Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.3 pp.597-609

Assessing Mobile Location-Based Service (m-LBS) Quality: A Combination of m-LBS Quality Scale and Importance-Performance Analysis

M. Mujiya Ulkhaq*, Auni W. I. Pertiwi, Lakshita Pritandari, M. Taufik Aditya
Department of Industrial Engineering, Faculty of Engineering, Diponegoro University, Semarang 50275, Indonesia
*Corresponding Author, E-mail:
December 28, 2017 May 8, 2018 March 20, 2020


The popularity of personal navigation devices and mapping services have made the society utilizes certain locationbased ideas and their applications. With the convenience of using smart phones, it could create an opportunity for the development of mobile location-based service (m-LBS) in order to bring personalized values to the users. Consequently, it leads to a fierce competition among the providers of the m-LBS applications; thus, they have to improve their service qualities in order to fulfill the customers’ needs. This study tried to assess the m-LBS quality using the m-LBS quality scale and combine it with the importance-performance analysis (IPA) model. The m-LBS quality scale has been shown to be statistically reliable and valid for use in measuring the m-LBS quality. However, assessing the service quality per se is considered not sufficient since the service providers have to prioritize the aspects they want to improve. It has to be done since they are constrained by limitations on the resources they have. The IPA model, on the other hand, is regarded as an effective method to find out particular aspects that performed poorly but are highly important. A case study to exhibit the applicability of the proposed method was conducted in one of the biggest motorcycles- based transport services in Indonesia.



    The manufacturing sector has been displaced by the service sector over the past decades which led to the development of many service industries in both developed and developing countries. This is also confirmed by a shift of employment from manufacturing sector to the service sector; where in most of the developed countries, such as the United States, the United Kingdom, Germany, France, and Japan, more than 70% of the labor force is engaged in the service sector (World Bank, 2017). Consequently, the service providers have to continuously improve the quality of the services that they have to offer to their customers.

    The service in today’s highly competitive global market has evolved dramatically by the support of the Internet. The Internet has obviously penetrated and changed the way people do communication, and obviously, do business. With the aid of the Internet and its rapid global growth, the service providers have to accept and adopt new information as well as communication technology since it can facilitate their activities without being bounded by distance and time. Over the past several years, one could observe the rapid revolution in location-based technology and geospatial information due to the ubiquity of geographic layer in many everyday activities. Currently, the service providers employ the location-based service (LBS)—we use the term mobile-LBS (m-LBS) since mobile devices are usually utilized to accomplish particular services—to provide personalized information based on customer location and surrounding environment (Duri et al., 2001;Pura, 2005;Dhar and Varshney, 2011). The market for m-LBS is rapidly expanding and the provision of m-LBS is accelerating globally (Lee et al., 2014). With more than 70% of smart phone users in the United States that used m-LBS (Zickuhr, 2013), the m-LBS industry is expected to earn 43.3 billion US dollars in revenue by 2019 worldwide (Sorrell, 2014).

    The m-LBS indeed gives several conveniences for the customers to find needed information about places, such as coffee shops, restaurants, shopping malls, stores, or even some events. However, although m-LBS gives many advantages to the customers, there are several limitations in the usage of m-LBS; for example, it cannot reach some remote locations and it needs a stable internet connection. In addition, most m-LBSs are still below customer expectations. In addition, customers also perceive some quality issues which are considered to be the critical barriers to provide satisfactory levels of m-LBS. For that reason, service providers have to put concern about the quality of the m-LBS to increase customer satisfaction and enhance company competitiveness (Chang et al., 2007;Petrova and Wang, 2011).

    The service quality, in general, has to be enhanced and continuously improved since it has been considered to be a vital aspect for the success of the service provider (Parasuraman et al., 1985;Gilbert and Veloutsou, 2006;Chow et al., 2007;Ulkhaq et al., 2016a). In addition, excellent service precedes customer retention and leads to repeat customer purchase behavior (Cronin and Taylor, 1992;Ladhari et al., 2008), which could increase the market share as well as generate high incomes (Luo and Homburg, 2007).

    To assess the quality of the service is an uneasy task yet challenging since the nature of the service is intangible, heterogeneous, inseparable, and simultaneous (Fitzsimmons et al., 2014). Some researchers have extensively studied how to assess the service quality, see for example Cronin and Taylor (1992), Parasuraman et al. (1988, 1991). However, assessing the quality of the m-LBS is slightly different from the traditional service quality since it uses real-time geographic data from a mobile device or smart phone to provide particular information.

