Transportation (2006) 33: 329–346 DOI 10.1007/s11116-005-2308-3
Ó Springer 2006
Telecommuting suitability modeling: An approach based on the concept of abstract job AMIR REZA MAMDOOHI*, MOHAMMAD KERMANSHAH & HOSSAIN POORZAHEDY Civil Engineering Department, Center for Transportation Studies and Research, Sharif University of Technology, Azadi Str., Tehran, 1458889694, Iran (*Author for correspondence, E-mail:
[email protected])
Key words: abstract job, telecommuting suitability, job-task vector, discrete choice models, stated preference Abstract. A new approach to modeling telecommuting suitability is proposed in this paper. The approach, based on the concept of abstract job, can be employed to assess the level of suitability for telecommuting of the bundle of tasks comprising a job. By abstract job is meant a way of considering jobs on the basis of their elements and tasks, representing the general structure of the job. In this study, the basic tasks a job is composed of, pertaining to telecommuting suitability, are identified. To show the applicability of the approach, discrete choice models are calibrated, based on a sample of 245 employees in Tehran, Iran, indicating that from among the 6 tasks identified, 5 tasks are significantly associated with the level of telecommuting suitability.
1. Introduction While the automobile has made possible the achievement of long sought goals and activities which once seemed impossible due to the deterrence of distance, it has also caused some ever increasing and serious socio-economic, cultural, and environmental problems. Viewed as a transportation demand management (TDM) strategy, telecommuting has the potential to reduce the foregoing problems. However, as defined by working at home or in the neighborhood instead of commuting to the conventional work place, telecommuting or teleworking needs more research and planning. Telecommuting has developed and evolved in various forms, and different kinds of telecommuting like home-based, center-based, and hoteling have been the focus of many studies in recent years. The present research (due to various reasons) focuses only on center-based telecommuting. Employing this strategy does not result in reducing the number of work trips but rather changes the destination from a usually distant office, e.g. in the central business district (CBD), to a telecenter in the neighborhood, resulting in a
330 decrease in vehicle-miles traveled (VMT) at the peak period, usually in the congested corridors (Mahmassani et al. 1993; Sullivan et al. 1993). Assuming a densely populated city which is suffering from heavy traffic and its negative consequences like air pollution, fuel consumption, long travel times and stress, telecommuting may be considered a very promising alternative with many positive traffic and environmental impacts (Bernardino et al. 1993; Mahmassani et al. 1993; Sullivan et al. 1993; Yen et al. 1994; Bernardino & Ben-Akiva 1996; Yen & Mahmassani 1997). However, the extent of such impacts is directly proportional to the level of adoption of telecommuting, which in turn depends on its suitability for the population of employees. Most conceptual frameworks proposed in the analysis of the telecommuting adoption process (Bernardino et al. 1993; Sullivan et al. 1993; Yen et al. 1994; Bernardino & Ben-Akiva 1996; Yen & Mahmassani 1997) incorporate employer and employee perspectives: the employer decision to offer a telecommuting program (as a function of costs and benefits for the organization) and the employee decision to adopt telecommuting (as a function of costs and benefits for the individual). It is generally believed that a telecommuting program must be offered by the employer as a prerequisite, making the program available and giving the employees the right to choose whether to telecommute or not, or to choose the level of telecommuting adoption. Bernardino et al. (1993) suggested a framework for home-based telecommuting adoption in which they incorporated the perspectives of the organization and the employees. Their proposed framework consists of three main components: environment, individual, and decision process. Later, Bernardino and Ben-Akiva (1996) proposed a comprehensive framework for the telecommuting adoption process consisting of three models: (a) employer design of the telecommuting program, (b) employer decision to offer the designed program to the employees, and (c) employee decision to adopt telecommuting. Mahmassani et al. (1993) and Yen et al. (1994) analyzed, respectively, employee and employer attitudes and stated preferences toward telecommuting. Data used in both studies are from a survey conducted in three Texas cities: Austin, Dallas, and Houston. They further compared the stated preferences of executives and those of employees (Yen et al. 1994). Both papers emphasize the fact that the adoption of telecommuting involves two principal categories of decision makers: the employee and the employer. Yen and Mahmassani (1997) proposed a conceptual framework for telecommuting adoption. They developed a mathematical model of the employee’s telecommuting adoption process on the basis of a dynamic generalized probit model. Mokhtarian and Salomon (1994) presented a conceptual model of the individual decision to telecommute, in which key elements of that decision,
331 including constraints, facilitators, and drives were defined. Beginning to operationalize their conceptual model, Mokhtarian and Salomon (1996a) presented empirical data from a non-representative sample of 628 employees of the City of San Diego, California on key variables and relationships in their model. Noting that there was a wide gap between preferring to telecommute and actually telecommuting, Mokhtarian and Salomon (1996b) further developed binary logit models based on their conceptual model for telecommuting adoption. What distinguishes the current study from similar ones, is the approach devised and developed based on the concept of abstract job, which views jobs as composed of tasks, whose time shares can be significant to telecommuting suitability as necessary conditions and prerequisites of its adoption. The distance from suitability to adoption can be short or long, depending on various factors. In the conceptual framework presented in Figure 1, telecommuting adoption is the result of the interaction of three components: (a) job suitability, (b) employers’ attitudes, and (c) employees’ attitudes. Components (b) and (c) can have different roles in this framework depending on the context. For example, for an authoritarian employer, component (c) may be negligible, while in a tight labor market, component (b) may be negligible and employees may ‘‘call the shots.’’ However, component (a) is considered a
