a high

school diploma) is extremely low in the Canadian sample in comparison to the U.S. and U.K. samples. This figure should be interpreted as indicating a high level of education in the Canadian call center work force.

+ttThe establishment age variable is a square root transformation and establishment size is a natural log transformation.

(p < .05; Table la, row 2). Turning to the use of temporary employment contracts, 34.88% of centers in Canada and 41.32% of those in the United Kingdom used these arrangements, compared to just 24.78% of centers in the United States (Canada-U.S. comparison, p < .05; U.K.-U.S. comparison, p < .05; Table la, row 6). Although the use of temporary workers was fairly widespread across all three countries, the intensity of use was relatively small: 7.08% in the United States, 12.50% in Canada, and 11.98% in the United Kingdom (Canada-U.S. com parison, p < .05; U.K.-U.S. comparison, p < .05; Table la, row 3).

Next, we show in Table lb how union presence and ownership status (in-house or outsourced) affected the outcomes of interest by country. Unionized centers consistently had lower dismissal rates than their non

union counterparts across all three countries. In the United States, the dismissal rates for unionized versus non-union centers were 4.60% and 9.70%, respectively; in Canada, 2.75% and 6.78%; and in the United King dom, 2.60% and 3.63%.

Whether a center is in-house or outsourced reveals another dynamic of the global call center business. In-house centers repre sented a substantially smaller share of the Canadian sample (54.78%) than of either the U.S. sample (86.21%) or the U.K. sample (73.05%) (p < 0.05; Table la, row 8). The larger proportion of Canadian outsourced centers is due to an influx of U.S. call center work into Canada to serve the U.S. market, attracted by Canada's favorable exchange rate, well-educated work force, and world class infrastructure (UNCTAD 2004; van Jaarsveld, Frost, and Walker 2007). The cor


Table lb. Mean Comparison by Country: Union Presence and Ownership Characteristics (in-house/outsourced). <-g

-.- ^

U.S. Canada U.K. U.S. Canada U.K.

Non- Non- Non- In- Out- In- Out- In- Out

Variable Union Union Union Union Union Union Signif. House sourced House sourced House sourced Signif.

1 Annual Dismissal Rate 9.70 4.60 6.78 2.75 3.63 2.60 a,b,e 8.31 14.95 4.86 7.69 2.55 4.68 f,g,h,i,j

2 % Part-Time Agents* 15.96 16.31 27.92 28.23 19.47 17.43 d,e 13.19 33.87 25.53 31.18 19.96 15.47 f,ij

3 % Temporary Agents* 7.27 3.14 11.42 18.34 11.31 12.46 5.98 14.02 15.19 9.16 13.10 8.90 f,i

4 Dismissal Rate > 10% 77.61 66.67 76.19 43.33 66.67 42.86 b,c,d 73.16 98.25 64.68 78.71 49.53 75.00 f,g,h,ij

5 Incidence of Use of Part-Timers (%) 52.62 38.10 76.32 82.26 82.95 76.00 d,e 47.00 78.13 73.58 81.14 77.87 82.22 f,i _

6 Incidence of Use of Temps (%) 25.48 16.67 32.09 51.61 37.50 42.67 b,d,e 22.25 40.63 39.62 29.14 36.89 53.33 f,g,h,i,j ?

7 Union Presence 10.30 1.56 26.54 12.14 57.66 24.32 f,g,h,ij D

8 Ownership: In-House 85.00 97.62 51.40 74.19 60.23 89.33 a,b,c,d,e C

9 % of Work Force in Teams 27.21 16.39 35.64 36.01 18.07 23.33 a,d,e 27.54 17.29 36.86 34.22 22.76 14.10 f,h,ij ^

10 Investment in Training 20.76 30.47 23.96 32.18 30.01 31.82 a,b,e 22.81 13.83 29.10 20.60 32.73 24.81 f,g,h,i,j 3

11 Job Discretion 2.87 2.66 2.50 2.40 2.59 2.58 e 2.92 2.43 2.57 2.41 2.61 2.50 f,g,i H

12 Education Level (up to age 16)+ 38.61 42.86 3.87 1.69 51.90 43.48 d,e 36.43 55.56 1.94 5.45 47.27 52.50 f,i,j p

13 % Absent 5.64 6.98 5.56 5.95 6.15 7.77 5.46 7.59 4.92 6.47 7.03 6.57 f,g,i . 14 Gender (% female) 64.33 79.29 67.73 77.50 65.47 71.00 a,b,c,e 64.99 69.89 70.87 67.11 71.07 59.02 h,i,j ?

15 % with < 1 Year Experience 26.08 10.71 30.83 12.32 31.34 27.66 a,b,d,e 22.72 36.97 20.67 36.75 28.73 33.00 f,g,i g

16 Performance-Based Pay 14.55 9.00 7.60 2.26 5.04 1.17 b,c,d,e 14.10 13.39 6.04 7.54 3.26 3.17 ij

17 Establishment Sizet+ 4.23 4.65 4.35 4.19 4.35 4.22 4.14 4.99 4.13 4.55 4.12 4.75 f,g,h C

18 AgeofEstablishment* 3.82 4.00 3.06 3.21 2.91 2.70 d,e 3.88 3.59 3.25 2.91 2.80 2.85 g,i,j ft

19 Sector: Telecom 50.71 69.05 18.38 24.19 9.09 9.33 a,d,e 58.50 15.63 12.74 27.43 5.74 22.22 f,g,h,i O

20 Call Type: Inbound 84.76 90.48 74.77 85.48 78.41 94.67 c,e 87.25 71.88 87.74 62.29 90.98 71.11 f,g,h ?3

Sample Size_464_387_167_464_387_167_ ^

Note: The extent of use of part-timers is the ratio of part-time agents to all employees/100 (range: 0-1) and the extent of use of temporary workers is the ratio of temporary agents |T^

to all employees/100 (range: 0-3). |j

In the "Significance" columns, the codes from a through^, all of which indicate statistical significance at the .05 level, refer to the following: a, union difference within the U.S.; b, i-h union difference within Canada; c, union difference within the U.K.; d, country difference among the three countries within the union sector; e, country difference among the three O

countries within the nonunion sector;/ in-house/outsourced difference within the U.S.; g, in-house/outsourced difference within Canada; h, in-house/outsourced difference within the ^

U.K.; i, country difference among the three countries within the in-house centers; j, country difference among the three countries within the outsourced centers.

+The percentage of individuals who have education up to but not beyond age 16 (i.e., did not complete a high school diploma) is extremely low in the Canadian sample in comparison 2

to the U.S. and U.K. samples. This figure should be interpreted as indicating a high level of education in the Canadian call center work force. <*

+tThe establishment age variable is a square root transformation and establishment size is a natural log transformation. ?5


Table 2. Pooled Correlation Matrix.