    The development of scales for assessing the m-LBS quality has scarcely been addressed. Fortunately, Heo and Kim (2017) have developed a valid scale which is applicable in evaluating m-LBS quality. (It consists of 9 dimensions and 29 attributes—see Section 2 for the detail.) In this paper, we extend the work of Heo and Kim (2017) by combining their m-LBS quality scale with the importance-performance analysis (IPA) model by Martilla and James (1977). This IPA model is regarded as a simple and effective method to identify attributes that are doing well or to be improved. It can be used to prioritize the service attributes that have to be improved based on the results of the service quality assessment using the m-LBS quality scale. In other words, the output coming from the scale would be the input for the IPA model. Even though the IPA model was introduced more than thirty years ago, it is still popular to be applied nowadays due to its simplicity as well as easy to be used and be interpreted (see for example, Azzopardi and Nash, 2013;Dabestani et al., 2016;Rasyida et al., 2016;Pramono et al., 2017;Ulkhaq et al., 2017;Ulkhaq et al., 2019a, 2019b). In addition, the inclusion of the IPA model in this research, is because every company is constrained by its limited resources. As a result, it has to be decided how those limited resources are best deployed to achieve the customers’ satisfaction.

    To exhibit the applicability of the proposed method, a case study was conducted in one of the biggest motorcycles- based transport services in Indonesia. It employs m-LBS to pick up their customers based on the geographical location and take them to the desired location. In Indonesia, recently, motorcycles-based transport services have grown massively with the valuable market. There are various service providers whereas a plenty of them have not operated anymore due to lack of competitiveness. (It shows the needs for assessing the service quality to achieve customer satisfaction.)


    As service quality is defined as a global judgment or attitude relating to the superiority of service (Parasuraman et al., 1988), its measurement to assess has been widely well-developed, see for example SERVQUAL (Parasuraman et al., 1988) and SERVPERF (Cronin and Taylor, 1992) for “general” services, DINESERV (Stevens et al., 1995) for fine-dining restaurants, LibQUAL+ (Thompson et al., 2000) for research libraries, and LODGSERV (Knutson et al., 1990) for hotels or lodging industries.

    In this globalization era, the Internet has grown so fast to serve millions of users and a huge number of purposes in all parts of the world. As a consequence, the service providers should not only rely on conventional businesses, but also they have to enhance some competitive advantages to interact with their customers by employing electronic service (e-service), i.e., all cues and encounters that take place before, during, and after the electronic transactions. The definition of e-service quality then must be defined differently than the traditional one. Santos (2003) expressed e-service quality as overall customer assessment and judgment of e-service delivery in the virtual marketplace. As such, e-service quality could be explained broadly to encompass all phases of a customer’s interaction with a Web site: the extent to which a Web site facilitates efficient and effective shopping, purchasing, and delivery (Parasuraman et al., 2005). Several studies, therefore, have struggled to describe the domain of eservice quality and thereby offering an assessing scheme. Table 1 shows list of scales in assessing e-service quality.

    The advanced mobile communication technologies have facilitated the development of a variety of mobile commerce applications, including location-based services, mobile reading services, electronic books, mobile television, and mobile music. Previous aforementioned studies listed in Table 2, however, did not cover and clearly explain the aspect of locatability and related personalization. Locatability, which is defined as the capability to recognize the location of customers and surrounding environments (Junglas et al., 2008;Dhar and Varshney, 2011;Petrova and Wang, 2011), is considered as a distinctive characteristic of m-LBS that other m-services do not have.

    Locatability is expected to have a huge impact on the breadth and depth of m-service coverage since it enables the data of current physical locations to become a source of information in m-services. It can be used to deliver relevant, timely, and engaging content and information. For mobile network operators, m-LBS represents an additional stream of revenue that can be generated from their investments in fixed infrastructure. For the end user, it can help reduce confusion, improve the consumption experience, and deliver high-quality service options (Rao and Minakakis, 2003). Considering the increasing growth of the m-LBS market in recent times, a new scale dedicated to m-LBS is timely and needed.

    However, the development of scales for evaluating the m-LBS quality has scarcely been addressed. Some recent studies only focused on mobile location-based information service to measure m-LBS quality (e.g., Junglas et al., 2008;Basiri et al., 2015); while others focused on information status in the online environment rather than on the utilization of information in the real environment (e.g., Chae et al., 2002;Vlachos and Vrechopoulos, 2008). The aspects of security and privacy on location, have to be emphasized in assessing the m-LBS quality since the m-LBSs gather and store the locations of customers in their servers and share their locations with service providers. Although some studies (e.g., Choi et al., 2008;Akter et al., 2013;Su, 2014) included security and privacy on their scales, the aspect of protecting location data was not clearly reflected on those scales. In addition, some aspects related to content are not importantly considered in assessing the m-LBS quality, such as a variety of the content (Vlachos and Vrechopoulos, 2008), the popularity, and entertainment/enjoyment of the content (Su, 2014).

