Service Quality in the Commercial Passenger Transport Industry in India Authored By Nripendra Singh, Assistant Professor
[email protected] Jaypee Business School, JIIT University, Noida, India
William Koehler, Graduate Program Director College of Management, University of Massachusetts, Boston, MA USA
Mohan Agrawal, Director Jaypee Business School, JIIT University, Noida, India
Venue: 3rd International Conference on Services Management May 9-10, 2008 Penn Stater Conference Center Hotel 215 Innovation Boulevard State College, PA 16803 Phone: 814-863-5000
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ABSTRACT India’s rapid economic growth over the past several years, driven by the flourishing BPO, KPO, and financial services industries, has accelerated the development of India’s commercial passenger transport sector, which consists of four subsectors: Organized and Unorganized Conveyance Services and Organized and Unorganized Taxi Services. Our research will focus on the Organized Conveyance Services (OCS) subsector, comprised of firms contracted by corporations or private schools to transport employees and students to work or school. The OCS is characterized by a dual set of customer relationships, between the employee and employer, and between the employer and OCS firm, as well as the service delivery relationship between the OCS firm and employee, making this an intriguing case to consider. The sector currently accounts for nearly $10 billion in revenues in the US and an estimated INR 150-200 crore ($ 3750 million) range in India, with exponential increases projected in India over the next decade. We analyze key criteria for vendor selection and service quality, including tangibility, reliability, responsiveness, assurance, and empathy, as identified by Parasuraman et al. and others. Our work is designed in part to test the validity of Western measures of service quality and customer satisfaction when applied to a developing nation. Moreover, we intend to ascertain the utility of retail-oriented service quality tools in a non-retail service sector, one characterized by disjunctive relationships between perceived service quality and vendor selection. We are employing a subsector-specific measure based on the widely used SERVQUAL survey tool developed by Parasuraman, Zeithaml, and Berry (1988ff), to assess the service quality levels of several firms in the OCS subsector. The focus remains, however, on the sector as a whole rather than on the competitive environment. We argue that success and growth in this subsector in India will largely hinge on firms’ abilities to strengthen client loyalty through customer relationship management (CRM) practices, allowing greater pricing flexibility and accurate forecasting. The SERVQUAL-based measure employed in this research confirms several of the general conclusions of Parasuraman et al. while identifying trenchant areas of concern for the firms operating in this subsector and employers relying on these services to retain employees.
INTRODUCTION One of the most rapidly developing new service areas in India is the Organized Conveyance Services (OCS) subsector, which provides employee and student shuttle services for businesses and educational institutions. The recent dramatic growth in business process outsourcing (BPO), knowledge process outsourcing (KPO), and financial services employers in India’s National Capital Region, centered on Delhi, has spurred demand for these services, even as Delhi lags behind Bangalore in outsourcing revenue. Numerous multinational firms, including Dell, Accenture, Honda, American Express, Wipro, IBM, Microsoft, HCL, and Motorola, have located significant operations in Gurgaon, one of the two main satellite cities in the NCR, alone. Despite India’s chronic underemployment, the number of well-qualified employees remains small, and staff turnover is exceptionally high as firms compete fiercely for talented young professionals. Retention efforts have focused on the amenities provided to workers, with employee shuttle services as one effective recruiting tool in this overcrowded metropolis. This subsector provides
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a particularly interesting case in part because the daily service transaction takes place with a customer who does not directly make purchasing decisions, and thus the link between perceived service quality and the purchasing decision is mediated by the existing employer-employee relationship. Gurgaon and Noida, like many such satellite cities, draw their workforces in large part from Delhi, presenting a reversed commute from that in the typical American city, but one made infinitely more stressful by the overcrowding, poor infrastructure, and haphazard traffic patterns that characterize urban India. Car ownership is a luxury, with gasoline costing nearly $22 per gallon when purchasing power parity is factored in, and reliable, safe public transport is scarce, though the Delhi Metro is scheduled to reach Gurgaon in 2011 and Noida in 2014. Many BPO, KPO, and financial services workers work night shifts, so as to be on the same work schedule as North Americans or Europeans, making public transit infeasible, however. Moreover, having a “driver”, even if shared in a shuttle service, confers a substantial status in India, with a concomitant boost in employee recruitment, satisfaction, and retention. Thus, the demand for outsourced OCS has drawn new firms into the market and created a highly competitive landscape for the subsector. The relative immaturity of the subsector and lack of consistent standards has made it difficult for OCS firms and their customers, principally large MNCs, to evaluate service quality, as our survey and focus group results clearly indicate. The measure we developed, drawn from the SERVQUAL questionnaire, was tailored to the concerns of the end-user population, the actual commuters. This tool serves as a test of the applicability both of Western SQ models to emerging nations and of retail-oriented tools to more relational models of service interaction. Through the use of focus group discussions and a survey, we identified the three SQ factors with the most explanatory power for the OCS subsector: Tangibles, Reliability, and Responsiveness, drawing upon the categories of Parasuraman et al. While the particular characteristics of this industry and cultural differences result in significant deviations from Western norms, our research found strong similarities between the factors determining service quality perceptions for this subsector. We thus maintain that a substantially modified version of SERVQUAL possesses value for OCS firms in assessing service quality and directing CRM strategies.