Variable Mean Std. Dev. 12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1 Annual Dismissal Rate 7.06 1.24 1

2 % Part-Time Agents* 21.03 28.69 .03 1 ^

3 % Temporary Agents* 9.91 3.10 -.02 .07 1 3

4 Dismissal Rate > 10% 28.62 45.22 .78 .03 .02 1 B

5 Incidence of Use of Part-Timers 65.62 47.52 .02 .50 .02 .01 1 S

6 Incidence of Use of Temps 31.34 46.41 -.01 -.01 .47-.02 .12 1 S

7 U.S. 45.58 49.83 .19 -.16 -.09 .20 -.28 -.13 1 ^ 8 Canada 38.02 48.57 -.07 .19 .07 -.11 .19 .06 -.72 1 Q

9 U.K. 16.40 37.05 -.17 -.03 .03 -.13 .13 .10 -.41 -.35 1 fn

10 Union Presence 19.32 39.50 -.21 .01 .03-.21 .08 .11 -.24 .01 .32 1 ^

11 Ownership: In-House 72.10 44.87 -.11 -.18 -.01 -.10 -.20 -.06 .29 -.30 .01 .13 1 t"1

12 % of Work Force in Teams 28.81 29.75 -.11 -.13 -.03 -.12 -.14 -.09 -.08 .18 -.12 -.04 .03 1 W 13 Investment in Training 24.43 18.89 -.18-.13 -.02-.19 -.07 -.04-.14 .03 .14 .21 .15 .07 1 ?

14 Job Discretion 2.67 .76 -.13-.21 -.12 -.10 -.27 -.16 .22 -.18 -.05 -.09 .20 .30 .06 1 W

15 Education Level (up to age 16)+ 27.09 44.46 .13 .01 .02 .11 .05 .03 .25 -.41 .21 .02 .04 -.17 -.08-.12 1 p

16 % Absent 5.89 5.58 .20 .18 .04 .20 .13 .05 -.02 -.04 .08 .11 -.09 -.16 -.04-.16 .08 1 3

17 Gender (% female) 67.40 22.53 -.09 .15-.01-.il .14 .08 -.07 .06 .01 .15 .02 -.06 .06-.18 .22 .01 1 h<

18 % with <1 Year Experience 26.68 24.09 .34 .15 .04 .33 .17 .06-.08 .04 .06 -.18-.24 -.17 -.23 -.21 .10 .19 -.08 1 g

19 Performance-Based Pay 9.63 2.17 .17-.05 -.03 .16 -.15 -.13 .20 -.12 -.13 -.15 .04 .03 -.04 .14 -.03 -.01 -.24 .04 1 ^

20 Establishment Sizet+ 4.29 1.35 .19 .09 .03 .17 .31 .13 -.02 .02 .00 .03-.18 -.34 -.03-.37 .07 .22 -.01 .27 -.05 1 d

21 Age of Establishment^ 3.39 1.38 -.10-.05 -.05 -.08 -.14 -.09 .30 -.17-.18 -.05 .15 .02 .12 .12 .06 -.09 .10 -.22 .04 -.10 1 C/3

22 Sector: Telecom 33.01 47.05 .05 -.23 -.13 .03 -.20 -.13 .38 -.23 -.22 -.06 .12 .06 -.06 .16 .07 -.08 -.10 -.08 .10 -.05 .08 1 L

23 Call Type: Inbound_81.83 38.58 -.19 -.24 -.02 -.16 -.02 .07 .08 -.11 .04 .12 .26 -.01 .18 .03 .04 -.13 .10 -.23 -.20 .01 .04 .05 1 1$

Significant at p < 0.05 for Irl > .06. O

Note: The extent of use of part-timers is the ratio of part-time agents to all employees/100 (range: 0-1) and the extent of use of temporary workers is the ratio of temporary agents ? }

to all employees/100 (range: 0-3). j>

fThe percentage of individuals who have education up to but not beyond age 16 (i.e., did not complete a high school diploma) is extremely low in the Canadian sample in comparison Z

to the U.S. and U.K. samples. This figure should be interpreted as indicating a high level of education in the Canadian call center work force. p* tfThe establishment age variable is a square root transformation and establishment size is a natural log transformation. 2

r o w z H W ox 00



Table 3a. Incidence of Dismissal (pooled data). Variable Model 1 Model 2 Model 3 Model 4

Institutional Characteristics

1 Canada -0.749(0.172)*** 0.195 (-4.890)*** -0.918(0.206)*** -0.962(0.262)*** 2 U.K. -1.513(0.337)*** 0.355 (-3.660)*** -1.274(0.362)*** -1.658(0.399)*** Organizational Characteristics

3 Union Presence 0.304 (-3.830)*** -1.183(0.310)*** -0.896(0.336)*** 4 Ownership: In-House 0.201 (-3.860)*** -0.552(0.209)*** -0.103(0.238) Functional Flexibility

5 % of Work Force in Teams -0.692 (0.310)** -0.253 (0.347) 6 Investment in Training -0.014 (0.005)*** -0.006 (0.006)

7 Job Discretion -0.319 (0.121)*** -0.210 (0.142) Control Variables

8 Education Level (up to age 16) 0.387 (0.238) 9 % Absent 5.760 (1.735)***

10 Gender (% Female) -0.874 (0.442)** 11 % with < 1 Year Experience 2.372 (0.423) *** 12 Performance-Based Pay 0.793 (0.429)* 13 Establishment Size 0.056(0.078) 14 Age of Establishment -0.140 (0.074)* 15 Sector: Telecommunications -0.006 (0.201) 16 Call Type: Inbound -0.432(0.243)* 17 Constant -0.508(0.104)*** 0.203(1.290) 1.448(0.373)*** 0.465(0.808) Sample Size 795 795 795 795 LR-chi2 36.88 74.91 102.55 192.30 Prob>x2 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.0388 0.0788 0.1078 0.2022

Wald x2 (df)_49.65 (2)*** 89.59 (3)*** 123.75 (9)*** *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

relation results shown in Table 2 support the insights provided by the mean comparisons presented in Tables la and lb.