    The scale developed by Heo and Kim (2017) tried to integrate those important aspects that shape the m-LBS in evaluating the m-LBS quality. It consists of 9 quality dimensions and 29 attributes (measurement items). The scale has been shown to be statistically reliable and valid. It is expected to present researchers with solid knowledge on the m-LBS quality scale as well as to provide service providers an understanding of the features of m-LBS quality and easily evaluate their m-LBSs.

    In fact, the scale is considered to fail in evaluating the priority of improving the service quality attributes. The rationale behind the need for prioritizing is because each service provider is constrained by its limited resources. Therefore, it has to be decided how those limited resources are best employed to achieve customer satisfaction. In this paper, we combined Heo and Kim’s m-LBS quality scale with the IPA model (Martilla and James, 1997). The IPA model is considered as a simple and effective method to categorize what attributes are doing well and what attributes are needed to be improved. For the detail of the proposed method, see Section 3.


    The first objective of this research is to investigate the performance of m-LBS quality. In this study, it has been performed by employing the m-LBS quality scale (Heo and Kim, 2017). It consists of nine dimensions, i.e., information quality, localization, function quality, personalization, design quality, reliability, connection quality, interaction quality, and security, with 29 attributes (see Subsection 3.1 for the detail).

    The m-LBS quality is defined as the performance of the service provider that is perceived by the customers for particular attribute multiplied by the importance of corresponding attribute. (It is called “stand-alone” performance measurement.) Although several studies suggest using expectation and perception scales or disconfirmation model (which was originally proposed by Parasuraman et al., 1988), in this paper, we employed the standalone performance measurement (which was originally proposed by Cronin and Taylor, 1992) since it is considered suppressing the disconfirmation model (Cronin and Taylor, 1992;Teas, 1993, 1994). The disconfirmation model, moreover, has been subjected to a number of theoretical and operational criticisms as following.

    • 1. Theoretical criticisms:

    • 2. Operational criticisms:

      • • Expectations: the term expectation is polysemic; consumers use standards other than expectations to evaluate service quality (Iacobucci et al., 1994;Teas, 1993).

      • • Moments of truth (MOT): customers’ assessments of service quality may vary from MOT to MOT (Carman, 1990).

      • • Two administrations: two administrations of the instrument causes boredom and confusion (Bouman and van der Wiele, 1992;Carman, 1990).

      • • Variance extracted: the over disconfirmation score accounts for a disappointing proportion of item variances (Buttle, 1996).

    The second objective of this research is to identify the attributes that must be prioritized to be improved. The IPA model was employed to this research to classify those particular attributes. It was done by plotting the performance and importance data into the two-dimensional state-space diagram. The attributes belong to this category are they which perform poorly but perceived as highly important (see Subsection 3.2 for the detail). After that, the recommendations for improvement could be provided based on the results of the research. Summary of the steps employed in this research is depicted in Figure 1.

    3.1 M-LBS Scale

    The m-LBS scale consists of 9 dimensions with 29 attributes (Heo and Kim, 2017). Those nine dimensions are information quality, localization, function quality, personalization, design quality, reliability, connection quality, interaction quality, and security. Information quality (IF) as the first dimension refers to the extent to which m-LBSs provide accurate and valuable information for the customers (Vlachos and Vrechopoulos, 2008;Lu et al., 2009). Information is such an important thing because it could represent the real condition in the proper period. The accuracy of the information is asserted because the behaviors of customers are influenced by the information they get. This dimension consists of three attributes, i.e., (i) usefulness (IF1): the service provides useful information, (ii) accuracy (IF2): the service provides accurate information, and (iii) details (IF3): the service provides detailed information.

    Localization (LC) refers to the extent to which m- LBS provides the customers with information and functions that are based on their locations. It consists of three attributes, i.e., (i) organization (LC1): the service organizes information provide based on customer’s location, (ii) update (LC2): the service updates information and its organization based on the change in customer’s location, and (iii) inclusiveness (LC3): the service includes enough range of information based on customer’s location.