Job suitability: What job permits/requires
Employer: What company permits/requires
Employee: What employee desires/requires
Telecommuting Adoption Figure 1. Conceptual framework for telecommuting adoption.
332 necessary component in this framework and helpful in assessing the demand or market for telecommuting, and planning effective telecommuting programs. An attempt is made to identify and group the basic tasks a job is composed of, pertaining to telecommuting suitability and to show the applicability of the approach by focusing on the identified job-tasks. Such a model could be used to: (i) assess the suitability of a sample or population of jobs, whether at the company level or regional level, (ii) forecast changes in suitability as a function of changes in job-task composition (e.g. through changes in distribution of types of job, and/or changes in the content of a given job), and (iii) serve as a crucial stage in a multi-stage model of adoption.
2. The concept of abstract job Quandt and Baumol (1966) proposed the concept of an abstract mode in order to be able to produce an analysis pertinent to the future in a world of changing technology. They, thus, found it useful to define a mode in terms of the type of service it provides to the traveler rather than the administrative entity that controls its operations or the sort of physical equipment it employs. At about the same time, the concept of abstract commodity was proposed by Lancaster (1966). It is believed that the title of a job may not be very revealing, but rather the elements (and their extent) that a job comprises are considered to be effective in determining the level of suitable telecommuting. In a world of increasingly developing technology, where jobs for individuals and roles at work are changing so fast, it seems essential to think of the structure of jobs (i.e. the tasks), rather than the conventional job title. In modeling terms, rather than using a particular job category in the models as a single dummy variable which is a simple (and proper) approach when no other data are present, the tasks a job comprises, or more specifically the extents of time spent on each task are used as a series of continuous variables capturing the effects of the structure of the job in more detail. The concept of abstract job is proposed as the focus of the approach devised for modeling telecommuting suitability. The main objective of proposing this concept is to prepare the ground for a comprehensive analysis of telecommuting adoption. By abstract job, we mean a way of considering jobs on the basis of their elements and constituents, whose share distribution in the overall time allocation can be relevant to the level of telecommuting suitability. In this regard, important and effective aspects of jobs are identified and irrelevant aspects ignored and bypassed. These meaningful and determinant aspects are believed to be able to represent all different jobs regarding
333 the objective in mind. The abstract job approach can cover all possible jobs, and in this sense can be thought of as the universal set, from which only a limited number may be encountered in the society. This approach is evidently different from the usual and conventional approach of grouping and categorizing jobs based on some kind of commonality. Hence, the important constituents of jobs, which we call job-tasks, are identified and used as the factors affecting the level of telecommuting for which the job is suitable. Having identified job-tasks, each and every job can be analyzed duly as the mean amount of daily time allocated to each task. Thus, for every job j, a vector denoted by Xj representing these time periods (or their corresponding codes) is assigned which we call the job-task vector (JTV). Elements of the JTV are assumed to be continuums represented by continuous variables. Suppose that xij is the i-th element of JTV representing job j, where i=1,2,...,N, and N represents the total number of tasks identified as important and meaningful for the particular objective in mind. Thus, for each job j, the vector Xj=[x1j,x2j,...,xNj] denotes the mean daily time spent on, or assigned to each job-task i. It should be noted that the same job title may be given different JTVs by the jobholders in different organizations. For instance, a secretary in a computer-based company is apt to spend more time working with a PC than a secretary in an old-fashioned one. They are both considered and called secretaries while there is much difference between the two. This is exactly why the concept of abstract job is very useful for a comprehensive and precise modeling of telecommuting suitability. The difference between the JTVs for the same job category assigned by different jobholders (or their supervisors) can be ascribed to a number of reasons like task variation of the respondents due to different job details and conditions for the same job in different organizations or companies. One problem is that there are not precise descriptions for job categories, another one is that the total number of job categories is very limited, compared to the actual jobs and their variants. Regarding jobs as abstract puts an end to these problems, by having all jobs be characterized by their job-task vector. Since the elements of a JTV are continuous variables, the number of jobs that may be represented is infinite. However, a particular vector does not necessarily correspond to a unique job and may refer to many jobs, some of which can be very different considering conventional job titles.