The Effect of Institutions on Numerical Flexibility

We investigated the influence of institu tional factors, organizational characteristics, and functional flexibility on numerical flex ibility in the following manner. First, we evaluated the incidence of dismissal, part time workers, and temporary workers?that is, in each case, whether any use was in evi dence?using logistic regression; the results of the pooled analysis for each dependent variable are presented in Tables 3a, 4a, and 5a. Second, we estimated models of the intensity of reliance on dismissal, part-time

workers, and temporary workers with Tobit analysis; see Tables 3b, 4b and 5b. Third,

we added interaction terms to evaluate whether there are country-specific dynamics associated with managerial practices; these models are presented in Tables 3b, 4b, and 5b (Model 5). The addition of interaction terms appreciably increased the overall explanatory power of the models. Finally, given the improvement of the models with the inclusion of the interaction terms, we decided to separate the pooled dataset into country samples, not only to examine more closely which organizational characteristics were affecting the use of numerical flexibil ity, but also to explore country differences,

which might shed light on the specific fac tors that affect managerial decisions about work force flexibility. These results are presented in Tables 3c, 4c, and 5c. To test each hypothesis, we estimated five separate models, with the first model including the institutional characteristics. We added the



Table 3b. Extent of Dismissal (pooled data). Variable Model 1 Model 2 Model 3 Model 4 Model 5


1 Canada -0.037(0.010)*** -0.048(0.011)*** -0.046(0.011)*** -0.035(0.012)*** -0.174(0.038)*** 2 U.K. -0.096(0.016)*** -0.075(0.017)*** -0.077(0.017)*** -0.088(0.016)*** -0.195(0.065)*** Organizational Characteristics

3 Union Presence -0.076(0.014)*** -0.080(0.014)*** -0.061(0.013)*** -0.051(0.021)** 4 Ownership: In-House -0.055 (0.011)*** -0.039 (0.011)*** -0.004 (0.011) -0.011 (0.018) Functional Flexibility

5 % of Work Force in Teams -0.059(0.017)*** -0.017(0.016) -0.029(0.022) 6 Investment in Training -0.001(0.000)** 0.000(0.000) 0.000(0.000)

7 Job Discretion -0.030(0.007)*** -0.015(0.007)** -0.035(0.009)*** Control Variables

8 Education Level (up to age 16) 0.028 (0.011)** 0.022 (0.011)** 9 % Absent 0.263(0.081)*** 0.278(0.081)***

10 Gender (% Female) -0.036(0.021)* -0.042(0.021)** 11 % with < 1 Year Experience 0.129 (0.020)*** 0.129 (0.020)***

12 Performance-Based Pay 0.060(0.021)*** 0.067(0.021)*** 13 Establishment Size 0.016(0.004)*** 0.016(0.004)***

14 Age of Establishment -0.010(0.003)*** -0.010(0.003)*** 15 Sector: Telecommunications 0.005 (0.010) 0.013 (0.010)

16 Call Type: Inbound -0.034(0.012)*** -0.040(0.012)***

Country Interactions

17 Canada x Union Presence -0.026 (0.028) 18 Canada x In-House 0.019 (0.023) 19 Canada x % of Work Force in Teams 0.028 (0.032) 20 Canada x Investment in Training 0.000(0.001) 21 Canada x Job Discretion 0.045(0.013)*** 22 U.K. x Union Presence 0.004 (0.034) 23 U.K. x In-House -0.007 (0.035) 24 U.K. x % of Work Force in Teams 0.046 (0.071) 25 U.K. x Investment in Training 0.001(0.001) 26 U.K. x Job Discretion 0.035 (0.023) 27 Constant 0.069 (0.007)*** 0.125 (0.012)*** 0.225 (0.020)*** 0.084 (0.038)** 0.158 (0.043)*** Sample Size 795 795 795 795 795 LR-chi2 38.88 102.07 157.98 313.77 335.25 Prob>x2 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R2 -0.1363 -0.3579 -0.554 -1.1003 -1.1756 Wald F-Test (df)_31.29 (2) *** 22.34 (3) ***_17.89(9)*** 2.16(10,769)** *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

organizational characteristics to the second model, the functional flexibility measures to the third model, control variables to the fourth model, and interaction terms to the fifth model.

The full model in Table 3a, predicting the incidence of dismissals in call centers, supports part of Hypothesis 1: call centers located in the United States were more likely to dismiss employees than were their coun terparts in Canada (p < 0.01; Table 3a, row 1, Model 4) and the United Kingdom (p < 0.01; Table 3a, row 2, Model 4).

This pattern also holds for dismissal rates (Canada: p < 0.01, Table 3b, row 1, Model

4; United Kingdom: p < 0.01, Table 3b, row 2, Model 4), supporting Hypothesis l's prediction of higher dismissal rates in U.S. call centers than in Canadian or U.K. call centers. Institutional differences such as stronger employment regulations and labor regulations in Canada and the United King dom than in the United States may explain these differences.

Perhaps reflecting their disinclination to use the dismissal option, call centers in Canada and the United Kingdom were more likely than those in the United States to use part-timers, consistent with Hypothesis 2a (Canada: p < 0.01, Table 4a, row 1, Model



Table 3c. Extent of Dismissal by Country. Variable U.S. Canada U.K.

Organizational Characteristics

1 Union Presence -0.069 (0.024)*** -0.097 (0.019)*** -0.040 (0.018)** 2 Ownership: In-House -0.040 (0.021)* -0.027 (0.014)** -0.041 (0.019)** Functional Flexibility

3 % of Work Force in Teams -0.069 (0.025)*** -0.043 (0.023)* -0.061 (0.044) 4 Investment in Training -0.001(0.000)* -0.001(0.000)** 0.000(0.000) 5 Job Discretion -0.046(0.010)*** -0.012(0.010) -0.003(0.014) 6 Constant 0.274(0.029)*** 0.134(0.025)*** 0.067(0.039)* Sample Size 409 324 99 LR-chi2 66.78 56.24 14.54 Prob>x2 0.0000 0.0000 0.0125

Pseudo R2_-0.4124_-0.3476_-0.3137 *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

4; United Kingdom: p < 0.01, Table 4a, row 2, Model 4).

Centers in Canada (p < 0.01; Table 4b, row 1, Model 4) and those in the United Kingdom both made more extensive use of part-timers than did their U.S. counterparts, although once the control variables were added, the relationship was no longer significant in the U.K. sample. These patterns in non-standard use provide partial support for Hypothesis 2a?both U.K. and Canadian call centers were more likely to use part-timers than U.S. centers, and the direction of the relationship for extent of use of part-timers is positive in both the United Kingdom and Canada, but remains statistically significant only in the Canadian sample.

We find similar patterns in the likelihood and extent of use of temporary workers. Call centers in Canada and the United Kingdom (p < 0.1; Table 5a, row 2, Model 4) were more likely to use temporary workers than were

U.S. call centers, but with the addition of the control variables, only the United Kingdom results remain statistically significant. Call centers in Canada and the United Kingdom relied on temporary workers to a greater extent than those in the United States, al though the relationship is only marginally significant in both cases (p < 0.1; Table 5b, rows 1 & 2, Model 4). These findings offer partial support for Hypothesis 2b: relative to U.S. centers, centers in the United Kingdom were more likely to use temporary workers,

and centers in both Canada and the United Kingdom made significantly more extensive use of them. Our findings with respect to non-standard employment contracts suggest that both the likelihood of use of these ar rangements and the intensity of their use may be influenced by institutional context.