LITERATURE REVIEW The extensive literature on service quality offers numerous quantitative and qualitative frameworks by which to determine consumer expectations and perceptions of service quality. The most widely utilized (and criticized) model is SERVQUAL, the 22-item instrument developed by Parasuraman, Zeithaml and Berry (1985) and refined numerous times by these and other authors since then (Parasuraman et al. 1985, 1990, 1991, 1992, 1993, 1996; Fick and Ritchie, 1991; Johnston, 1995; Bennington and Cummane, 1998; Erasmus and Donoghue, 1998; Gwynne, Devlin and Ennew, 2000; Liu, 2005). SERVQUAL originally centered on measuring the gap between expected and perceived service quality, arising from the disconfirmation paradigm of gap theory, but the subsequent modifications have adjusted that approach. Many alternative frameworks, both industry-specific and general ones, for measuring customer satisfaction and service quality have been advanced, including CUREL, INDSERV, SITEQUAL, SERVPERF, and BANKSERV (e.g., Cronin and Taylor, 1992; Avkiran, 1997; Payne and Holt,
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2001; Gounaris, 2005; Horn, Feinberg, and Salvendy, 2005; Jain, Jain and Dhar, 2007). Many more authors have sought to elaborate an industry- or type-specific application of SERVQUAL (e.g., Bei and Chiao, 2001; Kettinger, Lee, and Choong, 2005). The criticism of the SERVQUAL measure has focused on several aspects. The most serious theoretical and replicative critiques include: the failure of the approach to account for the significant role of price in service quality; the inapplicability of certain factors to all service industries; the emphasis on the limited reliability and validity of the psychometric elements; the reliance on functional aspects of service quality at the expense of technical outcomes; the ordinal (Likert-scale) structure of the questionnaire items; the muddied distinction between measures of service quality and customer satisfaction; and the reduced probative value of the tool for product services and certain “pure” services (e.g., Carmen, 1990; Mangold and Babakus, 1991; Richard and Allaway, 1991; Cronin and Taylor, 1992, 1994; Iacobucci, 1994; Rust and Oliver, 1994; Smith, 1995; Buttle, 1996; Liljander and Strandvik, 1997; Preston and Colman, 2000; Brady, Cronin, and Brand, 2002; Chui, 2002; Coulthard, 2004; Kettinger et al., 2005; Andotra and Pooja, 2007). Many of the deficiencies that inhere in SERVQUAL, however, apply to other standardized measures, especially SERVPERF, and many of the other alternatives have proven too cumbersome in practice. Moreover, the categorizations developed by Parasuraman, Zeithaml, and Berry, though somewhat arbitrary, possess both broad intuitive and predictive validity. While we certainly recognize the many limitations of the measure, we nonetheless accept that the basic framework of SERVQUAL offers greater general utility than do other competing tools. We have thus chosen to employ a thoroughly modified version of SERVQUAL, one tailored to the specific concerns of the commercial passenger transport industry in India but drawing upon many of the features of Cronin and Taylor’s SERVPERF tool and accepting their distinction between measures of service quality (relational) and customer satisfaction (transactional). Since the nature of the interactions in this industry is daily and ongoing, the transaction-based SERVPERF seems inherently less relevant to this sector than does the relational, service-quality focus of SERVQUAL. To ameliorate the most salient limitations of SERVQUAL, our study utilized extensive focus group discussions to arrive at the industry- and culture-specific elements of our questionnaire. In line with the findings of Cronin and Taylor and subsequent authors, we agree that customer perceptions of service quality implicitly incorporate notions and calculations of expectation of service quality, and thus utilize a single questionnaire to measure perceptions, rather than a twoadministration questionnaire that relies upon the disconfirmation paradigm of the original SERVQUAL tool. We also maintain that this approach best reflects the qualitative assessments arising from our preceding focus group work. Moreover, since this industry presents an unusual disconnect between purchasing intention (employer-, purchaser-centered) and either customer satisfaction or service quality (primarily employee-, end-user-centered), SERVPERF’s more significant correlation with purchasing intention is irrelevant for our target industry and population. For this industry, employee (end-user) perceptions of service quality are heavily filtered and somewhat skewed before factoring into vendor selection at the employer level. Finally, we concur with more recent studies (e.g., Jain and Gupta, 2004) that the superior
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diagnostic value of SERVQUAL over SERVPERF affords the former greater utility in shaping the behavior of service firms.