3. Job-task identification The concept of abstract job relies heavily on identification of job-tasks. Hence, an important part of this study is the determination of job-tasks,
334 which depends largely on the objective in mind. In this study (suitability of jobs for telecommuting), tasks are identified based upon such characteristics as: (a) independence from a particular location (b) independence from colleagues or supervisors, and (c) dependence on modern communication technology. With other objectives in mind, the corresponding characteristics based on which the tasks are identified, and consequently the resulting tasks, will change accordingly. For example, if the objective is the determination of suitable income, the professionality and experience in the field and related qualifications will be the characteristics needed for consideration. After a rather comprehensive phase of job analysis pertaining to telecommuting suitability, based on responses from the employees and their supervisors in our sample (discussed further in the next section), 6 job-tasks were identified (Table 1). Obviously, tasks with both positive and negative impacts were sought and finally identified after a series of pilot surveys. The number of elicited tasks depends on data availability and level of precision required. In the first version of the questionnaire, 13 specific tasks were listed and 5 more left blank, inquiring after other tasks which were considered important by respondents but not listed in the questionnaire. Results of the survey showed that no other distinct task had been added to the list. Moreover, responses to these 13 detailed tasks could not be obtained with due reliability. After aggregation1 of similar tasks and deletion of unimportant ones, 6 general and easily distinguishable tasks remained. Response categories for each task-item included seven choices numbered consecutively from 1 to 7 and indicating the mean daily time period assigned to the basic tasks of the respondent’s job, namely 0--15 min, 15--30 min, 30 min--1 h, 1--2 h, 2--3 h, 3--4 h, and more than 4 h. Supervisors responded to this question in a separate column provided exclusively for this purpose. The distinction between employers and supervisors, as used in this study, may be stated as follows. Supervisors (or managers) refers to people in charge of some other employees, who are well-informed about the employees’ job duties and responsibilities. Their ideas (about suitability of telecommuting for their worker–employees) are of importance because of their knowledge about employees’ job duties, not as employers. Table 1. List of job-task symbols and their description. Symbol
Description
read/write PC phone talking teamwork mission
Reading or writing reports, correspondence, etc. Working with a PC Talking on the telephone Talking and conversing with clients and colleagues Teamwork and participating in meetings Mission out of the office
335 4. Overview of sample characteristics The model calibration sample used in this study includes 245 employees of seven companies and their departments, based in Tehran, Iran. Since telecommuting is in its very early stages in Iran, stated (as opposed to revealed) preference data were gathered through the survey (conducted in 2003). Respondents’ occupations encompassed a variety of job categories on the organizational chart. The average respondent is 34.3 years old with 5.5 years experience in the current organization and 10.5 years in total. About 71.4% of respondents are men, 65.7% are married, 83.7% have a driver’s license, and 20% own a private car. About 40% of the respondents commute to work by public transportation other than company-provided bus and their average travel times to and from work are, respectively, 49 and 56 min. Those commuting by company-provided bus comprise about 30%, whose average travel times to and from work are, respectively, 49 and 47 min. Commute trips by car constitute less than 20% of the sample with average travel times of 38 and 41 min. The absolute frequency of different job categories is as follows: sales/marketing 18, research/consultancy 103, management/supervision 39, financial/accountancy 17, PC programming/operating 29, logistics/services 15, technical/manufacture 6, administrative/secretarial 18. Figure 2 visualizes JTVs clearly: in this radar plot, there are six axes, each of which denotes a code (ranging from 1 to 7) for the mean daily time period assigned to one job-task. Each polygon represents