Turning to the effect of union presence, we find support for Hypothesis 3, as shown in Tables 3a and 3b: union presence reduced the likelihood of dismissal (p < 0.01; Table 3a, row 3, Model 4) and was associated with lower dismissal rates (p < 0.01; Table 3b, row 3, Model 4). We further evaluate whether the relationship between unions and dismissal rates varied across the three countries by adding country interaction terms to the model; the results of this analysis for dismissal rates are presented in Table 3b. The country union interactions are not statistically significant, and so there appear to have been no significant differ ences either between Canadian and U.S. centers or between U.K. and U.S. centers in the relationship between unions and dismissal rates (Table 3b, rows 17 & 22, Model 5). We also examine within-country differences (Table 3c), and find that in all three countries, unionized call centers had significantly lower dismissal rates than did non-union call centers (Table 3c, row 1) (United States: p < 0.01; Canada: p<0.01;

United Kingdom: p<0.05). We find no support for either Hypothesis



Table 4a. Incidence of Use of Part-Time Workers (pooled data). Variable Model 1 Model 2 Model 3 Model 4


1 Canada 1.116 (0.160)*** 0.870 (0.169)*** 0.961 (0.188)*** 1.038 (0.226)*** 2 U.K. 1.460(0.271)*** 1.313(0.285)*** 1.395(0.299)*** 1.376(0.335)*** Organizational Characteristics

3 Union Presence 0.227 (0.215) 0.166 (0.226) 0.086 (0.238) 4 Ownership: In-House -0.915 (0.200)*** -0.642 (0.209)*** -0.411 (0.223)* Functional Flexibility

5 % of Work Force in Teams -0.935 (0.277)*** -0.263 (0.301) 6 Investment in Training -0.013(0.004)*** -0.015(0.005)***

7 Job Discretion -0.564(0.112)*** -0.225(0.125)* Control Variables

8 Education Level (up to age 16) 0.320 (0.220) 9 Gender (% Female) 1.029(0.395)*** 10 % with < 1 Year Experience 0.511 (0.415) 11 Performance-Based Pay -0.478 (0.431) 12 Establishment Size 0.501 (0.078)*** 13 Age of Establishment -0.001 (0.064) 14 Sector: Telecommunications -0.427 (0.185)** 15 Call Type: Inbound 0.133(0.246) 16 Constant 0.061 (0.097) 0.838 (0.201)*** 2.753 (0.365)*** -1.419 (0.749)* Sample Size 873 873 873 873 LR-chi2 68.56 91.30 157.49 231.38 Prob>x2 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.0607 0.0808 0.1394 0.2048

Wald x2 (df)_5.52 (2)_50.84 (3)*** 45.13 (9)*** * Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

4a or Hypothesis 4b: union presence appears not to be significantly associated either with the likelihood that part-time or temporary workers were used or with the extent of use (see Tables 4b and 5b). We added interaction terms to the models predicting intensity of use, and we find no evidence of cross-national differences in the relationship between union presence and the extent of use of part-timers (Table 4b, rows 17 and 22). We also found no evidence that union presence is significantly related to the extent of use of part-time workers in the within-country analysis, although it is noteworthy that union presence is negatively related to the extent of part-time use in the U.S. sample, whereas in both the Canadian and U.K. samples union presence is positively related to the extent of part-time use (Table 4c, row 1).

Turning to the extent of temporary use, despite no independent effect of union on its own, the country x union interaction is

marginally significant for Canada (p < 0.01; Table 5b, row 17, Model 5) meaning that the negative impact of union presence on the extent of temporary use is weaker in Canada than the United States. This finding suggests that where unions are present in call centers in Canada, they do not object as strongly to the extent of temporary use as do their U.S. counterparts. We investigate this relationship further in the within-country analysis, and find that within all three coun tries, union presence did not significantly affect the extent of temporary use.

Yet, the signs of the beta coefficients suggest that union presence has a negative influence on the extent of temporary use in the United States and the United Kingdom, and a positive influence on the extent of temporary use in Canada.

There are three possible ways to interpret these results. First, unions in the service sec tor may lack the bargaining power to influ



Table 4b. Extent of Use of Part-Time Workers (pooled data). Variable Model 1 Model 2 Model 3 Model 4 Model 5


1 Canada 0.191(0.029)*** 0.149(0.031)*** 0.164(0.032)*** 0.115(0.035)*** -0.107(0.112) 2 U.K. 0.118 (0.043)*** 0.091 (0.046)** 0.099 (0.045)** 0.032 (0.046) -0.379 (0.178)** Organizational Characteristics

3 Union Presence 0.032 (0.038) 0.036 (0.037) 0.044 (0.036) -0.015 (0.068) 4 Ownership: In-House -0.133(0.032)*** -0.076(0.032)** -0.038(0.032) -0.099(0.054)* Functional Flexibility

5 % of Work Force in Teams -0.188(0.049)*** -0.133(0.049)*** -0.206(0.072)*** 6 Investment in Training -0.003(0.001)*** -0.003(0.001)*** -0.006(0.001)***

7 Job Discretion -0.085(0.019)*** -0.049(0.019)** -0.060(0.028)** Control Variables

8 Education Level (up to age 16) 0.022 (0.034) 0.014 (0.034) 10 Gender (% Female) 0.258(0.064)*** 0.256(0.065)***

11 % with < 1 Year Experience 0.100 (0.061) 0.090 (0.061) 12 Performance-Based Pay -0.052(0.068) -0.043(0.068)

13 Establishment Size 0.020(0.011)* 0.022(0.011)** 14 Age of Establishment 0.004(0.010) 0.007(0.010) 15 Sector: Telecommunications -0.153 (0.029)*** -0.136 (0.030)***

16 Call Type: Inbound -0.121(0.036)*** -0.135(0.036)***


17 Canada x Union Presence 0.092(0.084) 18 Canada x In-House 0.085 (0.068) 19 Canada x % of Work Force in Teams 0.084 (0.098) 20 Canada x Investment in Training 0.004 (0.002)** 21 Canada x Job Discretion 0.021(0.039) 22 U.K. x Union Presence 0.043 (0.098) 23 U.K. x In-House 0.142(0.099) 24 U.K. x % of Work Force in Teams 0.251 (0.179) 25 U.K. x Investment in Training 0.008 (0.002)*** 26 U.K. x Job Discretion 0.014 (0.063)

27 Constant 0.027(0.021) 0.139(0.034)*** 0.446(0.057)*** 0.175(0.114) 0.331(0.131)** Sample Size 862 862 862 862 862 LR-chi2 42.6 59.74 131.24 199.14 227.58 Prob>x2 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.0423 0.0592 0.1302 0.1975 0.2257 Wald F-Test (df)_11.79(2)*** 25.14(3)***_8.42(9)*** 2.79(10,837)***

*Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

ence decisions about the use of non-standard contracts. Second, the use of non-standard contracts may be a common feature in both unionized and non-union workplaces, and unions (when present) may tolerate their use given the dramatic call volume fluctuations call centers may experience. Third, the sig nificance of the Canada x union interaction

in the pooled analysis, coupled with the nega tive association between union presence and temporary use in the United States and the positive association in the Canadian sample, suggests that unions in the United States and Canada react differently to an increase in the extent of temporary work. One way

to interpret this difference is as the result of divergent union strategies in response to the use of temporary workers (Heery 2004).