METHODOLOGY Focus Group Discussions We utilized 5 focus groups, totaling 31 participants, to assist us in determining the key issues for end users of shuttle services in the NCR and thus the items on our questionnaire, as well as to test the results of our statistical analyses. The first three focus group discussions took place before the questionnaire was developed and administered, while the final two were used as a check on the analysis resulting from the survey findings. Survey Sample The population was defined as employees using the transport services provided by the companies for commuting between home and work. The sample consisted of 304 employees from six different corporations in India’s National Capital Region (NCR), which includes New Delhi and the burgeoning satellite cities of Noida and Gurgaon. Initially, focus group discussions were conducted of the employees to find out the attributes responsible for customer satisfaction and evaluation of service quality. Utilizing the results of our focus group work, we developed a 24item questionnaire, a significantly modified version of the basic SERVQUAL framework. The companies selected were of roughly the same level and size. While the subjects were selected on a convenience sampling basis, we nonetheless ensured that they were of approximately the same professional level and utilized comparable transportation services. Data collection An initial sample of 304 staff was obtained, who use transportation services provided by the third party from their companies. It was done using MBA student interviewers. Questionnaires were self-administered and reflected opinions about the transportation service provider. Opinions were obtained from administrative support and entry-level professional staff members of six different firms. Of the respondents, 51% were female. A cross-validation sample of 50 employees was also surveyed. We distributed an equal number of questionnaires (200 each) in person and via post or electronic distribution. 52 of the 200 e-mailed and mailed questionnaires (26%) were returned, compared with 152 of the 200 hand-delivered ones (76%). Questionnaire Questionnaires were administered by e-mail, post, and by hand. To ensure randomness, the employees were selected from different working shifts, though a higher percentage of those surveyed worked evening/night shifts. It was given to only those employees who had been using the transportation services for a minimum period of six months so as to focus on sustained perceptions of service quality rather than on short-term transactional perceptions, thus enabling us to focus on service quality rather than customer satisfaction. All respondents completed the questionnaire on the same day it was received. Respondents were also requested to provide their answers at the time when they were waiting for their transport vehicle to come, or immediately upon their arrival at the office from the transport vehicle. The theoretical rationale for the data collection method is that respondents will be more attentive to the task of completing a questionnaire and provide more meaningful responses when they are contextualized in the
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environment that they are evaluating. Further, while in the relevant environment, employees would be more likely to focus on dimensions of importance to them for evaluating the quality of service of the transport services. An additional motivation was the avoidance of reliability issues associated with similar techniques that require respondents to recall past service experiences. Our questionnaire consisted of 24 items, with approximately 73% overlap with the 22 items on the SERVQUAL questionnaire. The questions employed in our survey were based roughly upon the performance questions in the 22-item SERVQUAL measure, with a number of modifications suggested by our original qualitative research (see Appendix 1). S2 corresponds roughly to P6; S6 to P3; S7, S9 and S23 to P2 and P4; S8, S10 and S16 to P5, P8, and P11; S12 to P13; S14 to P16; S15 to P7; S17 to P18 and P19; S18 to P12; S21 to P1; and S22 to P14 and P15. While the framework of SERVQUAL is intuitively useful across all service industries, the particular items in the perceived service quality survey are most relevant to the retail interactions for which the measure was originally developed, as the authors suggest. We have chosen to interpret the authors broadly when they indicate that “The skeleton, when necessary, can be adapted or supplemented to fit the characteristics or specific research needs of a particular organization” (Parasuraman et al., 1988: 31). The measurement we employ to determine transportation service quality consists of a hierarchical factor structure comprising the five dimensions outlined above. The scale is designed specifically for use in assessing relative and absolute levels of perceived service quality in transportation businesses that offer shuttle services to commuting staff of third-party firms or to students. The results obtained should also be useful in detecting inadequacies in transportation services, both enabling purchasers to make more informed choices about OCS providers and assisting providers in addressing perceived shortfalls and inadequacies. Confirmatory factor analysis with partial aggregation (i.e. individual indicators randomly combined into composite indicators) was used to test the proposed scale structure. Adequate fit was obtained using both samples for the sub-dimensions models, as well as the basic fivedimension SERVQUAL model of Parasuraman et al: •
Dimension 1, TANGIBILITY, with eight attributes: Vehicle Music System, Vehicle Cleanliness, Vehicle Model, Relaxation during Transit, Interior Vehicle Appearance, Exterior Vehicle Appearance, Vendor Brand, and Vehicle AC System.