    Function quality (FQ) refers to the availability of embedded functions to append the utility of information in m-LBS (Vlachos and Vrechopoulos, 2008). It also consists of three attributes, i.e., (i) variety (FQ1): the service provides various functions, (ii) useful function (FQ2): the service provides useful functions, and (iii) explanation (FQ3): the service provides sufficient information to utilize functions.

    Personalization (PS) is a quality dimension by considering the intentions to use, style, and references of customers (Tan and Chou, 2008;Yeh and Li, 2009). It means that the customers can obtain the services based on their needs or requirements. It comprises three attributes, i.e., (i) history (PS1): the service provides information based on customer’s record of service utilization, (ii) interest (PS2): the service provides information based on customer’s interest, and (iii) preference (PS3): the service is provided according to customer’s preference.

    Design quality (DQ) related to user interface, aesthetics, and convenience of using the application of m- LBS. Normally, service providers will provide the visual attractive appearance and easy to use (Tan and Chou, 2008;Su, 2014). This dimension guarantees that customer will not be confused in using the m-LBS application. It consists of five attributes, i.e., (i) aesthetics (DQ1): the service design is visually beautiful, (ii) ease of use (DQ2): the service design is easy to use, (iii) efficiency (DQ3): the service design is efficient to use, (iv) simplicity (DQ4): the service design is simple to use, and (v) intuitiveness (DQ5): the service design is intuitive to use.

    Reliability (RL) refers to guaranteed service for the customer (Özer et al., 2013). The service providers guarantee that their services could give consistent results. There are three attributes for this dimension, i.e., (i) stability (RL1): the service provided is stably without error, (ii) consistency (RL2): the service provides consistent result regardless of trial, (iii) dependability (RL3): the service is dependable.

    Connection quality (CQ) refers to immediately respond to the customer inputs and be consistently provide it without any error (Woo and Fock, 1999;Lim et al., 2006). CQ is considered important dimension since m- LBSs are operated through mobile networks. CQ consists of three attributes, i.e., (i) speed (CQ1): the service is provided with rapid speed, (ii) seamlessness (CQ2): the service is provided without disconnection in the middle of service utilization, and (iii) coverage (CQ3): the service is available anytime and anyplace.

    The next dimension is interaction quality (IQ) which refers to the extent to which m-LBS providers effectively interact with customers and assist them in solving problems related to their use of m-LBSs (Shareef et al., 2014). Given the complexity of m-LBS use, many customers require appropriate cares and support from their providers to effectively use their m-LBSs. This dimension comprises three attributes, i.e., (i) ease of access (IQ1): the customer can easily access the service provider whenever he/she has a problem, (ii) promptness (IQ2): the service is responsive to solving the customer’s problem, and (iii) activeness (IQ3): the service is active in solving the customer’s problem.

    The last dimension is security (SC) which refers to the extent to which m-LBSs protect the personal and location information of the customers (Choi et al, 2008;Huang et al., 2015). This last dimension consists of three attributes, i.e., (i) person (SC1): the customer feel that the service protects his/her private information, (ii) location (SC2): the customer feel that the service protects his/her location information, and (iii) record (SC3): the customer feel that the service protects his/her record of service utilization.

    The service quality of the m-LBS is assessed by multiplying the weights of each attribute with the performance scores as follows:

    S Q j = i = 1 N ( W i j P i j ) N

    where SQj refers to the scores of service quality of attribute j, Wij is the weighting factor of attribute j to an individual i, Pij is the score obtained from the perception of individual i with respect to the performance of the m-LBS on attribute j, and N is the number of the respondents. The weighting factors is the standardized importance score and can be calculated as follows:

    W i j = I i j min i I i j max i I i j min i I i j

    where Iij is the score of the importance of attribute j to an individual i.

    3.2 Importance-Performance Analysis

    IPA is a descriptive analysis technique which was introduced by Martilla and James (1977). It is an analytical technique that is used to identify the performance of the service provider along with its corresponding importance. The importance and performance scores for each attribute are used to create the two-dimensional state space plot: the IPA diagram. The vertical axis illustrates the importance score while the performance score is labeled by the horizontal axis. This plot classifies attributes into four quadrants to set the priorities in allocating limited resources. The quadrants are typically identified as concentrate here (I), keep up the good work (II), low priority (III), and possibly overkill (IV); see Figure 2.