The Effect of Outsourcing on Numerical Flexibility

Our results offer some qualified support for Hypothesis 5a: compared to managers of in-house centers, managers of outsourced centers, we found, were more likely to dismiss workers (p < 0.01; Table 3a, row 4, Model 3) and had higher dismissal rates (p < 0.01; Table 3b, row 4, Model 3), but the statistical significance of the effect of ownership status



Table 4c. Extent of Use of Part-Time Workers by Country. Variable U.S. Canada U.K.

Organizational Characteristics 1 Union Presence -0.038(0.078) 0.065(0.053) 0.011(0.050) 2 Ownership: In-House -0.196 (0.061)*** -0.035 (0.042) 0.063 (0.058) Functional Flexibility

3 % of Work Force in Teams -0.318 (0.081)*** -0.148 (0.070)** 0.000 (0.117) 4 Investment in Training -0.006(0.001)*** -0.003(0.001)** 0.001 (0.001) 5 Job Discretion -0.116(0.030)*** -0.062(0.029)** -0.042(0.040)

6 Constant 0.708(0.091)*** 0.511(0.074)*** 0.181(0.112) Sample Size 420 339 103 LR-chi2 86.45 24.37 3.56 Prob>x2 0.0000 0.0002 0.6140 Pseudo R2 0.1608 0.0678 0.0983

* Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

disappears in both cases with the addition of control variables. An analysis that added interaction terms to the model predicting dismissal rates yielded no evidence of cross national differences. Within each country sample, in-house centers had significantly lower dismissal rates than outsourced centers

(Table 3c, row 2) (United States: p < 0.1; Canada: p< 0.05; United Kingdom: p<0.05).

We also find some support for Hypothesis 5b: outsourced centers were more likely than in-house centers to make some use of part-time workers (p < 0.1; Table 4a, row 4, Model 4). Outsourced centers also used part-timers to a greater extent than in-house centers, but the relationship is not significant once the control variables are added to the analysis (Table 4b, row 4, Model 4). With the addition of the country interactions, we also find no evidence of cross-national differences

in the use of part-time workers by in-house and outsourced centers, although the main effect becomes statistically significant (p < 0.1; Table 4b, row 4, Model 5).

Some interesting within-country differ ences emerge in the relationship of ownership status to the extent of part-time use. In the United States, in-house call centers tended to use part-timers less intensively than outsourced centers did (p < 0.01; Table 4c, row 2), whereas ownership status did not have a statistically significant effect on the extent of part-time use in the United Kingdom or Canada.

We do not find support for Hypothesis 5c in the pooled analysis: whether a center was in-house or outsourced was not signifi cantly related to either the likelihood of use or extent of use of temporary workers (Table 5a, row 4, Model 4, and Table 5b, row 4, Model 4). A comparative country analysis turned up no evidence of statisti cally significant differences across countries in the intensity of temporary worker use by in-house and outsourced call centers (Table 5b, row 4, Model 5).

Turning to within-country differences, we find (a) marginally significant evidence that in the United States, in-house centers relied to a lesser extent on temporary workers than did outsourced centers (p < 0.1; Table 5c, row 2); (b) marginally significant evidence that in Canada, in-house centers relied to a greater extent on temporary workers than did their outsourced counterparts (p < 0.1; Table 5c, row 2); and (c) non-significant evi dence that in the United Kingdom, in-house centers used temporary workers less inten sively than did outsourced centers Table 5c, row 2). We would speculate, based on these results, that U.S. in-house call centers were reluctant to use temporary workers as an expedient to achieve numerical flexibility, perhaps because they feared an erosion of customer service quality, whereas Canadian in-house centers viewed temporary work ers as an important source of work force



Table 5a. Incidence of Temporary Worker Use (pooled data). Variable Model 1 Model 2 Model 3 Model 4


1 Canada 0.509(0.159)*** 0.434(0.170)** 0.437(0.180)** 0.324(0.213) 2 U.K. 0.842 (0.226)*** 0.696 (0.240)*** 0.695 (0.246)*** 0.471 (0.270)* Organizational Characteristics

3 Union Presence 0.348 (0.197)* 0.323 (0.203) 0.274 (0.212) 4 Ownership: In-House -0.144(0.175) 0.021(0.181) 0.001(0.191) Functional Flexibility

5 % of Work Force in Teams -0.483 (0.282)* -0.230 (0.303) 6 Investment in Training -0.006 (0.004) -0.008 (0.005)*

7 Job Discretion -0.334(0.111)*** -0.211(0.120)** Control Variables

8 Education Level (up to age 16) 0.127 (0.204) 9 Gender (% Female) 0.427 (0.397) 10 % with < 1 Year Experience 0.137 (0.365) 11 Performance-Based Pay -0.912 (0.485)* 12 Establishment Size 0.154 (0.066)** 13 Age of Establishment -0.049 (0.062) 14 Sector: Telecommunications -0.499 (0.180)*** 15 Call Type: Inbound 0.451(0.229)** 16 Constant -1.108(0.113)*** -1.019(0.186)*** 0.034(0.331) -1.231(0.689)* Sample Size 873 873 873 873 LR-chi2 18.15 21.5 42.01 69.63 Prob>x2 0.0001 0.0003 0.0000 0.0000 Pseudo R2 0.0167 0.0198 0.0387 0.0642

Wald x2 (df)_8.60 (2)**_22.07 (3)*** 23.37 (9)*** *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

flexibility to address the stricter dismissal regulations they faced.

In sum, we find that across all three coun tries studied, dismissal rates were signifi cantly lower in unionized and in-house call centers than in non-union and outsourced centers. Also, in the pooled analysis, union presence was not significantly related to either the likelihood of use or intensity of use of part-time or temporary workers. With respect to part-time employment contracts, whether the center was in-house or outsourced clearly mattered, with the incidence of part-time use being lower in in-house centers than outsourced centers at a marginal level of significance. In the within-country analysis, ownership status is related to the extent of temporary and part-time use, albeit in different ways. Both institutional and organizational factors ap pear to influence how managers in these three countries deployed these numerical

flexibility strategies, but not necessarily in a consistent manner.

The Relationship between Functional Flexibility and Numerical Flexibility

We find some support for Hypothesis 6, which predicted that call centers use numeri cal flexibility and functional flexibility as sub stitutes. The strength of these relationships varies by the type of numerical flexibility we predict, and by country.