•
Dimension 2, EMPATHY, with four attributes: Employer Attentiveness to Feedback, Pickup Waiting Time, Vehicle Identification Logo, and Employer Response Time on Complaints.
•
Dimension 3, RESPONSIVENESS, with five attributes: Commuting Time, Ease of Vehicle Identification, Vehicle Route, Punctuality of Pickup, and Vehicle Crowding.
•
Dimension 4, RELIABILITY, with three attributes: Consistency of Schedule, Employer Collection of Feedback, and Schedule Change Notification.
•
Dimension 5, ASSURANCE, with four attributes: Driver Cooperativeness, Driving Skill, Driver Appearance, and Driver Courteousness.
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Factor Analysis for Data Reduction Stage 1, Factor extraction process The correlation matrix constructed from the data obtained to know the key evaluation criteria for judging service quality, is shown in Table 1, the Correlation Matrix. There is high correlation among the attributes S1, S2, S6, S9, S10, S16, S17, S18, S20, S22, and S23; among S3, S5, S15, and S24; among S4, S7, S13, and S21; and among S8, S11, S12, and S14. These variables may also be expected to correlate with the same factors. The results of factor analysis are given in Table 4. The implicit null hypothesis, that the population correlation matrix is an identity matrix, is rejected by the Bartlett's test of sphericity. The approximate chi-square statistic is 2,915.661 with 206 degrees of freedom, which is significant at the 0.000 level. The value of the Kaiser-Meyer-Olkin measure of sampling adequacy (0.817) is also large, (> .5). Thus factor analysis may be considered an appropriate technique for analyzing the correlation matrix of Table 1. We then drew upon the eigenvalue rule-of-thumb, limiting further analysis to those factors with eigenvalues greater than 1.00. Our results produced three factors with eigenvalues greater than 1.00, corresponding to the factors of Tangibility, Reliability, and Responsiveness (Table 3). Stage 2, Rotation of principal components We employed a varimax approach in our orthogonal rotation, utilizing our analysis of factor loadings in the resulting factor matrix (Table 2). As both the rotated factor matrix and the table of component analysis identify the same group of ten attributes as clustering in factor 1, we have collapsed attributes S1, S3, S5, S8, S9, S11, S18, S21, S23 and S24 as factor one, Tangibility, as these attributes correspond generally to this category. We also find that attributes S4, S10, S12, S13, S16, and S19 have high loadings of 0.683, 0.550, 0.586, 0.604, -0.536, and .714 and group them as factor two, Reliability. Attributes S2, S6, S17, S20, and S22, grouped as Responsiveness, have high loadings of 0.399, 0.397, 0.565, 0.433, and 0.468, respectively. Our factor matrix suggests that these three of our original five factors represent the most significant criteria in perceptions of service quality. Stage 3, Scree plot analysis A scree plot is a plot of the eigenvalues against the number of factors in order of extraction. The shape of the plot is used to determine the number of factors. Typically, the plot has a distinct break between the steep slope of factors with large eigenvalues and a gradual trailing off associated with the rest of the factors. This gradual trailing off is referred to as the scree. As we can see from the shape of the scree plot (Table 6), there is a distinct break after the first factor, second factor and third factor only, and therefore we have extracted three factors.