the mean JTV for each job category. For example, the JTV for the job category research/ consultancy is equal to (4.42, 3.75, 2.27, 3.14, 2.74, 2.49) indicating that for this category, tasks read/write, PC, and talking are the most time consuming or the main tasks, and tasks phone and mission are the least time consuming (and hence presumably less important) tasks for the average person in this job category. Similarly, the JTV for the job category sales/marketing is (3.61, 3.72, 3.06, 3.39, 3.44, 2.94), with tasks in decreasing order of time consumption including tasks PC, read/write, teamwork, talking, phone, and mission, with a low variance of mean time allocated across tasks. Job categories with the most and least time consumed for each task are also easily identified through the use of this figure. For instance, job categories with the most and least time spent on task PC, are PC programming/operating and logistics/services, respectively. Similarly, job categories with the most and least time consumption on task mission, are logistics/services and PC programming/operating, respectively. This figure represents the views of employees; the corresponding views of the supervisors are shown in
336
read/write
mission
7 6 5 4 3 2 1 0
PC
sales research management accountancy PC operation logistics manufacture
teamwork
phone
administration
talking Figure 2. Mean job-task vectors for job categories – views of employees.
Figure 3. The difference between these two figures is an indication of the need for the views of the two groups of respondents for a detailed analysis. To get a sense of the variation within a specific group of respondents, for example those (six observations) in job category 7 (technical/manufacture), the JTV polygons obtained from the views of every respondent in this group are presented in Figures 4 and 5, as rated by employees and supervisors, respectively. If all the respondents in this category rated their jobs the same, there would be only one single polygon. The more diverse the polygons, the more different the tasks pertaining to the same job are. As can be seen in Figures 4 and 5, the vectors assigned to the job-tasks of a single job category are rated differently by different jobholders and their supervisors. Hence, it is clear that simple job categorization does not suffice for a comprehensive analysis of telecommuting suitability. This fact emphasizes the importance and necessity of the concept of abstract job to the analysis of the problem. As an application of this concept, the suitability of the job for telecommuting for 0, 1, 2, or 3 days per week is modeled as a function of the time spent on each of its constituents.
5. Telecommuting suitability modeling Based upon the concept of abstract job, all different jobs can be broken down into their basic underlying tasks and represented by JTVs (as far as
337 read/write
mission
7 6 5 4 3 2 1 0
PC
sales research management accountancy PC operation logistics manufacture
teamwork
phone
administration
talking Figurre 3. Mean job-task vectors for job categories – views of supervisors.
telecommuting adoption is concerned). Job suitability for telecommuting can be modeled using basic job-tasks as continuous explanatory variables. Since suitability is measured in terms of the integer number of days a week the job can be telecommuted, this makes possible the application of discrete choice models such as logit. In these models, the utility of alternative i (Ui) is assumed to be a random variable whose systematic (or deterministic) and stochastic parts are represented by Vi, and ei, respectively: Ui ¼ Vi þ ei :
ð1Þ
The logit model assumes a Gumbel distribution for the random term, resulting in the formulation (Ben-Akiva & Lerman 1985): eV i PðiÞ ¼ P Vj ; e
ð2Þ
j2J
where J is the set of possible alternatives. A major drawback of this structure is that it assumes the independence from irrelevant alternatives, which is often unjustified in practice. The nested structure has been proposed to overcome this shortcoming. In this structure, correlated alternatives are placed in the same nest, separate from other alternatives, with which they are not correlated. For each nest k, a variable called the inclusive value (IVk) – also, referred to as the expected maximum utility or composite utility – is defined as
338 read/write 7 6 5
mission
4
PC
3 2 1 0
phone
teamwork
talking Figure 4. Job-task vectors for the six respondents in job category 7: technical/manufacture – views of employees.