The incidence of dismissal is negatively associated with each measure of functional flexibility at varying levels of significance, although the significance of these rela tionships disappears with the addition of the control variables. With regard to dismissal rates, all three measures of func tional flexibility are significantly related to lower dismissal rates, consistent with our findings about the incidence of dismissal.



Table 5b. Extent of Use of Temporary Workers (pooled data). Variable Model 1 Model 2 Model 3 Model 4 Model 5


1 Canada 0.189(0.057)*** 0.178(0.061)*** 0.164(0.063)** 0.122(0.074)* -0.268(0.238) 2 U.K. 0.290 (0.082)*** 0.274 (0.087)*** 0.267 (0.087)*** 0.170 (0.093)* 0.055 (0.358) Organizational Characteristics

3 Union Presence 0.034 (0.072) 0.009 (0.073) 0.007 (0.074) -0.194 (0.160) 4 Ownership: In-House -0.026(0.062) 0.029(0.063) 0.022(0.066) -0.111(0.113) Functional Flexibility

5 % of Work Force in Teams -0.122 (0.099) -0.049 (0.103) -0.154 (0.161) 6 Investment in Training -0.001 (0.002) -0.002 (0.002) -0.004 (0.003)

7 Job Discretion -0.142(0.039)*** -0.107(0.042)** -0.107(0.061)* Control Variables

8 Education Level (up to age 16) 0.075 (0.071) 0.049 (0.072) 10 Gender (% Female) 0.030 (0.135) 0.049 (0.137)

11 % with < 1 Year Experience 0.002 (0.124) 0.035 (0.126) 12 Performance-Based Pay -0.137(0.150) -0.128(0.150)

13 Establishment Size 0.034(0.023) 0.035(0.023) 14 Age of Establishment -0.022(0.021) -0.022(0.021)

15 Sector: Telecommunications -0.222(0.062)*** -0.184(0.065)*** 16 Call Type: Inbound 0.130(0.078)* 0.112(0.078)


17 Canada x Union Presence 0.346(0.188)* 18 Canada x In-House 0.231(0.141) 19 Canada x % of Work Force in Teams 0.203 (0.210) 20 Canada x Investment in Training 0.004 (0.004) 21 Canada x Job Discretion 0.011(0.085) 22 U.K. x Union Presence 0.064 (0.212) 23 U.K. x In-House 0.072 (0.196) 24 U.K. x % of Work Force in Teams 0.088 (0.389) 25 U.K. x Investment in Training 0.003 (0.004) 26 U.K. x Job Discretion 0.006(0.129)

27 Constant -0.468 (0.049)*** -0.449 (0.071)*** -0.035 (0.117) -0.245 (0.236) -0.054 (0.274) Sample Size 864 864 864 864 864 LR-chi2 17.76 18.1 38.9 61.64 75 Prob>x2 0.0001 0.0012 0.0000 0.0000 0.0000 Pseudo R2 0.0164 0.0168 0.036 0.0571 0.0694

F-Test (df)_1.38 (2)_8.13 (3)***_2.17 (9)**_1.30 (10, 839) * Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

With the addition of the control variables, onlyjob discretion (p < 0.05; Table 3b, row 7, Model 4) continues to be significantly associated with lower dismissal rates. The country interactions show that the negative relationship between job discretion and dismissal rates was weaker in Canada (p < 0.01; Table 3b, row 21, Model 5) than in the United States.

Analyzing three countries together may obscure unique relationships that exist within each country. We thus investigated within country differences. Among call centers in the United States, all three measures of functional flexibility were significantly related to lower dismissal rates. In call centers in

Canada, the use of teams (p < 0.1; Table 3c, row 3) and investment in training (p < 0.05; Table 3c, row 4) were significantly related to lower dismissal rates. In contrast, in the U.K. sample, we do not find evidence of a significant relationship between functional flexibility and lower dismissal rates. Taken together, the results from the United States and Canada support the substitution argu ment, with the strength of the relationship depending on the dimension of functional flexibility.

Turning to non-standard work arrange ments, all three measures of functional flex ibility were significantly related to a lower incidence of use of part-time workers (p <



Table 5c. Extent of Use of Temporary Workers by Country. Variable U.S. Canada U.K.

Organizational Characteristics

1 Union Presence -0.194(0.127) 0.164(0.112) -0.091(0.156) 2 Ownership: In-House -0.152(0.089)* 0.154(0.093)* -0.009(0.177) Functional Flexibility

3 % of Work Force in Teams -0.240 (0.126)* 0.019 (0.151) -0.153 (0.398) 4 Investment in Training -0.003 (0.002) 0.000 (0.003) -0.001 (0.004) 5 Job Discretion -0.138(0.047)*** -0.116(0.067)* -0.112(0.127)

6 Constant 0.296 (0.133)** -0.167 (0.171) 0.193 (0.351) Sample Size 422 339 103 LR-chi2 32.05 9.69 2.06 Prob>x2 0.0000 0.0845 0.8407

Pseudo R2_0.0752_0.0206_0.0129 *Statistically significant at the .10 level; **at the .05 level; ***at the .01 level.

0.01; Table 4a, rows 5-7, Model 3). When control variables are included, the estimates show that centers investing more in training (p < 0.01; Table 4a, row 6, Model 4) and

where agents had more discretion on the job (p < 0.1; Table 4a, row 7, Model 4) were less likely to rely on part-timers. As for the intensity of use of part-time workers, we find statistically significant negative associations between this variable and all three measures of functional flexibility; moreover, unlike the results for the incidence of part-time use, this finding persists with the addition of the control variables.

Results of analyses incorporating the inter action terms indicate that the negative rela tionship between investment in training and intensity of use of part-time workers varied,

with a weaker relationship existing in Canada (p < 0.05; Table 4b, row 20, Model 5) and the

United Kingdom (p < 0.01; Table 4b, row 25, Model 5) than in the United States. We also examined within-country differences (Table 4c). Among centers in the United States and Canada, the three measures of functional flexibility are significantly and negatively related to the extent of use of part-timers, whereas in the United Kingdom, no signifi cant relationships are found. In contrast to the other two countries, although the results are not significant, only job discretion in the United Kingdom is negatively related to less extensive use of part-timers, while the other two measures of functional flexibility

are positively related. Overall, call center managers in the United States and Canada might find the use of functional flexibility a substitute for the use of part-time workers.

As with the use of part-time workers, we find that some dimensions of functional flex

ibility are related to the incidence of use of temporary workers in the pooled analysis. Investment in training (p < 0.1; Table 5a, row 6, Model 4) and the degree of job discre tion (p < 0.05; Table 5a, row 7, Model 4) are both negatively related to the incidence of temporary worker use. Only one measure of functional flexibility?the degree of job discretion an agent has?is negatively asso ciated with the extent of use of temporary workers (p < .05; Table 5b, row 7, Model 4). Turning to the interaction terms, we find no statistically significant differences across the three countries.