    The first quadrant, i.e., concentrate here, which is located in the north-west corner is the one with the low performance but importantly perceived by the customers, therefore the company should invest more to improve these attributes so the customers will be delighted. In the other words, attributes that fall into this quadrant represent key areas that need to be improved with top priority. The second quadrant is keep up with the good work. It is the one that is considered as important and the customers are fond of the performance of the service. All attributes that fall into this quadrant are the strength and pillar of the organizations, and they should be the pride of the organizations. The third quadrant is low priority. The attributes belong here are performing well but customers perceive them as less important when compared with other attributes; thus, any of the attributes that fall into this quadrant are not important and pose no threat to the organizations. The last or the fourth quadrant is considered less important by the customers and felt too excessive so that need to be reduced due to the excessive investment.


    The purpose of this study is to analyze the performance of the service quality of m-LBS application using the m-LBS quality scale combined with the IPA analysis. The object of the research is one of the biggest motorcycles- based transport services in Indonesia. It is established in 2010 and currently operates in 25 major cities in Indonesia. It now becomes an on-demand mobile platform which provides various services not only transportation but also logistics, e-payment, food delivery, etc.

    In this study, there are two sections of the survey: the first is to assess the performance of the object of the study using the m-LBS quality scale, and the second is to identify the relative importance of each attribute belong to the corresponding dimensions of the m-LBS quality scale. All attributes were measured on a 5-points Likert-type scale, ranging from 1 (strongly disagree for performance-type questions or extremely unimportant for importance-type questions) to 5 (strongly agree for performance-type questions or extremely important for importance-type questions).

    The participants of this survey were required to be over 18 years old and have been experienced in doing transactions with the object of the research. The potential participants were first approached and asked if they agreed to participate in the survey. Note that only completed answers are further analyzed. Two hundred and ten respondents participated in this survey. They consist of students, employees, entrepreneurs, etc., indicating plenty diversity for the purpose of the research. The profile of the respondents is shown in Table 3.

    The reliability test with Cronbach’s alpha (Cronbach, 1951) was conducted to check whether the respondents’ scores on any attribute tends to be related to their scores on the others. It has been proposed that the Cronbach’s alpha can be viewed as the expected correlation of two tests that measure the same construct. By using this definition, it is implicitly assumed that the average correlation of a set of items is an accurate estimate of the average correlation of all items that pertain to a certain construct or dimension (Nunnally and Bernstein, 1994). Suppose that we would like to measure a quantity of a dimension X which can be reflected as a sum of k attributes: X = Y + Y 2 + + Y k 1 , the Cronbach’s alpha is defined as (Cronbach, 1951):

    α = k k 1 ( 1 i = 1 k σ Y i 2 σ X 2 ) ,

    where σ X 2 is the variance of the observed total test scores of each dimension, and σ Y i 2 is the variance of the attribute i for the current sample of persons. The results are shown in Table 4. Note that all of the dimensions have the value of Cronbach’s alpha more than 0.7 (Nunnally and Bernstein, 1994), indicated that the questionnaire being utilized is reliable.

    The average values of importance, weight, performance, as well as service quality for each attribute are computed throughout all respondents. The results are shown in Table 5. In the performance part, the attributes with the highest score for each dimension are: IF1 of information quality, LC1 of localization, FQ2 of function quality, PS3 of personalization, DQ5 of design quality, RL3 of reliability, CQ1 of connection quality, IQ2 of interaction quality, and SC3 of security; whereas information quality has the highest average score of 4.062 among all dimensions. It seems the service gives useful, accurate, and detail information to the customers. For the sake of the customers’ convenience, the application shows the driver’s name and phone number, the location of the driver, the license plate of the motorcycle, estimated arrival time, as well as the amount of the fare to be paid. On the contrary, the dimension that has the lowest average score is connection quality, i.e., 3.148. The lowest scores for each attribute are: IF2, LC2, FQ1, PS1, DQ1, RL1, CQ2, IQ1, and SC2 of information quality, localization, function quality, personalization, design quality, reliability, connection quality, interaction quality, and security respectively. RL1 is considered as the worst among others. It indicates that the m-LBS could not provide stable service without any error; it means that the service provider needs to improve its performance.

    In the importance part, the attributes with the highest score for each dimension are: IF3, LC1, FQ3, PS2, DQ2, RL2, CQ3, IQ2, and SC3 of information quality, localization, function quality, personalization, design quality, reliability, connection quality, interaction quality, and security respectively; whereas information quality has the highest average score of 4.521. It seems that the respondents expect to get useful, accurate, and detail information from the m-LBS. Meanwhile, the attributes with the lowest score for each dimension are: IF2 of information quality, LC2 of localization, FQ2 of function quality, PS3 of personalization, DQ1 of design quality, RL1 of reliability, CQ2 of connection quality, IQ1 of interaction quality, and SC1 of security. LC2 is considered as the least important attribute. It seems that the updated information based on the change of the customer’s location is the least important attribute.