the logarithm of the sum of the exponential function of the systematic utilities associated with the alternatives (Mk) in that nest: IVk ¼ ln
X
expðVm Þ:
ð3Þ
m2Mk
The inclusive value is used as an explanatory variable in the upper nest and its coefficient must be estimated between 0 and 1 to prove the structure appropriate, otherwise the nested structure is not a valid one for the problem being modeled. In maximum likelihood estimation for calibrating such models, the likelihood of the observations is maximized, which is equivalent to maximizing the logarithm of this function: namely the log-likelihood function (L) which plays an important role in model validation as well. Value of this function for the case when choice of alternatives are equally likely (EL), that is, all coefficients are equal to 0 (L(0), indicating equal probability for each alternative) can be used as a base in model validation. Also, the value at market share (MS), or the model with only constant terms (L(C), indicating
339 read/write 7 6 5 4
mission
PC
3 2 1 0
teamwork
phone
talking Figure 5. Job-task vectors for the six respondents in job category 7: technical/manufacture – views of supervisors.
choice probabilities equal to the respective aggregate shares in the observations) can be used as another, but higher base. Maximum value of this function L(b) can be used to show how much the model can improve from equal, or market share base. Various goodness of fit measures are defined, as (Ben-Akiva & Lerman 1985): q2ELbase ðbÞ ¼ 1
LðbÞ Lð0Þ
q2ELbase ðMSÞ ¼ 1 q2MSbase ðbÞ ¼ 1
LðCÞ Lð0Þ
LðbÞ LðCÞ
ð4Þ
ð5Þ
ð6Þ
5.1. Study variables The dependent variable, number of days ‘‘suitable and feasible’’ for telecommuting considering conditions and characteristics of the job, has four
340 response categories: not suitable (0), 1, 2, and 3 days per week. Supervisors’ answers to this question are also gathered. The explanatory variables (the mean daily time spent on each of the job-tasks presented in Table 1) can be measured with values from 1 to 7 (as ordinal codes), or as midpoints of the time duration intervals they represent. Since the purpose of this paper is to show the applicability of the proposed approach (rather than having a good fit), the study variables include only job-tasks. Both approaches assume a continuous nature for the variable. Using ordinal codes assumes equal distance between the response items, while using midpoints, the assumed distance between them is closer to the actual distance. While codes were used in the figures for more clarity, in the models hereafter, midpoints will be used because of their higher precision and easier interpretation.2 Correspondence between codes and midpoints is represented by the set of ordered pairs: (1,0.125) (2,0.375) (3,0.75) (4,1.5) (5,2.5) (6,3.5) (7,5), where the second member of each pair is the midpoint number of daily hours spent on the task. Use of logit models, including nested and multinomial logit, assumes a discrete nominal nature for the dependent variable, whereas our dependent variable is actually ordinal. In particular, in multinomial and nested logit models, variables can be defined and used as alternative-specific variables (whose impact on job suitability can vary by alternatives representing potentially non-linear relationships between time on various tasks and telecommuting suitability) rather than the more constrained generic ones (as assumed in ordered response models), and therefore more flexibility and explanatory power can be expected from these models. However, the ordered nature of the dependent variable does not necessarily mean that ordered response models have better results (Bhat & Pulugurta 1998). Figure 6 presents the two model structures used in this study. In the multinomial structure (Figure 6a), the choice consists of 4 alternatives (in a single nest); 0, 1, 2, and 3 week days, considering them as unrelated and independent alternatives. However, one might expect correlation of unobserved variables among the positive number of suitable telecommuting days (1, 2, and 3 days), as opposed to unsuitable telecommuting (0 days). Hence, as an alternative, a nested structure (Figure 6b), in which positive numbers are considered to comprise the lower nest, while the upper nest includes the binary choice of telecommuting versus not telecommuting, is also examined.