Some interesting findings emerge from the analysis of within-country differences. In the United States, the percentage of agents in teams (p < 0.1; Table 5c, row 3) and the degree of discretion an agent has (p < 0.01; Table 5c, row 5) are associated with reduced levels of use of temporary workers. In Canada, only the level of discretion (p < 0.1; Table 5c, row 5) is negatively associated with the extent of temporary use. In the United Kingdom, we find no statistically significant relationships.

In summary, the results reported above reveal some differences in the strength and direction of the relationship between



functional flexibility and the three forms of numerical flexibility we examine in this paper. In the pooled analysis, the strongest evidence that employers use numerical and functional flexibility as substitutes comes from our models of the extent of part-time use and functional flexibility. Of the three functional flexibility measures, once the control variables are added to the analysis, only job discretion is significantly related to lower dismissal rates and less reliance on temporary worker use. Across the pooled analysis, the relationship between numeri cal flexibility and functional flexibility is for the most part negative. However, there is variation in statistical significance across these relationships. These findings suggest that firms may configure various forms of numerical flexibility and functional flexibility in different ways depending on the pressures they face.

The within-country analysis yields some evidence that the dismissal rate and the ex tent of use of part-timers are substitutes for functional flexibility in the United States and Canada. In the United Kingdom, however, we find no evidence of statistically significant relationships between the measures of func tional flexibility and numerical flexibility. For example, although we find negative relationships between all three measures of functional flexibility and the extent of temporary worker use in both U.S. and U.K. call centers, the only significant relationships were found in the United States. In Canada, only job discretion was negatively related to the extent of temporary use at a marginal level of significance. These results suggest that the relationship between functional flexibility and numerical flexibility may also depend on the institutional context.

Discussion and Conclusions

A central theme reverberating through this analysis is whether national institutions influence managerial decisions about work force flexibility in call centers operating within liberal market economies. Overall, the findings from this analysis have three

main implications. First, despite pressures for convergence in employment practices, we

find that even within liberal market econo mies, national institutions have a marked effect on managerial strategies for achieving numerical flexibility. Second, whether a firm is an in-house or outsourced operation affects decisions about numerical flexibility strategies. Third, we find some differences in the strength and direction of the association between the three types of numerical flex ibility we evaluate and functional flexibility.

Both institutional and organizational fac tors shape the numerical flexibility strategies adopted by call center managers in the United States, Canada, and the United Kingdom. We find that managers in the Canadian and U.K. samples were less likely to resort to dismissal than those in the U.S. sample, consistentwith Colvin's (2006) comparison of dismissal rates in establishments located in Pennsylvania and Ontario. Perhaps the stricter dismissal regulations in the United Kingdom and Canada than in the United States?notably a requirement of notice (or pay in lieu of notice)?dampen employers' willingness to dismiss employees. The stronger union threat effect in the United Kingdom and Canada characterized by higher union density and stronger institutional support for unions than in the United States may also help to explain these findings. For example, manag ers of non-union call centers in Canada may be more likely to comply with employment regulations in order to avoid unionization (Godard2009).

In the pooled analysis, we find that union ized call centers had significantly lower dismissal rates than non-union call centers, but we do not find cross-national differences in the relative strength of this relationship. That is, dismissal rates in unionized firms in Canada and the United Kingdom do not differ significantly from dismissal rates in unionized firms in the United States. In the within-country analysis, even in the United States, with its struggling labor movement, union presence still appears to have reduced dismissal rates. This particular finding must be treated cautiously, however, given that the U.S. study oversampled the telecom

munications industry, historically a union stronghold. Similar to the U.S. case, union presence also reduced dismissal rates in call



centers in Canada and the United Kingdom. On one hand, an optimistic interpretation

of these findings is that unions in these three countries are still able to influence employee outcomes in these workplaces. On the other hand, unions in several countries have en countered difficulties organizing call centers because of the short-term nature of these jobs even when they are full-time positions, and the footloose nature of this sector. One

possible explanation for the cross-national similarities is that unionized call centers in each of the countries are not newly organized workplaces, but are in traditionally unionized industries such as telecommunications where

the collective agreements are comprehensive and better able to protect job security for union members.

We also find that institutions influence the likelihood and intensity of use of part time and temporary workers. The strongest evidence of how institutions shape the use of non-standard arrangements emerged from the likelihood of part-time use in call centers in both Canada and the United Kingdom, which was significantly higher than in U.S. based call centers. The extent of reliance on part-timers was also significantly higher in Canadian than U.S. call centers. The presence of stronger dismissal regulations in Canada and the United Kingdom than in the United States may explain why call centers in Canada and the United Kingdom are more likely to use part-timers. Thus, our findings offer additional support for how the strength of dismissal regulations may have the unintended consequence of encouraging firms to hire non-standard workers (Olsen and Kalleberg 2004; Autor 2003).

Some interesting patterns emerge in how firms use temporary workers. Consistent with the likelihood of part-time use, we find that call centers in Canada and the United Kingdom are more likely to use temporary workers than U.S. centers, but with the addi tion of control variables only the U.K. results are marginally significant. We also find that centers in Canada and the United Kingdom use temporary workers to a greater extent than U.S. centers. Similar to our interpre tation of part-time use, the strength of dis

missal regulations may also help explain these

patterns. Alternatively, weak non-standard regulations may also offer some insights about the patterns in non-standard use we find. In Canada, until recently, temporary agency workers had clearer rights than in the United Kingdom, and the blurriness of their rights in the United Kingdom coupled with stricter dismissal regulations may have encouraged employers to hire them. Meanwhile, in the United States, employers can more easily churn through full-time employees given the more relaxed dismissal regulations than in the United Kingdom and Canada, and the short term nature of these jobs, thus reducing the need to hire part-time and temporary work ers for the purposes of numerical flexibility.

The legal protection of part-time and temporary employment classifications also differs from one institutional setting to the next and has important implications for both the work force and employers. For example, a part-time employee in Canada would be covered by universal healthcare, whereas in the United States a part-time em ployee would not necessarily be eligible for health insurance. Employers in the United Kingdom are required to provide part-time employees with rights and benefits similar to those for their full-time counterparts. These legal distinctions are important because the

more widespread use of non-standard employ ment contracts in Canada and the United Kingdom does not necessarily signal that a low-road approach is being used. Rather, contextual differences related to the non standard classifications need to be taken into consideration to understand the implications of these patterns.

We also considered the possibility that union presence supported by stronger labor regulations and reflected in higher union density in Canada and the United Kingdom, in combination with stricter dismissal regu lations, may explain the divergent patterns

we find in the incidence and intensity of use of non-standard employment arrangements across these three countries.

