This survey study among 111 teleworkers in a bank organization investigated the relationship between telework intensity and individual productivity, and whether this relationship was mediated by employees’ intrinsic motivation. Also the moderating role of office hours in the model’s associations was studied. Based on the Job Demands-Resources Model (Bakker & Demerouti, 2007) and the professional isolation literature (e.g., Golden, Vega, & Dino, 2008), we developed and tested a set of hypotheses. Partly in line with expectations, we found a direct curvilinear relationship between telework intensity and individual productivity, characterized by a slight, non-significant positive association at the low telework intensity end, and a significant negative association for the high telework intensity end. Strikingly, we neither found support for a mediating role of intrinsic motivation, nor for a moderation effect of the number of office hours in the relationship between telework intensity and intrinsic motivation. However, the direct relationship between telework intensity and individual productivity appeared to be moderated by the number of office hours. It was concluded that consequences for productivity are contingent on telework intensity, and that the number of office hours has an important impact on the consequences of different telework intensities. The study’s outcomes can inform management and HR practitioners to understand how to implement and appropriately make use of telework.
Hoornweg, N., Peters, P. and van der Heijden, B. (2016), "Finding the Optimal Mix between Telework and Office Hours to Enhance Employee Productivity: A Study into the Relationship between Telework Intensity and Individual Productivity, with Mediation of Intrinsic Motivation and Moderation of Office Hours", New Ways of Working Practices (Advanced Series in Management, Vol. 16), Emerald Group Publishing Limited, pp. 1-28. https://doi.org/10.1108/S1877-636120160000016002Download as .RIS
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In the Netherlands, multiple factors, such as market-competition issues (productivity, effectivity, efficiency, and flexibility), labor-market issues (e.g., work-life balance and gender equality), the need to control overhead costs, commuting and environmental issues, and national and local policy initiatives, have been driving the adoption of flexible work practices (Peters, 2011), such as New Ways of Working, including telecommuting or teleworking. Although these latter two concepts are often used interchangeably to indicate the practice associated with flexible work arrangements where employees can (partially) work away from the central work office at any moment in time (Morganson, Major, Oborn, Verive, & Heelan, 2010), there is an important difference between the two. Telecommuting implies an elimination of daily commuting to the central office (Nilles, 1998), whereas teleworking refers to the practice of employees performing tasks in different locations than the primary workplace (Gajendran & Harrison, 2007, p. 1525), herewith not per se implying the substitution of daily commuting. In some teleworking organizations, for example, telework rather implies working overtime at home (Peters, Bleijenbergh, & Oldenkamp, 2009).
In the present study, we focus upon teleworking, which may relate to working at various locations other than the central office during or outside formal working hours. Examples are working from home in an employee’s personal dwelling (Konradt, Schmook, & Mälecke, 2000); mobile working, meaning that employees can work at any place (Bailey & Kurland, 2002); working from a satellite office or in a neighborhood work center, the latter housing employees from multiple organizations (Di Martino & Wirth, 1990).
In the telework literature, individual productivity is one of the most acclaimed telework gains (Baker, Avery, & Crawford, 2006; Di Martino & Wirth, 1990; Dubrin, 1991; Gajendran & Harrison, 2007; Hartman, Stoner, & Arora, 1991), which is also decisive for informal and formal telework adoption by organizations (Peters & Batenburg, 2015). The relationship between telework and individual productivity, however, is not un-debated. A management decision in 2013 by Yahoo’s CEO Marissa Mayer, for example, to eliminate telework in her organization due to a loss of productivity, attracted world-wide attention (Webwereld, 2013). Mindmetre (2012), a leading consumer and business analyst company, however, reported on a study among 16,000 managers throughout the world, showing that, for example, in 73% of the Dutch teleworking companies in the study, managers stated that telework improved their individual productivity, and that 60% of the Dutch managers indicated that productivity improvement was to be attributed to increased work motivation due to them teleworking.
Both the claims of Marissa Mayer and the Mindmetre study, however, were not explicitly based on academic research. Moreover, no clear insights were given into how the central concepts were defined, and no information was provided on employees’ telework intensity and number of weekly office hours. Therefore, we aim to contribute to the discussion on telework outcomes by investigating the following research questions: What is the relationship between telework intensity and individual productivity; to what extent is this relationship mediated by intrinsic motivation; and to what extent does the number of weekly office hours play a moderating role in the relationships between, on the one hand, telework intensity and individual productivity, and, on the other hand, intrinsic motivation and individual productivity?
Contributions to the Scientific and Societal Debates
By addressing these research questions, we aim to take into account two issues with current telework research, and to contribute to the societal debate on the value of flexible working.
First, most telework literature treats telework as one single, undifferentiated program. According to Gajendran and Harrison (2007, p. 1529), this “overlooks the potentially important structural distinctions among work arrangements.” Although previous studies have provided insight into the curvilinear relationship between the extent of teleworking and employees’ job and life satisfaction (Virick, DaSilva, & Arrington, 2010), it is still largely unclear whether individual productivity increases or decreases with variations in telework intensity. Therefore, telework intensity, defined as “the amount of scheduled time employees spend doing tasks away from a central work location” (Gajendran & Harrison, 2007, p. 1529), needs to be taken into account in scholarly work in this field (Bailey & Kurland, 2002; Peters & Wildenbeest, 2010).