5.2. Model results Results of the nested structure model proved this structure to be inappropriate for this particular problem, thus the multinomial structure (Figure 6a)
341
(a)
Telecommuting Suitability
Not Suitable (0 days)
1 day
2 days
3 days
Multinomial Structure
(b)
Telecommuting Suitability
Not Suitable (0 days)
Suitable
1 day
2 days
3 days
Nested Structure Figure 6. Different structures used for modeling in this study.
was considered for both groups of respondents to examine the effectiveness of job-tasks. 5.2.1. Views of employees Results of the multinomial logit model regarding the views of employees are presented in Table 2. In this model, 5 alternative-specific variables (besides the 3 constant terms), relating to 3 tasks, are significant in the utility functions. The only task significant in all 3 utility functions is mission. The coefficient of this task is negative in all 3 functions. Thus, spending a lot of time on mission out of the office (presumably at specific required locations) reduces the suitability of the job for telecommuting. There are 2 tasks with positive effects in the third function, namely PC and talking on the telephone, which appear with coefficients of 0.173 and 0.206, respectively. These tasks
342 Table 2. Results of multinomial logit model for employees. Alternative
Variable
Coefficient
t-statistic
1 day
Constant Mission out of the office Constant Mission out of the office Constant Working with a PC Talking on the telephone Mission out of the office
0.143 )0.337 0.723 )0.428 )0.233 0.173 0.206 )0.255
0.543 )2.027 3.141 )3.016 )0.660 1.986 1.588 )1.890
2 days 3 days
The alternative of 0 days telecommuting per week is used as a reference, with zero utility. Log-likelihood at zero=)339.6, at market share=)335.7, and at convergence=)326.4. 2 335:7 2 326:4 q2ELbase ðbÞ ¼ 1 326:4 339:6 ¼ 0:039, qELbase ðMSÞ ¼ 1 339:6 ¼ 0:011, qMSbase ðbÞ ¼ 1 335:7 ¼ 0:028 (EL, equally likely model; and MS, market share model). Number of observations=245.
were not statistically significant in the first and second functions and thus, their effects are probably incorporated into the corresponding constant terms. 5.2.2. Views of supervisors Table 3 presents the multinomial logit model results regarding the views of supervisors. In this model, there are 4 alternative-specific variables besides the 3 constant terms, relating to 3 tasks. There is no task which appears in all 3 utility functions. The only task which appears in 2 utility functions (second and third) and with an increasing coefficient ()0.515 and )0.405) is talking. Thus, the more time the job requires to converse with clients and colleagues, the less suitable supervisors consider it to be for telecommuting. The 2 positive tasks PC and teamwork appear only in the third function, Table 3. Results of multinomial logit model for supervisors. Alternative
Variable
Coefficient
t-statistic
1 day 2 days
Constant Constant Talking and conversing with clients and colleagues Constant Working with a PC Talking and conversing with clients and colleagues Teamwork and participating in meetings
1.214 2.303 )0.515 0.272 0.367 )0.405 0.493
4.632 7.315 )2.912 0.579 3.822 )2.300 2.501
3 days
The alternative of 0 days telecommuting per week is used as a reference, with zero utility. Log-likelihood at zero=)339.6, at market share=)304.6, and at convergence=)289.3. 2 304:6 2 289:3 q2ELbase ðbÞ ¼ 1 289:3 339:6 ¼ 0:148, qELbase ðMSÞ ¼ 1 339:6 ¼ 0:103, qMSbase ðbÞ ¼ 1 304:6 ¼ 0:050 (EL, equally likely model; MS, market share model). Number of observations=245.
343 showing that the more time spent on these tasks, the more suitable the job for telecommuting 3 days a week. Thus, the two tasks most suitable for telecommuting adoption are teamwork and PC. Although, for some technologically oriented institutions the sign of teamwork may come out to be positive, in conventional institutions where presence at work is necessary for teamwork to take place, the sign of this task may be expected to be negative. Comparing and contrasting the modeling results for the employees with those for the supervisors results in a few points worth mentioning. First, both groups identify PC as an effective positive task. This is, in fact, the only significant task common in both models. Second, supervisors consider teamwork an important positive task as well as PC, whereas employees consider the task phone significant with a positive effect. Third, tasks with negative impacts are stronger in the sense that their coefficients have larger absolute values and also, they appear in more utility functions (at least 2). Also, goodness-of-fit is higher for the supervisors’ model than the employees’. As a basis for comparison, it is worth mentioning that Bernardino et al. (1993) have reported a q2ELbase ðbÞ of 0.271 for their ordered probit model of telecommuting preference, Sullivan et al. (1993), 0.147 for their multinomial logit model, and Yen and Mahmassani (1997), 0.252 for their dynamic generalized ordinal probit model.