RESULTS AND DISCUSSION Our results indicate that transportation service quality for the Organized Conveyance Services subsector is perceived by the employees on three dimensions, unlike other service industries in which all five SERVQUAL dimensions are valuable and significant determinants of perceived
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service quality. Tangibility, Reliability, and Responsiveness represent the categories of greatest consequence for the end users, accounting collectively for nearly 74% of variance. The scale developed can be used for the gathering data than can be used for benchmarking current levels of service quality, as well as regular checks to measure service quality, in not only the OCS subsector, but in numerous related sectors in public and private transportation, with modification. The scale is also suitable for using it to help the transporter identify areas of service delivery that are weak and in need of managerial attention. Also it can be used to evaluate overall and dimension-specific quality and would enable managers to identify problem areas within their offices and the service provider. Managerial implications The unique set of customer relationships in the OCS subsector, between the employer and transportation firm, the employer and employee, and the transportation firm and the employee, requires tailored guidelines for managing expectations and the establishment of adequate levels of perceived service quality. The three factors below should constitute the primary emphases of service and quality management initiatives in the OCS subsector and ought to provide the greatest return on investment in QM. FACTOR 1, TANGIBILITY • Relaxation during Transit • Vendor Brand • Vehicle Identification Logo • Vehicle Music System • Vehicle Cleanliness • Vehicle Model • Interior Vehicle Appearance • Exterior Vehicle Appearance • Vehicle AC System • Frequency of Schedule Changes • Vehicle Crowding FACTOR 2, RELIABILITY • Vehicle Route • Commuting Time • Employer Collection of Feedback • Ease of Vehicle Identification • Punctuality of Pickup • Commitment of the time by the driver FACTOR 3, RESPONSIVENESS • Employer Attentiveness to Feedback • Employer Response Time on Complaints • Driver Appearance • Driving Skill
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Our survey and focus group discussion research indicates that customer assessments of service quality in the OCS subsector are remarkably negative, though certainly not uniformly so. While many of the externalities discussed herein, such as the infrastructure issues in the NCR, adversely impact assessment, one common theme among those polled and interviewed was a sense of frustration with the lack of service quality evaluation on the part of the transportation firms and employers. Mistaking our research for an employer-sponsored initiative, the respondents were quite receptive to our efforts. One such employee whom we surveyed wrote, "Thanks a lot to the Admin dept...At least after hundreds of complaints you have thought of taking our feedback. Let us see how much you will incorporate from our responses, or is it simply to waist [sic] the paper and our time." While numerous aspects of perceived service quality in the OCS need to be addressed, coordinated efforts on the part of the employers or OCS firms to collect end-user perceptions would pay huge dividends simply by demonstrating interest in improving satisfaction. The lacunae in active CRM efforts in the Organized Conveyance Service subsector in India are symptomatic of a widespread inattentiveness to issues of customer service that typifies much of the service sector in India. The American axiom that “the customer is always right” characterizes neither the rhetoric nor the nature of most service interactions in India. Expectation levels for service quality appear to be significantly lower in India than in the West Andotra and Pooja, 2007), mirroring differences between the US and Europe, but with a more significant differential (Witkowski and Wolfinbarger, 2003), and similar to those for East Asian societies (Furrer, Liu, and Sudharshan, 2003). However, globalization, rising standards of living, and market liberalization have all significantly raised customer expectations for service quality across industries and income levels, though particularly for those in the higher income brackets in India (what Indians refer to as “middle-class” corresponds roughly to the top quintile in income and standard of living). Avenues for Future Research Our work is intended to function as an exploration of the basic concepts of service quality assessment in a particular cultural and industry setting. Moreover, we believe that this work lays a practical foundation for broader applications of service quality assessment in transport services in emerging and developing economies, especially in situations where rising consumer expectations are outpacing advances in CRM efforts, as is certainly the case in many sectors in India. Additional research on the tool employed here, its applicability and adaptability for related sectors, and its utility in other regions or countries is certainly warranted.