At the workplace level, we found that union presence did not influence the likelihood or extent of part-time and temporary use by call center managers in either the pooled or within-country analyses, lending support to



other studies that have found no relationship between non-standard work arrangements and union presence (Abraham and Taylor 1996; Houseman 2001). Several explana tions may account for this finding in the call center context. First, union presence in the service sector is weak, and therefore, where unions are present, they may be unable to influence decisions about the use of non standard contracts in contrast to other studies

in manufacturing workplaces where union presence was significantly related to less use of temporary workers (Vidal and Tigges 2009). Second, from a more optimistic perspective, unions representing call center workers may recognize the inherent flexibility required to be competitive in this free-wheeling industry and realize that discouraging the use of non standard contracts in this context could be

counterproductive. Third, the differences in working conditions between standard and non-standard classifications maybe narrower in call centers than in industries where the use of non-standard arrangements is less common. Finally, the apparent lack of as sociation between union presence and the use of non-standard employment contracts may be spurious, and simply result from the crudeness of our measure of union presence. Using the percentage of union members in the workplace might offer richer insights.

The outsourcing of customer service work has received much media attention, especially the international outsourcing of work from the United States and United Kingdom to India. In this paper, however, the subject of our study is outsourced call centers that have stayed within liberal market economies. A substantial amount of customer service work is outsourced within and between lib eral market economies (UNCTAD 2004).

While research attention has shed light on why firms outsource work from a higher wage country to a lower-wage country, much remains to be learned about the nature of employment practices in outsourced firms operating in high-wage economies. Even when outsourced firms are operating within liberal market economies, consistent with a low-cost, low-road management strategy, our

within-country analysis for all three countries revealed that dismissal rates were significantly

lower in in-house centers than in outsourced

centers; and a pattern of greater extent of use of part-timers by outsourced centers than by in-house centers was particularly pronounced for call centers within the United States. Considering temporary workers, our within country analyses revealed different patterns by country: in the United States, in-house centers relied to a lesser extent on temporary workers than did outsourced centers, whereas in Canada, the opposite relationship held. Even when outsourced firms are operating within liberal market economies, their jobs are more cost-sensitive and less stable than

jobs in in-house call centers. In Canada, in house call centers?influenced, in part, by stricter dismissal regulations?may be more inclined than outsourced call centers to view temporary workers as an opportunity for employers to screen prospects for full time positions. Conversely, U.S. in-house centers may be less likely to use temporary workers than U.S. outsourced centers be cause the type of work in-house centers in the United States handle may require more extensive training than work performed in outsourced centers.

Beyond the institutional effects on nu merical flexibility, we also evaluated whether numerical flexibility was related to functional flexibility. In the pooled analysis, we found that an increase in job discretion was associ ated with a decrease in dismissal rates. The within-country analysis provided additional evidence that numerical flexibility and func tional flexibility are substitutes. Our finding that functional flexibility had stronger effects on dismissal rates in the United States and Canada than in the United Kingdom suggests that managers need to be cautious when combining different means for enhancing work force flexibility, since an increase in dismissal rates may undermine the benefits of functional flexibility.

With regard to non-standard employment contracts, our finding that functional flex ibility was a substitute for the use of part-time workers supports the view that these two types of work force flexibility have opposing objectives, at least in the call center context (Cappelli and Neumark 2004; Cully et al. 1999; Osterman 1994). When we examined



this relationship on a country-by-country basis, we found that all three measures of functional flexibility were negatively and significantly related to the extent of use of part-time workers in both the United States and Canada, but not in the United Kingdom. Recent U.K. legislative initiatives devised to require employers to treat part-timers in the same manner as full-timers may account for the different relationship between the extent of part-time use and functional flexibility in the United Kingdom. It is possible that the absence of regulations requiring equal treatment in the United States and Canada provides employers with an incentive to use part-timers. The presence of equal treat ment regulations in the United Kingdom reduces the distinction between full-time and part-time status and thereby erodes the incentive for employers to hire part-timers for the purposes of cost-cutting or increas ing flexibility. We also find similar patterns in the analysis

of temporary work and functional flexibility. While the direction of the relationships be tween functional flexibility and the likelihood of and the extent of use of temporary work ers is consistently negative, there are some variations in the significance levels of these relationships. In the within-country analysis, the effect on the use of temporary workers is less clear. In the United States, employee discretion and use of teams were negatively associated with the extent of reliance on tem porary workers. Similarly, Canadian centers where agents had more discretion relied to a lesser extent on temporary workers. In contrast to the U.S. findings, however, there is no relationship between any form of func tional flexibility and the extent of reliance on temporary workers in the United Kingdom. These findings may suggest that employers do use temporary and part-time workers in different ways. From one institutional con text to the next, the relationship between the full-time and the non-standard work force may widen or narrow, altering the trade-off between numerical and functional flexibility.

As with any study, ours has some limita tions. First, the relatively short length of the survey limited the number of variables that could be included in the analysis. Second,

for each call center, a single managerial representative provided survey responses, and one might question the reliability of such single-source data. Third, the survey's

mixture of question types, with most calling for subjective judgments but some calling for facts (such as performance metrics and number of employees), could have confused some respondents. Fourth, managers have

more choices of ways to improve numerical and functional flexibility than our study investigates. Looking at a greater range of types of numerical and functional flexibility would enrich our understanding of how and under what circumstances firms mix these strategies together.

This study represents only a first step in understanding how institutional and orga nizational factors influence decisions about work force flexibility. The limitations of the study remarked above are suggestive of pos sible research extensions. First, in particular, as noted, we used a fairly narrow definition of numerical flexibility. Future studies could examine the effect of institutional characteristics on a measure of numerical flexibility that includes an expanded variety of non-standard work arrangements (adding, for example, independent contractors) and differentiates between dismissals for the pur poses of work force adjustment and dismissals for cause. Second, a deeper understanding of institutional effects could be developed if a broader set of institutional measures were used. Third, several legislative initiatives were recently passed in the United Kingdom to reduce the inequalities between non standard workers and full-time employees, and studying how these influence employer decisions about numerical flexibility will add important insights to our understanding of patterns in the use of non-standard workers. Fourth, this study occurred during a period of positive economic conditions, which colored our conclusions. Subsequent studies could adopt a longitudinal approach and include a period of negative economic conditions, which could offer alternative insights to the trends we identified. Such refinements

could yield new insights into the dynamics of converging employment practices in an era of globalization.



Some of this study's conclusions are noteworthy. Our evidence suggests that even within liberal market economies where firms encounter few limitations on decisions

about numerical flexibility, institutional differences influence these decisions, albeit to varying degrees across countries. We also found that union presence reduced dismissal rates but, at the same time, did not affect firm decisions about whether to use non-standard work arrangements or the

extent of their use. Finally, our findings suggest that service-sector firms use some forms of functional flexibility as substitutes for numerical flexibility. Of course, such differences, one may argue, are merely transitory and eventually institutions will converge to produce identical outcomes around the globe. Future research address ing these questions will further contribute to the debate around converging employment practices in an era of globalization.

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