6. Conclusions and further research In this paper, the concept of abstract job was proposed, composed of basic tasks with different continuous effects (positive or negative) on the suitability of the job for telecommuting. Suitability of jobs for telecommuting, based on job-task fitness, was modeled rather than choice of telecommuting based on self-utility maximization of employees as has been the norm in prior research; the suitability model can constitute the first stage of the conceptual chain shown in Figure 1. In order to show the applicability of the proposed approach, an instance of its implementation was carried out through discrete choice models on data from seven organizations in Tehran, Iran. Views of 245 employees and their direct supervisors as people in charge and responsible for them, with sufficient experience and knowledge, were employed to examine effective tasks suitable for telecommuting. A multinomial logit structure showed that job-tasks with positive impacts on telecommuting suitability are working with a PC, talking on the telephone and teamwork and participating in meetings. The 2 tasks with negative impact on suitability are mission out of the office and talking and conversing with clients and colleagues.
344 It needs to be pointed out that although the above results largely conform to common sense and logical expectations, the primary aim of this study was to show the applicability of the abstract job approach and an instance of its implementation. Finally, the following two points may be stated as avenues for further research: (a) other model structures like probit, ordered logit and probit, and Poisson regression models may be used to investigate the consistency of the results with respect to model structure, (b) grouping of datasets (regarding e.g. developed versus developing countries, technologically sophisticated versus conventional or traditional institutions) may be used to investigate the change in the coefficients sign and magnitude, as well as the significance in the model.
Acknowledgement The authors would like to thank Prof. P L Mokhtarian for her significant contributions to the concepts and organization of this paper. They would also like to thank the Institute for Transportation Studies and Research of Sharif University of Technology for their close cooperation and assistance. Cooperation of the companies participating in the surveys including CODIRAN and Tehran Comprehensive Transportation and Traffic Studies, various departments of ministries of Interior, Agriculture, Road and Transportation, Economics and Trade, and also, Tehran Municipality, Transportation and Terminals Organization, Institute for Management and Planning Studies, is also greatly acknowledged.
Notes 1. For example, in the first version, the 3 tasks working with a stand-alone PC, working with a PC connected to a LAN (local area network), and working with a PC connected to the Internet, were listed as separate and independent items. However, since the respondents could not generally distinguish and discriminate between the 3 items as to the mean daily amount of time they allocated to each, these 3 tasks were aggregated to the item working with a PC. This kind of aggregation was needed in some other instances as well. 2. Models calibrated with the codes showed poor results.
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About the authors Amir Reza Mamdoohi has received his Ph.D. in Transportation Planning and Engineering from Sharif University of Technology, in summer 2005. He has been a faculty member of the Dept. of Transportation Systems Planning at the Institute for Management and Planning Studies, Tehran, Iran since 1999 and the head of the Transportation Studies Group since 2003. He received his MS in Socio-Economic Systems Engineering in 1996 from the Institute for Research on Planning and Development, and his BS in Mechanical Eng. in 1989 from Sharif University of Technology. His areas of interest are transportation planning and modeling, travel demand modeling and analysis, transportation demand management and telecommuting. Mohammad Kermanshah has received his Ph.D. in Transportation Planning and Engineering from University of California (Davis) in 1983, his MS in CE from South Dakota School of Mines and Technology in 1978, and his BS from Shiraz University (Iran). He is currently Associate Professor of the Dept. of CE, and Vice-Chancellor on Research, at Sharif University of Technology, Iran. He has been working in the
346 areas of travel demand and activity analysis, trip chaining and comprehensive planning, and recently in the area of telecommunication. TDM and TSM are some of the other areas he has worked in recently. Hossain Poorzahedy has received his Ph.D. in Transportation from Northwestern University in 1980, his MS in CE Planning from Stanford University in 1976, and his BS from Shiraz University (Iran). He is currently Associate Professor of the Dept. of CE, and Director of the Institute for Transportation Studies and Research, at Sharif University of Technology, Iran. He has been working in the area of Network design, and recently in the area of modern heuristic algorithms to solve such problems. Reliability aspects of network design; network pricing and traffic management for air pollution control, are some of the areas he has worked in recently.