Second, the mechanisms underlying the relationship between flexible work arrangements and work outcomes is under-researched (Feldman & Gainey, 1997; Kelly et al., 2008; Morganson et al., 2010). According to Bailey and Kurland (2002, p. 394), “research is largely unsuccessful in identifying and explaining what happens when people telework” and that “by establishing links (…), scholars might develop better explanations of telework’s impact.” The present study particularly looks into employees’ “individual productivity” which is defined as their self-reported effectiveness, efficiency and productiveness, and the quality of their work (cf. Staples, Hulland, & Higgins, 1999). “Effectiveness” is the degree to which an employee perceives that he or she is able to perform tasks and obligations, and is able to meet deadlines. “Efficiency” deals with the perception of the employee that he or she is able to perform tasks and obligations and to meet deadlines with the least amount of effort. “Productiveness” refers to the actually finished tasks and obligations, and the meeting of deadlines. “Quality,” in conclusion, deals with the degree to which an employee perceives to deliver his or her tasks and duties qualitatively well (ibid.).
Specifically two underlying mechanisms will be looked into. First, “intrinsic motivation,” here defined as the desire to perform an activity with the goal of experiencing pleasure or satisfaction that is inherent to the activity rather than depending on an external stimulus, such as a reward (Bakker & Demerouti, 2007; Warr, Cook, & Wall, 1979), is frequently mentioned as a possible mechanism in the relationship between telework intensity and individual productivity (Caillier, 2012; Morganson et al., 2010; Olson, 1989; Peters & Wildenbeest, 2010).
Second, “the number of office hours” (Caldwell, 1997) can be viewed as a possible important factor in the relationship between telework intensity and individual productivity. However, to the best of our knowledge, the relationship between telework intensity, intrinsic motivation, the number of office hours, and individual productivity was never scientifically studied simultaneously.
In the present study, the Job Demands-Resources Model (JD-R Model) (Bakker & Demerouti, 2007), which is not only suitable to explain psychological outcomes, but also to explain objective performance outcomes' (e.g., Bakker, Demerouti, & Verbeke, 2004, p. 91), studied performance which was conceptualized as workers’ in-role and extra-role performance as perceived by colleagues, is used to enlighten the relationship between telework intensity and individual productivity, and, particularly, to investigate whether intrinsic motivation plays a mediating role in this relationship. In addition, the phenomenon of professional isolation, referring to “a state of mind or belief that one is out of touch with others in the workplace” (Golden, Veiga, & Dino, 2008, p. 1412), can explain how insufficient connectivity and low quality of social interactions with managers (cf. Neufeld & Fang, 2005) and coworkers due to reduced office hours may affect individual productivity, possibly running through the JD-R model’s motivational process.
In order to analyze the relationships between telework intensity and individual productivity, the mediating role of intrinsic motivation, and the moderating role of office hours in the model’s relationships, in the next section, we will first incorporate insights from the professional isolation literature into the JD-R model in order to develop a set of testable hypotheses. Then we will explain the study’s methodology, present and discuss our results, and conclude with some research and policy implications.
The Curvilinear Relationship between Telework Intensity and Individual Productivity
There is considerable resemblance in study findings regarding the positive effect of telework on individual productivity (Shin, Sheng, & Higa, 2000). Gajendran and Harrison (2007), for example, argued that positive effects result, amongst other factors, from time savings. Due to access to telework, employees can reduce commuting time and thus can have more time for work or for other non-work-related activities. Moreover, telework provides workers with the opportunity to modify their work environment to their own needs which enhances efficiency. Bailey and Kurland (2002) referred to a study at IBM in which 87% of the employees reported that they believed that their productivity and effectiveness increased because of teleworking. Hartman et al. (1991) and Dubrin (1991) stated that individuals who practice telework claim that their productivity gained with 15–25%. Although some of these studies were conducted a long time ago, these findings are consistent with the findings of Mindmetre (2012).
We have to bear in mind, however, that workers’ telework intensity is usually low, often not more than a (few) day(s) per week or per month, which may neither affect their working conditions much, nor its impact (Bailey & Kurland, 2002; Peters & Van der Lippe, 2007; Varma, Ho, Stanek, & Mokhtarian, 1998). Nevertheless, for some employees, telework intensity is rather high, and due to enhanced professional isolation and loss of social interactions (Neufeld & Fang, 2005), this may have detrimental effects on work outcomes (Caldwell, 1997; Golden et al., 2008), such as reduced individual productivity.
Based on the theoretical outline given above, we developed the following hypotheses reflecting a direct curvilinear relationship between telework intensity and productivity:
Low telework intensity is positively related to individual productivity.
High telework intensity is negatively related to individual productivity.
Incorporating Insights from the Professional Isolation Literature in the Job Demands-Resources Model to Study the Possible Mediating Role of Intrinsic Motivation and the Moderating Role of Weekly Office Hours
Bakker and Demerouti (2007) developed the so-called Job Demands-Resources model (JD-R model) taking into account strengths and weaknesses of existing models in the occupational health literature which explain employee well-being; basically the demand-control model of Karasek (1979), and the Effort-Reward Imbalance model of Siegrist (1996). The JD-R model can be used to analyze how job characteristics of a particular job can influence psychological outcomes, such as employee well-being (Bakker & Demerouti, 2007). However, much in line with the Job Characteristics model of Hackman and Oldham (1976), who argued that job characteristics would contribute to high levels of intrinsic motivation, which enhances employees’ job satisfaction, and in turn, motivates them to improve employee performance, Bakker et al. (2004) showed the JD-R model to be also valid for explaining how job characteristics can influence performance measures.
Demerouti, Bakker, Nachreiner, and Schaufeli (2001) categorized job characteristics in two main categories: job resources and job demands. Job resources refer to “those physical, psychological, social, or organizational aspects of the job that are either/or, functional in achieving work goals, reduce job demands and the associated physiological and psychological costs, stimulate personal growth, learning, and development” (Bakker & Demerouti, 2007, p. 312). Job demands refer to “those physical, psychological, social, or organizational aspects of the job that require sustained physical and/or psychological (cognitive and emotional) effort or skills and are therefore associated with certain physiological and/or psychological costs” (Bakker & Demerouti, 2007, p. 312).