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Table 1: Correlation Matrix Table 1: Correlations VAR01
VAR02
VAR03
VAR04
VAR05
VAR06
VAR07
VAR08
VAR09
VAR10
VAR11
VAR12
VAR13
VAR14
VAR15
VAR16
VAR17
VAR18
VAR19
VAR20
VAR21
VAR22
VAR23
VAR24
VAR01 1 VAR02 0.706
1
0.579
0.63
VAR03 1
VAR04 0.535
0.504
0.646
1
0.597
0.636
0.562
0.453
0.623
0.584
0.317
0.353
0.695
1
0.49
0.54
0.587
0.359
0.571
0.416
1
0.582
0.517
0.369
0.448
0.481
0.576
0.445
0.669
0.468
0.236
0.249
0.376
0.397
0.385
0.475
1
0.648
0.519
0.241
0.413
0.438
0.593
0.312
0.647
0.641
1
VAR05 1
VAR06 VAR07 VAR08 1
VAR09 VAR10 VAR11 0.554
0.523
0.326
0.48
0.48
0.476
0.347
0.549
0.446
0.567
1
0.567
0.537
0.321
0.469
0.469
0.574
0.311
0.654
0.544
0.723
0.692
VAR12 1
VAR13 0.561
0.503
0.583
0.46
0.459
0.54
0.363
0.569
0.471
0.582
0.584
0.616
1
0.454
0.448
0.249
0.443
0.454
0.467
0.375
0.525
0.343
0.543
0.578
0.586
0.606
0.558
0.633
0.396
0.434
0.537
0.635
0.344
0.581
0.399
0.505
0.495
0.605
0.641
0.498
1
0.604
0.53
0.283
0.37
0.553
0.66
0.362
0.533
0.428
0.566
0.526
0.505
0.486
0.554
0.7
1
VAR14 1
VAR15 VAR16 VAR17 0.646
0.59
0.35
0.359
0.572
0.695
0.385
0.653
0.463
0.648
0.506
0.562
0.519
0.419
0.672
0.724
1
0.612
0.604
0.417
0.489
0.625
0.602
0.466
0.579
0.331
0.484
0.58
0.577
0.476
0.566
0.604
0.676
0.655
1
0.508
0.44
0.298
0.276
0.515
0.557
0.515
0.541
0.404
0.432
0.344
0.477
0.465
0.455
0.548
0.505
0.607
0.587
1
0.635
0.526
0.357
0.285
0.563
0.618
0.49
0.498
0.409
0.456
0.541
0.475
0.419
0.513
0.615
0.743
0.613
0.676
0.599
1
0.527
0.517
0.525
0.384
0.494
0.557
0.352
0.639
0.439
0.594
0.56
0.686
0.556
0.498
0.563
0.508
0.568
0.569
0.565
0.503
1
0.616
0.597
0.417
0.355
0.59
0.635
0.394
0.489
0.294
0.427
0.444
0.359
0.414
0.421
0.566
0.701
0.591
0.655
0.534
0.749
0.445
VAR18 VAR19 VAR20 VAR21 VAR22 1
VAR23 0.61
0.553
0.394
0.468
0.555
0.624
0.411
0.502
0.396
0.521
0.434
0.519
0.475
0.285
0.637
0.598
0.624
0.503
0.518
0.568
0.47
0.654
1
0.577
0.591
0.421
0.316
0.388
0.382
0.414
0.484
0.487
0.533
0.559
0.601
0.467
0.427
0.536
0.47
0.508
0.484
0.428
0.51
0.566
0.396
0.439
VAR24 1
**. Correlation is significant at the 0.01 level (2-tailed).
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Table 2: Results of Principal Components Analysis KMO and Bartlett's Test: Bartlett's Test of Sphericity Approx. Chi-Square = 2,915.661 Df = 206 Significance = 0.000 Kaiser-Meyer-Olkin measure of Sampling Adequacy = 0.817 Communalities Initial Extraction VAR00001 1.000 0.739 VAR00002 1.000 0.453 VAR00003 1.000 0.814 VAR00004 1.000 0.652 VAR00005 1.000 0.682 VAR00006 1.000 0.411 VAR00007 1.000 0.253 VAR00008 1.000 0.646 VAR00009 1.000 0.687 VAR00010 1.000 0.543 VAR00011 1.000 0.645 VAR00012 1.000 0.577 VAR00013 1.000 0.648 VAR00014 1.000 0.300 VAR00015 1.000 0.254 VAR00016 1.000 0.554 VAR00017 1.000 0.427 VAR00018 1.000 0.761 VAR00019 1.000 0.689 VAR00020 1.000 0.459 VAR00021 1.000 0.673 VAR00022 1.000 0.410 VAR00023 1.000 0.683 VAR00024 1.000 0.598 Extraction Method: Principal Component Analysis.