Bakker and Demerouti (2007) explained the positive and negative effects of job resources and demands by pointing out two different underlying psychological processes. The first psychological process is motivational in nature. Job motivation can either be intrinsic because the job characteristics fulfill basic human needs, or extrinsic because an external stimulus increases the likelihood that employees achieve their goals. According to Bakker and Demerouti (2007), job resources have a motivation potential and can lead to positive organizational and individual consequences, for example, excellent performance. Job demands, on the other hand, can have a de-motivational potential which leads to detrimental outcomes.
The second psychological process is the health impairment process. Bakker and Demerouti (2007, p. 313) stated that “poorly designed jobs or chronic job demands (…) exhaust employees’ mental and physical resources and may therefore lead to the depletion of energy,” and in turn have a negative effect on individual and/or organizational outcomes. Job demands are not necessarily negative, however, when meeting demands costs a lot of energy, they may become job stressors and may lead to job strain. These job demands can be dealt with by the use of job resources (Bakker & Demerouti, 2007).
The Mediating Role of Intrinsic Motivation in the Relationship between Telework Intensity and Individual Productivity
Teleworking is often associated with workers’ intrinsic motivation (Peters & Van der Lippe, 2007; Peters & Wildenbeest, 2010), since it changes the inherent characteristics of a job (Sardeshmukh, Sharma, & Golden, 2012) which may make it more enjoyable and satisfactory (Peters, Poutsma, Van der Heijden, Bakker, & De Bruin, 2014). In scholarly studies, intrinsic motivation pertains that certain behaviors are performed for its inherent satisfaction or for its own sake (Spector, 2008), rather than for some separable consequences (Ryan & Deci, 2000). People, in this case, engage in several activities because they enjoy and derive pleasure from it, without getting a reward.
The JD-R model of Bakker and Demerouti (2007) is particularly useful for elaborating the assumed curvilinear relationship between telework intensity and individual productivity. In many telework studies, telework is regarded a job resource (cf. Peters, Kraan, & Van Echtelt, 2013; Peters et al., 2014; Peters & Van der Lippe, 2007; Peters & Wildenbeest, 2010). According to Bakker and Demerouti (2007), the effects of job demands can be buffered by job resources, such as telework. Assuming that telework can function as a job resource, the JD-R model of Bakker and Demerouti (2007) predicts that telework intensity has a positive relationship with intrinsic motivation (cf. Hackman & Oldham, 1976). Therefore, in line with previously performed telework studies, telework is believed to have the potential to change the motivational qualities of work (Morganson et al., 2010).
Yet, from the professional isolation literature (e.g., Golden et al., 2008), it can be expected that the relationship between telework intensity and intrinsic motivation is not linear (Caillier, 2012). In fact, due to the increased degree of professional isolation and the earlier-mentioned loss of connectivity and social interactions (Neufeld & Fang, 2005) associated with substantial telework (Bailey & Kurland, 2002; Golden et al., 2008), a (too) high telework intensity may rather operate as a job demand than as a job resource (cf. Van den Broeck, De Cuyper, De Witte, & Vansteenkiste, 2010). This may particularly hold for those employees who have a personal need for more structure in their daily work (Slijkhuis, 2012). The more teleworkers are professionally isolated, the greater the detrimental effect of teleworking on work outcomes, such as intrinsic motivation (Caldwell, 1997). This leads to the following hypotheses:
Low telework intensity is positively related to intrinsic motivation.
High telework intensity is negatively related to intrinsic motivation.
According to Hackman and Oldham (1976), intrinsic motivation is positively associated with individual productivity. Therefore, the following hypothesis was developed as well.
Intrinsic motivation is positively related to teleworkers’ individual productivity.
In this study, it is expected that the relationship between telework intensity and individual productivity is mediated by intrinsic motivation. That is, we assume that the positive association between telework intensity and individual productivity, for the low telework intensity end (Hypothesis 1a), and the negative association with productivity, for the high telework intensity end (Hypothesis 1b), will disappear or attenuate in strength, because the relationship is expected to be (partially) mediated through intrinsic motivation. Therefore, we have formulated the following hypothesis:
The relationship between telework intensity and individual productivity is mediated by intrinsic motivation.
The Moderating Role of Weekly Office Hours in the Direct and Indirect Relationships between Telework Intensity and Individual Productivity
The professional isolation literature (e.g., Golden et al., 2008) provides strong reasons to believe that the number of hours spent at the office is an important moderating factor in the relationship between telework intensity and individual productivity. Professional isolation has several consequences: employees do not have social reference points to compare themselves with others (Golden et al., 2008); they are less able to share and receive tacit knowledge in order to perform their jobs more effectively (Nonaka & Takeuchi, 1995); and they believe that they lack relevant information to perform their jobs (ibid.). A combination of telework and traditional work can remedy or eliminate professional isolation, because it provides employees the opportunity to share and receive experiences and knowledge, and to keep in contact with their organization and coworkers (Di Martino & Wirth, 1990), which allows them to perform well, or even better. This leads to the following hypothesis:
The effect of telework intensity on individual productivity is contingent on the number of weekly office hours.
Several authors have argued that the number of weekly office hours can also influence the relationship between telework intensity and intrinsic motivation (Caldwell, 1997; Di Martino & Wirth, 1990; Golden et al., 2008), which we expect to mediate the relationship between telework intensity and individual productivity. Teleworking can have an adverse impact on intrinsic motivation, because it separates employees from their colleagues (ibid.). Indeed, previous empirical studies reported that high telework intensity without frequent office visits can result in demotivation (Caldwell, 1997). In order to avoid demotivation, Di Martino and Wirth (1990) argued that most teleworkers need to combine telework hours with weekly office hours. Also, presence at the office, in addition to telework, can positively contribute to intrinsic motivation (Golden et al., 2008). Therefore, it is expected that when teleworking is combined with longer weekly office hours, teleworkers’ intrinsic motivation is higher compared with teleworkers who combine teleworking with more infrequent office hours. Therefore, the following hypothesis has been developed:
The effect of telework intensity on intrinsic motivation is contingent on the number of weekly office hours.
Sample and Procedure
A digital questionnaire was developed and distributed among employees of a Dutch local banking office that voluntarily participated in the research. Through a contact person, the link to the online questionnaire was distributed to the employees. Two reminders were sent to increase the response rate. 160 employees of the total population of the banking office (377 employees), filled in the online questionnaire (response rate 42.4%). The initial research population included all employees of the local bank as they were all formally given access to telework. Although telework was available to all employees, however, its prevalence largely depended upon the employees’ job category. Two broad job categories could be distinguished: those with abundant telework opportunities, characterized by a high mobility, a high degree of independence, and a possibility to substitute regular office hours for telework hours; and those with limited telework opportunities, characterized by low mobility, lower degree of independence, and for whom telework was only allowed in addition to regular office hours. In fact, although every employee, in principle, had access to telework, not everyone made use of this work arrangement. Moreover, the bank did not specify specific telework days or telework hours, which does not enable us to distinguish telework substituting contractual office hours from telework in addition to office hours. To examine the effect of telework intensity, only “actual teleworkers” were included (N = 111). Hence, teleworking either implied “teleworking outside regular office hours,” or “teleworking away from the office in addition to regular office hours.”
The characteristics of this subsample of teleworkers are shown in Table 1. Note that not everyone having children has a partner at the time of the data collection, and not everyone having a partner cohabitates with his or her partner. As we strived to avoid that the effect of telework intensity and office hours would mirror the effect of job category, rather than telework intensity, job category was controlled for in our statistical analyses.
|Sex||58 (52.3%)||53 (47.7%)|
|Partner||99 (89.2%)||12 (10.8%)|
|Cohabiting||94 (84.7%)||17 (15.3%)|
|Children||75 (67.6%)||36 (32.4%)|
|External service worker||Client supporter||Internal supporter||Organizing worker||External relations worker|
|Function||12 (10.8%)||31 (27.9%)||21 (18.9%)||27 (24.3%)||20 (18.0%)|
Individual productivity was assessed by using four statements extracted from Baker et al. (2006) measuring employee effectiveness, efficiency, productiveness, and quality. For example, the quality item was formulated as follows: “I am satisfied with the quality of my work output.” A five-point Likert scale was used, ranging from: 1 “totally disagree” to 5 “totally agree” (Cronbach’s alpha = .96; M = 3.71; SD = 1.06). As non-normality was observed in the individual productivity variable, a cube transformation (Hair, Black, Babin, & Anderson, 2010) was performed (M = 63.10; SD = 34.15).
Intrinsic motivation was assessed through the six-item instrument by Warr et al. (1979), which was also used by Bakker and Demerouti (2007). For example, “I take pride in doing my job as well as I can” (Warr et al., 1979, p. 145). A five-point Likert scale was used ranging from: 1 “totally disagree” to 5 “totally agree.” After deleting one item that did not fit the scale well, five items remained (Cronbach’s alpha = .64; M = 4.03; SD = .46).
Weekly office hours were assessed by asking respondents how many hours per week they actually spend in the office.
Telework intensity was assessed in a three-step procedure. First, the number of weekly contractual hours was asked. Second, the actual average weekly working hours were asked, including overtime and weekend work, if applicable. Third, the actual weekly working hours spent teleworking were asked. Because of the expected curvilinear relationship of telework intensity with individual productivity and intrinsic motivation, respectively, a polynomial term was created for the telework intensity variable by squaring the centered variable (M = .00, SD = 4.20) (polynomial term for telework intensity (M = 17.47, SD = 35.01)).
A number of control variables were included in the analyses. Autonomy and feedback were measured based upon the job diagnostic survey of Hackman and Oldham (1976). For both measures, three items were used (based on one question on the particular job characteristic and two statements). For example, job autonomy was measured with the question: “How much autonomy is there in your job?,” one of the associated statements being “The job gives me considerable opportunity for independence and freedom in how I do the work.” Feedback was measured with the question: “To what extent does doing the job itself provide you with information about your work performance?,” one of the associated statements being: “Just doing the work required by the job provides many chances for me to figure out how well I am doing.” To measure both job characteristics, for each item, a five-point Likert scale was used: 1 “very little or very inaccurate” and 5 “very much or very accurate” (Autonomy: Cronbach’s alpha = .69; M = 3.73; SD = .81; Feedback: Cronbach’s alpha = .74; M = 3.35; SD =.79).
Other measures controlled for were: age (for age two additional polynomial terms were created due to non-linearity (squared: M = 74.63; SD = 91.49); (cubed: M = 59.37; SD = 1866.80)); overtime (calculated by subtracting contractual hours from actual worked hours (M = 4.06; SD = 3.13)); the number of children; sex of the respondent (dummy variable men = 1 (M = .52; SD = .50), women being the reference category); children living at home (having children living at home (= 1) (M = .65; SD = .48), respondents not having children living at home or not having children being the reference category); and finally, age of the youngest child (two dummy variables: having children aged 6–18 (M = .36; SD = .48); having children over 18 or no children (M = .37; SD = .49), respondents with children up to and including five years old being the reference category).
In preliminary analyses, also other control variables were used. However, non-significant effects were deleted in a stepwise fashion in order to enhance explained variance (Hair et al., 2010). Consequently, “commuting time,” “actual weekly work hours,” “job category,” and “telework location” were not included in the reported analyses.