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Table 3: Eigenvalues Total Variance Explained Initial eigenvalues % of Cumulative Component Total Variance % 1 12.826 41.125 41.125 2 3.861 13.418 54.543 3 2.518 8.235 62.778 4 1.191 4.107 66.885 5 1.072 3.696 70.581 6 0.895 3.186 73.767 7 0.857 3.001 76.768 8 0.714 2.994 79.762 9 0.662 3.429 83.191 10 0.659 2.993 86.184 11 0.611 2.882 89.066 12 0.555 2.701 91.767 13 0.522 1.996 93.763 14 0.452 1.567 95.33 15 0.377 1.167 96.497 16 0.337 0.901 97.398 17 0.26 0.467 97.865 18 0.181 0.308 98.173 19 0.16 0.197 98.37 20 0.1 0.102 98.472 21 0.053 0.78 99.252 22 0.032 0.56 99.812 23 0.004 0.15 99.962 24 0.001 0.038 100 Extraction Method: Principal Component Analysis.
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Table 4: Component matrix Component Matrix(a) VAR01 VAR02
1
2
3
4
5
0.821
-0.111
0.324
-0.099
0.167
0.328
-0.162
0.399
-0.092
0.656
-0.345
0.123
-0.186
0.107
0.683
0.386
-0.34
0.333 0.035 0.161
0.739
0.352
0.084
-0.073
0.02
0.314
-0.188
0.397
-0.025
0.07
0.281
-0.222
0.266
-0.303
0.199
0.759
0.328
-0.111
-0.2
0.034
0.609
0.302
0.039
0.088
0.314
0.256
0.55
-0.09
-0.055
0.728
0.239
0.304
-0.182
0.225 0.153
VAR03 VAR04 VAR05 VAR06 VAR07 VAR08 VAR09 VAR10 VAR11 VAR12 VAR13 VAR14 VAR15
0.171
0.586
-0.044
-0.31
0.1
0.107
0.604
-0.033
-0.282
0.163
0.267
0.224
0.105
-0.324
-0.24
0.172
-0.04
0.294
-0.098
0.171
-0.536
-0.171
-0.001
0.226 0.112
VAR16 VAR17 VAR18 VAR19
0.194
-0.09
0.565
0.016
0.128
0.783
-0.161
-0.139
-0.137
-0.29
0.587
0.714
-0.219
0.102
0.371
-0.2
0.433
0.218
0.764
0.271
-0.323
0.082
0.236
-0.375
0.468
0.203
0.148 0.151 0.094 0.219
0.733
-0.27
-0.066
0.142
0.695
0.274
0.156
0.116
VAR20 VAR21 VAR22 VAR23 VAR24
0.221 0.344
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. 5 components extracted.
Page 3 of 19
Table 5: Factor loading and rotation Component
Extraction Sums of Squared Loadings % of Variance
Total 1 2 3 4 5
Cumulative %
12.826
41.125
41.125
3.861
13.418
54.543
2.518
8.235
62.778
1.191
4.107
66.885
1.072
3.696
70.581
Component Score Coefficient Matrix 1 VAR01 VAR02 VAR03 VAR04 VAR05 VAR06 VAR07 VAR08 VAR09 VAR10 VAR11 VAR12 VAR13 VAR14 VAR15 VAR16 VAR17 VAR18 VAR19 VAR20 VAR21 VAR22 VAR23 VAR24
2
3
4
5
0.61
-0.006
0.142
-0.098
0.122
-0.051
-0.015
0.337
-0.01
0.008
0.622
-0.041
0.434
-0.014
-0.093
-0.187
0.555
0.35
-0.011
-0.168
0.785
-0.042
0.164
-0.09
-0.039
0.167
0.011
0.357
-0.128
0.031
0.006
-0.13
0.241
-0.095
0.115
0.516
0.173
-0.016
-0.101
0.039
0.683
-0.069
-0.035
-0.049
0.454
-0.059
0.496
-0.079
-0.05
0.25
0.584
0.217
0.015
0.041
-0.086
-0.11
0.41
-0.031
-0.066
0.134
-0.094
0.391
0.029
-0.162
0.146
-0.055
0.311
-0.029
-0.02
-0.178
0.092
0.091
-0.051
-0.103
0.014
0.199
0.369
-0.13
-0.051
-0.103
0.145
0
0.387
-0.106
0.102
0.411
0.15
0.009
-0.007
-0.265
0.174
0.487
-0.061
-0.082
0.109
0.228
-0.072
0.207
0.097
-0.114
0.515
0.077
-0.081
0.154
0.003
0.255
-0.102
0.334
0.092
-0.203
0.75
-0.165
0.028
-0.071
0.163
0.68
-0.001
0.004
0.157
0.12
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores.