First, a correlation analysis was conducted. Second, in order to test Hypotheses 1 to 4, multiple regression analyses (N = 111) were conducted (hypotheses tested one-tailed), following the procedure suggested by Baron and Kenny (1986), intrinsic motivation and (the cube transformation of) individual productivity, respectively, being the outcome variables.
Third, in order to test Hypotheses 5a and 5b, two ANOVA-analyses were conducted to establish whether the number of office hours “moderated” the relationship between telework intensity and individual productivity and intrinsic motivation, respectively. Due to the curvilinear relationship between the independent variable “telework intensity” and the dependent variables “individual productivity” and “intrinsic motivation,” respectively, it was impossible to calculate the associated interaction terms between telework intensity and weekly office hours and, consequently, regression analysis was not suitable (cf. Hair et al., 2010). Therefore, two groups were created for telework intensity and two groups for office hours, combining into four telework intensity/weekly office hours categories. Based on previous studies (cf. Peters & Wildenbeest, 2010), telework intensity was considered low when the number of weekly teleworking hours were 8 hours or less (N = 92), and high when the number of weekly telework hours were over 8 hours (N = 19). Office hours categories were based on the mean weekly number of office hours in the sample, which was 32 hours. Two categories were distinguished: a group with a low number of office hours (32 weekly office hours or less; N = 59) versus a group with a high number of office hours (over 32 weekly office hours; N = 52). Four groups were created for the analyses: Group 1 comprising respondents characterized by both a low telework intensity and a low number of weekly office hours; Group 2 being characterized by a low telework intensity and a high number of weekly office hours; Group 3 being characterized by a high telework intensity and a low number of office hours; and Group 4 being characterized by both a high telework intensity and a high number of weekly office hours.
Table 2 presents the bivariate correlations between all key model variables and shows that, at the univariate level, telework intensity does not correlate significantly with individual productivity (r = −.095; p > .05), however, the polynomial term for telework intensity (telework intensity squared) significantly and negatively correlates with individual productivity (r = −.185; p < .05), but no significant correlations were found between telework intensity and intrinsic motivation (r = −.095; p > .05) and the polynomial term for telework intensity and intrinsic motivation (r = .020; p > .05), respectively.
|1. Individual productivity||1|
|2. Intrinsic motivation||.125||1|
|3. Telework intensity||−.095||−.095||1|
|4. Telework intensity²||−.185*||.020||.722**||1|
|9. Number of children||−.067||.104||.013||−.071||−.180||.402**||−.194*||.314**||1|
|12. Children at home||−.074||.089||−.023||−.066||−.358**||.260**||−.351**||.260**||−.817**||−.053||.137||1|
|13. Youngest child ≤5||−.076||−.022||.011||−.025||−.221*||−.320**||−.200*||−.320**||.298**||.038||.109||.448**||1|
|14. Youngest child 6–18||.036||.101||−.051||−.018||−.163||.497**||−.214*||.497**||.536**||−.066||.071||.552**||−.457**||1|
|15. Youngest child >18, no children||.034||−.080||.041||.041||.366**||−.200*||.397**||−.200*||−.807**||.030||−.171||−.962**||−.466**||−.574**||1|
*p < .05; **p < .01.
Table 3 shows the mean scores of the two distinguished telework intensity worker categories (i.e., 8 telework hours or less versus more than 8 telework hours per week) on some model variables. An across-category comparison shows that those on the high telework end have significant higher contractual hours (mean difference = 3.75; p < .05); higher actual working hours (mean difference = 7.99; p < .05); higher telework hours (mean difference = 8.81; p < .05); and higher overtime hours (mean difference = 4.24; p < .05). No differences between the group means were found regarding individual productivity (mean difference = .34; p > .05) and intrinsic motivation (mean difference = .24; p > .05).
|Variable||Mean Score on Variable|
|Categories:||Individual productivity||Intrinsic motivation||Contractual hours||Actual hours||Telework hours||Office hours||Overtime|
|8 hours or less||3.77||4.03||31.62||34.96||3.72||31.22||3.34|
|N = 92|
|More than 8 hours||3.43||4.01||35.37||42.95||12.53||30.42||7.58|
|N = 19|
N = 111.
**p < .001.
Table 4 presents the results of the final regression analysis predicting individual productivity (Model 3). Telework intensity is shown not to have a significant direct relationship with individual productivity (β = .08; p > .05). Hence, no support was found for Hypothesis 1a. The effect of the polynomial term of telework intensity (telework intensity squared), however, appeared to be significant and negative (β = −.28, p < .05) supporting Hypothesis 1b stating that a high telework intensity is negatively related to individual productivity.
|Dependent Variable||Dependent Variable|
|Individual productivity||Individual productivity||Individual productivity||Change statistics||Intrinsic motivation||Intrinsic motivation||Change statistics|
|Model 1||Model 2||Model 3||Model 1||Model 2|
|Independent variables||Beta||Beta||Beta||ΔR² in %||Beta||Beta||ΔR² in %|
|Step 1: Control variables||ΔR² 16.0||ΔR² 8.1|
|Men (reference group: women)||−.174||−.148||−.158||.075||.053|
|Children living at home (reference group: no children living at home)||−.251||−.301||−.346||.199||.247|
|Age of the youngest child 6–18 (reference group: age of the youngest child 0–5)||.311*||.322*||.290*||.197||.173|
|Age of the youngest child > 18 (reference group: age of the youngest child 0–5)||−.180||−.240||−.337||.502||.536|
|Number of children||−.118||−.153||−.212||.302||.327|
|Age2 (first polynomial term)||.006||−.015||.011||−.160||−.148|
|Age3 (second polynomial term)||.737*||.736*||.737*||−.027||−.010|
|Step 2: independent variables||ΔR² 4.3||ΔR² 2.1|
|Telework intensity2 (polynomial term)||−.244*||−.276*||.178|
|Step 3: mediator||ΔR² 2.9|
|R² in %||16.0||20.4||23.3||8.1||10.2|
N = 111.
*p < .05.
Figure 1 graphically represents the curvilinear relationship between telework intensity and individual productivity. The figure shows that low telework intensities can be associated with slightly, though insignificant, higher levels of individual productivity, and that higher telework levels (teleworking 8 or more hours) report lower productivity levels.
Moreover, intrinsic motivation was a significant predictor in the individual productivity equation (β = .18, p < .05), herewith confirming Hypothesis 3.
Table 4 shows the results of the regression analysis predicting intrinsic motivation (Model 2). In contrast to Hypothesis 2a and 2b, neither the telework intensity variable, nor the polynomial telework intensity term had a significant effect on intrinsic motivation. These findings also imply that Hypothesis 4, assuming a mediating effect for intrinsic motivation, was not corroborated. In fact, after including intrinsic motivation in the individual productivity model, the effect of the two telework intensity variables on individual productivity did not attenuate and the polynomial term of telework intensity remained significant.
The first ANOVA analysis for individual productivity (Table 5) shows the differences across the four groups representing various telework intensity/number of weekly office hours to be significant (F, (1, 94) = 5.60, p < .05). The effect size of the “interaction effect,” based on eta squared, was mediocre (.06) (Hair et al., 2010). This implies that Hypothesis 5a, stating that the effect of telework intensity on individual productivity is contingent on the number of weekly office hours, was confirmed by our data. Importantly, as we controlled the initial analyses with job category and weekly working hours, across group differences were not caused by employees working part-time versus full-time.
|Children living at home||.950||2||.475||.475||.624|
|Age of the youngest child||5.729||2||2.865||2.861||.062|
|Number of children (covariate)||2.885||1||2.885||2.882||.093|
|Age2 first polynomial term (covariate)||.598||1||.598||.597||.441|
|Age3 second polynomial term (covariate)||8.962||1||8.962||8.951||.004*|
|Telework intensity categorical||.375||1||.375||.375||.542|
|Office hours categorical||6.770||1||6.770||6.762||.011*|
|Telework intensity categorical × Office hours categorical||5.577||1||5.577||5.570||.020*|
N = 110.
*p < .05.
Closer mean comparisons (Table 6) reveal significant differences in the reported individual productivity levels between the four groups. The difference between Group 3 (high telework intensity/low number of weekly office hours) (M = 3.50, SD = .43) versus Group 4 (high telework intensity/high number of weekly office hours) (M = 5.10, SD = .70) was most striking, with those characterized by high numbers of weekly office hours being more productive, keeping constant for high telework intensity. The differences in productivity mean scores between other groups were smaller, for example the differences between Group 1 (low telework intensity/low number of weekly office hours) (M = 3.91, SD = .38) versus Group 2 (low telework intensity/high number of weekly office hours) (M = 4.20, SD = .35). Also in this comparison, high numbers of weekly office hours can be associated with higher levels of reported individual productivity. A significant difference was also revealed between Group 4 (high telework intensity/high number of weekly office hours) (M = 5.10, SD = .70) and Group 2 (low telework intensity/high number of weekly office hours) (M = 4.20, SD = .35), this comparison indicating a positive association between high telework intensity and individual productivity (under the condition of a high number of office hours). In the comparison between Group 4 (high telework intensity/high number of weekly office hours) (M = 5.10, SD = .70) and Group 1 (low telework intensity/low number of weekly office hours) (M = 3.91, SD = .38), a high telework intensity was shown to be accompanied by a higher productivity level, when also weekly office hours were relatively high.
|Telework Hours||Office Hours||Mean||Standard Error|
|8 hours or less||32 or less (group 1)||3.911||.384|
|More than 32 hours (group 2)||4.166||.352|
|More than 8 hours||32 or less (group 3)||3.454||.434|
|More than 32 hours (group 4)||5.094||.654|
|Compared Groups||Mean Differences|
|Group 1–group 2||.255|
|Group 1–group 3||.457|
|Group 1–group 4||1.183*|
|Group 2–group 3||.712|
|Group 2–group 4||.928*|
|Group 3–group 4||1.640*|
N = 110.
*p < .05.
Figure 2 shows a graphical representation of the “interaction” between telework intensity and office hours and individual productivity.
In conclusion, in a second ANOVA analysis (Table 7), it was tested whether office hours moderated the relationship between telework intensity and intrinsic motivation. The results showed that the “interaction” between telework intensity and the number of office hours was not significant (F, (1.95) = 1.90, p > .05). The effect size of the interaction effect, based on the eta squared, was very small (.02).
|Children living at home||.072||2||.036||.163||.850|
|Age of the youngest child||.544||2||.272||1.236||.295|
|Number of children (covariate)||.520||1||.520||2.361||.128|
|Age first polynomial term (covariate)||.262||1||.262||1.188||.278|
|Age second polynomial term (covariate)||.011||1||.011||.050||.823|
|Telework intensity categorical||.061||1||.061||.277||.600|
|Office hours categorical||.131||1||.131||.593||.443|
|Telework intensity categorical × Office hours categorical||.418||1||.418||1.899||.171|
N = 110.