Page 4 of 19
Component Transformation Matrix Component 1
1 0.802
2 0.506
3 0.389
4 0.335
5 0.344
2
0.536
0.474
-0.446
0.416
0.341
3
0.520
-0.207
0.752
0.348
0.027
4
0.275
-0.638
-0.288
0.639
0.162
5
-0.063
-0.264
0.052
-0.430
0.859
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Table 6: Scree plot 14 12
Eigenvalue
10 8 6 4 2 0
1
3
5
7
9
11 13 15 17 19 21 23
Com ponent Num ber
Page 5 of 19
Appendix A QUESTIONNAIRE: CUSTOMER PERCEPTION Directions: With reference to the TRANSPORT SERVICE provided to you in your company, we would like to seek your response on certain dimensions, to understand your level of SATISFACTION with the QUALITY OF SERVICE provided to you. Please give your response on an Agreement-Disagreement scale of 1 to 7, where 1 represents “strongly disagree” and 7 represents “strongly agree”.
STATEMENTS S1: The trip is comfortable and relaxing
RANK out of "1 to 7" 1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
S2: When you have problems with transport, your employer is sympathetic and reassuring S3: The transport provider is a recognized brand S4: The vehicle follows a consistent, approved route S5: The vehicle has an easily identifiable logo for rapid boarding S6: The drivers are well-dressed and appear neat S7: The appearance of the vehicle’s interior is appealing S8: The vehicle arrives for pickup and drop off at the promised times S9: The appearance of the vehicle’s exterior is appealing S10: The transportation service does not bring you promptly to work and home S11: The vehicle has a properly functioning AC unit S12: Employer takes quick action on requests or complaints S13: The vehicle is easy to identify, even during rush hour S14: The drivers of the vehicle are polite S15: Employees are notified properly of schedule changes S16: Waiting time for pickup is not excessive S17:The employer takes regular feedback for maintaining quality S18: The vehicle has a properly functioning stereo S19: The drivers are committed to maintaining the schedule S20: The drivers are cooperative and helpful S21: The model of vehicle is out of date and of low quality S22: The drivers are skillful and safe S23: The vehicle is clean and well-maintained S24: The vehicle is regularly overcrowded NAME (optional): COMPANY NAME: MOBILE (optional):
Page 6 of 19
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Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1991), "Refinement and Reassessment of the SERVQUAL Scale," Journal of Retailing (67), Fall, pp. 420-450. Pitt, L. F., Watson, R. T., and Kavan, C. B. (1995) "Service Quality: A Measure of Information Systems Effectiveness," MIS Quarterly 19 (2), pp. 173-187. Teas, R.K. (1993), “Expectations, Performance Evaluation and Consumers’ Perceptions of Quality”, Journal of Marketing Research, 25 (May), pp. 204-212. Van Dyke, T. P., Kappelman, L. A., and Prybutok, V.R. (1997), "Measuring Information Systems Service Quality: Concerns on the Use of the SERVQUAL Questionnaire," MIS Quarterly, 21(2), pp. 195-208. Witkowski, T H and Wolfinbarger, M F (2002), “Comparative Service Quality: German and American Ratings across Service Settings,” Journal of Business Research, 55 (11), 875-81. Zeithaml, V., Parasuraman, A., and Berry, L. L. (1990), Delivering Quality Service: Balancing Customer Perceptions and Expectations, Free Press, New York. Zeithaml, V A and Parasuraman, A (1996). “The Behavioral Consequences of Service Quality,” Journal of Marketing, 60 (April), 31-46. Zeithaml, V A and Bitner, M J (2001). Services Marketing: Integrating Customer Focus Across the Firms, 2nd Edition, Boston: Tata-McGraw Hill.
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