Also in-depth mean comparisons (Table 8) shows no significant differences in intrinsic motivation between the four distinguished groups reflecting different combinations of weekly telework and office hours intensities. This implies that Hypothesis 5b, stating that the effect of telework intensity on intrinsic motivation is contingent on the number of weekly office hours, was not supported by the data.
|Telework Hours||Office Hours||Mean||Standard Error|
|8 hours or less||32 or less (group 1)||3.916||.180|
|More than 32 hours (group 2)||3.973||.165|
|More than 8 hours||32 or less (group 3)||4.009||.204|
|More than 32 hours (group 4)||3.690||.305|
|Compared Groups||Mean Differences|
|Group 1–group 2||.057|
|Group 1–group 3||.093|
|Group 1–group 4||.226|
|Group 2–group 3||.036|
|Group 2–group 4||.283|
|Group 3–group 4||.319|
N = 110.
Conclusions and Discussion
The Curvilinear Relationship between Telework Intensity and Individual Productivity
First, in contrast to previous evidence in the telework literature (Bailey & Kurland, 2002; Gajendran & Harrison, 2007; Hartman et al., 1991; Shin et al., 2000) in case of low telework intensity (i.e., 8 hours or less per week), the relationship between telework intensity and individual productivity was non-significant, although a positive trend was shown. In line with our hypothesis, however, in case of a relatively high telework intensity (i.e., more than 8 hours per week), the relationship with individual productivity was significant and negative. Hence, to some extent, evidence was found for the course of the relationship between telework intensity and individual productivity being curvilinear, characterized by a slight (but non-significant) ascend trend and a significant descend effect. These combined findings contribute to the scientific and societal debates concerning the importance of telework intensity and its outcomes (Bailey & Kurland, 2002; Gajendran & Harrison, 2007; Peters & Wildenbeest, 2010), stressing that telework intensity needs to be taken into account in research in the field, instead of only comparing teleworkers with non-teleworkers.
More specifically, the finding that relatively low telework intensity had no effect on individual productivity, while relatively high telework intensity had a negative effect on productivity nuanced the mainstream telework literature implicitly or explicitly building on the JD-R Model in which telework is commonly regarded as a job resource which can be associated with positive work outcomes (Di Martino & Wirth, 1990; Dubrin, 1991; Gajendran & Harrison, 2007; Hartman et al., 1991), especially when it is perceived as such by employees (Peters et al., 2014), or when access to telework is part of a synergetic HRM-bundle (Peters et al., 2013). In light of this study’s findings, we may conclude that low telework intensity does not automatically function as a job resource as defined by Bakker and Demerouti (2007), that is, not when it comes to individual productivity, and that a high telework intensity rather functions as a demand.
The Mediating Role of Intrinsic Motivation
This conclusion was further strengthened by the lack of evidence in this study for the mediating role of intrinsic motivation in the relationship between telework intensity and individual productivity characterizing the function of job resources (Hackman & Oldham, 1976). Although we found a direct positive relationship between intrinsic motivation and individual productivity (indicating that the higher an employee is intrinsically motivated, the higher his or her productivity), our analyses did not support the assumption that telework intensity can be associated with higher levels of intrinsic motivation, let alone that intrinsic motivation mediates the relationship between telework intensity and individual productivity. These findings contribute to the debate concerning the mechanisms underlying the relationship between telework intensity and individual productivity (Bailey & Kurland, 2002; Feldman & Gainey, 1997; Kelly et al., 2008; Morganson et al., 2010). Based on our findings, we may rather conclude that in case of low intensity, telework can be viewed a “neutral HRM-tool” when it comes to individual productivity, as it does not significantly affect how individuals perceive their individual productivity. Moreover under higher telework intensities, telework can rather be viewed a challenge (cf. Van den Broeck et al., 2010), since this has the potential to affect individual productivity, which can be related to the next discussion point.
The Moderating Role of Weekly Office Hours
Our findings also contribute to the professional isolation literature by confirming that working away from the office more frequently, in this study represented by a relatively high telework intensity, could function as a job demand (Bakker & Demerouti, 2007), or job challenge (Van den Broeck et al., 2010), possibly resulting in professional isolation, herewith negatively affecting work outcomes (Bailey & Kurland, 2002; Caldwell, 1997; Di Martino & Wirth, 1990; Golden et al., 2008), such as individual productivity. According to the JD-R model, a job demand does not necessarily influence intrinsic motivation. Rather, a job demand can turn into a stressor and can cause strain, which might explain why a high telework intensity resulted in lower individual productivity, also when controlled for intrinsic motivation.
Importantly, our findings also showed that the consequences of telework intensity are contingent on how telework behavior is accompanied by weekly office hours, possibly indicating higher quality of social interactions at work (cf. Neufeld & Fang, 2005). When simultaneously taking into account both telework intensity and the number of weekly office hours, our analyses indicated that the group of teleworkers with a relatively high telework intensity (i.e., more than 8 hours per week) combined with a relatively large number of office hours (i.e., more than 32 hours per week) reported the highest level of individual productivity. This finding shows that high telework intensity can be viewed as an HRM-tool which has the potential to foster individual productivity, as long as it is combined with a relatively large number of office hours. This finding further refines the previous findings on the relationship between telework intensity and individual productivity, where the number of office hours was not taken into account. However, like in previous studies (Peters, Den Dulk, & Van der Lippe, 2009), it seems that telework in these situations runs parallel with “working overtime” at home. This may also confirm the outcomes in the literature that teleworking fosters work intensification and longer working hours, either spent teleworking or working at the office (Kelliher & Anderson, 2010). When interpreting the outcomes, however, we have to keep in mind that our study measured self-reported individual productivity using a construct comprising four elements (i.e., effectiveness, efficiency, productivity, and quality). This measure, however, does not indicate whether the individuals’ total productivity (productiveness), or the individuals’ hourly productivity (efficiency) was higher, or both.
To some extent, however, our study supports the professional isolation literature which argues that employees’ presence at the central office, besides teleworking, reduces or eliminates the effect of professional isolation regarding individual productivity which is often associated with full-time telework (Di Martino & Wirth, 1990; Golden et al., 2008), and could even have the consequence that employees perform better. However, our findings are not fully in line with what we expected based on the professional isolation literature (Bailey & Kurland, 2002; Caldwell, 1997; Di Martino & Wirth, 1990), in which it has been argued that a high telework intensity combined with frequent office visits would positively influence employees’ intrinsic motivation. Also due to the lack of significant differences in intrinsic motivation of the four telework categories distinguished in this study (each varying in their combination of telework intensity and numbers of office hours), the relationship between telework intensity, office hours, and intrinsic motivation could not be supported by the data. This demands further research.
In conclusion, our analyses indicated that for substantial teleworkers in our banking organization, commuting between home and work may not always be substantially eliminated, given the combination of high telework intensity and frequent office hours. In any case, frequent office visits were shown an important factor in achieving individual productivity. With this outcome, it is also stressed that the terms “teleworking” and “telecommuting” should not be used interchangeable, but that its use should be dependent upon the effect telework has on employees’ commuting behaviors.
Limitations and Research
First, our study was characterized by a relatively small sample size (N = 111) derived from one research site. Given the study’s limited statistical power, the conclusions should be adopted with some caution. Although the sample size was appropriate (it covered nearly 30% of the total population of the case organization), further research including and comparing more organizations is needed to get more insight into the results’ generalizability.
A second limitation stems from the operationalization of the concepts in this study. The respondents were asked to report their perceptions regarding the model variables. Although perceptions can be viewed a reliable and valid source of information which are commonly used (cf. Baker et al., 2006), we should be cautious as judgments of teleworkers about their own productivity may be biased. Moreover, the productivity measure used does not indicate whether it relates to individuals’ total productivity or to hourly productivity. However, Baruch (1996) argued that it is hard to obtain objective data and to relate objective data to telework, since other factors might play a role as well. Future research might need to take both more objective or subjective productivity and intrinsic motivation measures into account, for example, by using estimations based on coworkers’, managers’, and clients’ perceptions (Bakker et al., 2004) or concrete output measures.
A third limitation stems from our study’s focus, intentionally excluding non-teleworkers. Future research could study differences in total and hourly productivity of teleworkers and non-teleworkers.
A fourth limitation is that our study only focused on a bank environment. Future research could check whether our findings also apply to other occupational settings. Especially given the fact that this study found no support for the central role of intrinsic motivation as an underlying mechanism, despite the fact that the telework literature repeatedly mentions intrinsic motivation as an important factor in the relationship between telework intensity and individual productivity, future empirical work in this field is needed. In fact, rather than focusing on the “motivational process” in the JD-R model, future telework studies could also focus on the “health impairment process,” as telework can be accompanied by a loss of resources (collegial and managerial support) (Peters et al., 2014), or by an increase of stressors (Bakker & Demerouti, 2007).
Sixth, this study did not take the use of Information and Communication Technologies (ICT) into account. As this study shows that telework at a high intensity can be beneficial for productivity, more empirical work is needed to overcome the paradox that frequent physical presence is in contradiction with the purpose of telework and to shed more light on how ICT can help to improve and maintain connectivity and high quality work relationships in distributed working (cf. Ten Brummelhuis, Bakker, Hetland, & Keulemans, 2012).
Seventh, although New Ways of Working, including teleworking, may be used to improve productivity, it is also believed to enable employees to better balance work and non-work activities (Peters, 2011). However, our study presented some indications that the anticipated productivity gains were only realized when teleworking was accompanied by longer working hours. It is not clear, however, whether this was intended by employees or not and how this affected their work-life balance. As teleworking practices may be accompanied by an organizational culture pressuring employees to be constantly available for work (cf. Derks, Van Duin, Tims, & Bakker, 2014), future research could use boundary theory (Ashforth, Kreiner, & Fugate, 2000) to give more insight into how this may possibly enhance work-life conflict, may be impacting productivity in the longer run (cf. Rothbard, Philips, & Dumus, 2005).
This study shows that low telework intensity does not significantly affect individual productivity, neither positively, nor negatively. Since low teleworking practices were not shown to affect productivity negatively, it can be used to achieve other HRM goals, for example, work-life balance. The negative relationship between high telework intensity and individual productivity may also explain why direct supervisors prefer to allow employees to telework only for a limited amount of time (Peters & Wildenbeest, 2010).
However, this study also shows that a high telework intensity can be fruitful in terms of productivity when it is accompanied with frequent office hours. As this may come down to working longer hours, the optimal mix or constellation may depend on the number of office hours or alternative ways to maintain the quality of social interactions (cf. Neufeld & Fang, 2005). A policy recommendation for HRM practitioners and managers might be that it is more beneficial for an organization to allow employees to telework substantially, but under clear conditions. On the one hand, in case of a high telework intensity, organizations should be alert to the damaging effect of professional isolation, by instructing employees to be physically present regularly as well. On the other hand, however, organizations should be aware of the risks of organizational cultures pressuring employees to be constantly available for work (cf. Derks et al., 2014), meaning that boundaries between work and non-work become too blurred (Ashforth et al., 2000), leading to longer working hours which come at the expense of well-being and long-term productivity (cf. Rothbard et al., 2005).
The importance of finding the best mix between telework intensity and the number of (weekly) office hours shows that neither telework, nor abolishing telework (like in the case of Yahoo!) should be considered best practices when it comes to enhancing individual productivity. That is to say, telework should be carefully managed, monitored, facilitated, and adapted. Telework should be viewed an HRM-tool which is dependent on the context in which it is implemented, and HR practitioners and organizations should create a proper policy to support this aspect of “New Ways of Working.”
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