    The overall performance of the object of the research is considered not impressive (2.852 form the maximum score of 5). It implies that the m-LBS has to enhance its service performance because there are still rooms for improvements. The IPA model can be used to establish strategic strategies to attain customer satisfaction based on the performance and the importance of the attributes from the customers’ point of view. The conventional approaches only examined one side of the customer acceptance, usually the performance. However, empirical research has demonstrated that customer satisfaction is a function of both expectations related to certain important attributes and judgments of attribute performance (Parasuraman et al., 1985, 1988, 1991). The IPA then combines both the importance and performance facets on a unique diagram to give a useful insight into the service providers’ performance corresponding with its importance.

    The IPA diagram of the result of the research is shown in Figure 3. The attributes belong to the first quadrant are CQ1, CQ2, CQ3, IQ2, IQ3, FQ1, SC2, SC3, RL2, and PS1. It means that the service provider needs to pay attention to those attributes and invest more to gain satisfactory from the customers. Three statements from connection quality dimension that considered as important which have low performance are speed (CQ1), seamlessness (CQ2), and coverage (CQ3). It means that the service provider has to improve their speed connection, the stability of the connection, and the ability of the application to be used anytime and anywhere. Another dimension that has to be improved is interaction quality which attributes are promptness (IQ2) and activeness (IQ3). It means that the service provider has to be more responsive and active in solving every customers’ complaints. Function quality dimension, especially for attribute of variety (FQ1) needs to be improved so that the service provider could give various services to the customers. The security of the customers’ information is one the most important aspect, thus the service provider has to protect their customers’ personal information, especially on location information and records of the service they have used. Customers perceived that the consistency of the m-LBS is low, whereas it is considered as important. It means that the service provider must improve its performance so that it can give consistent service to the customers by ensuring the information given to the customers is exactly the same for repeated orders. The last dimension that needs to be concerned about is personalization. The service provider is required to provide the information based on the customers’ record.

    The statements belong to the second quadrant are IF1, IF2, IF3, DQ1, DQ2, DQ3, DQ4, DQ5, FQ2, FQ3, PS2, LC1, and LC3. Since the second quadrant shows the attributes that have high performance and high importance values, the m-LBS needs to keep up its good work to maintain the satisfactory level from the customers. The statements belong to the third quadrant are RL1, IQ1, SC1, and LC2. The m-LBS does not need to invest more in those attributes because the customers do not consider them as important. The last, the statements that belong to the fourth quadrant are PS3 and RL3. These attributes need to be reduced due to the excessive investment. The summary of which attribute belongs to which quadrant is summarized in Table 6. Note that the attributes that are typed bold fall into the first quadrant, i.e., concentrate here.


    The study has demonstrated how to assess the m- LBS quality using a combination of m-LBS quality scale and IPA analysis. A case study was managed in one of the most leading m-LBS in Indonesia, in the field of transportation service. This approach, which consists of importance and performance aspect, has been found to provide relatively simple and inexpensive means of doing service quality assessment.

    The result showed that the assessment of the service quality has many potential benefits for the service provider. Identifying customers’ perceptions of the performance of the m-LBS for a particular establishment could allow the service provider to improve marketing efforts to meet customers’ expectation. This includes identifying, prioritizing, and improving areas of the service weaknesses and ensuring that valuable resources are allocated in the most effective areas.

    For the future research, it is suggested to use customer zone of tolerance-based service quality (CZSQ) and CZSQ-based IPA (CZIPA) (Chen, 2014) to assess the service quality based on the competitive zone of tolerance by benchmarking against its competitors, as well as to prioritize the service attributes to be improved. Even though these methods originally were developed in the area of hospitality to evaluate the priority of improving the service quality and to overcome some limitations in the applicability of IPA analysis (Oh, 2001;Matzler et al., 2004;Taplin, 2012), it can be further implemented to assess the service quality of m-LBS with some modifications. However, despite of their advantages, the applications remain limited—see Ulkhaq et al. (2016b, 2018) that were applying these methods to the different service areas.



    Summary of the steps employed in the research.


    IPA diagram.


    IPA diagram of the object of the research.


    Several scales in assessing e-service quality

    Several scales in assessing m-service quality

    1 for virtual products.
    1,2 for physical products.

    Profile of the respondents

    Cronbach’s alpha for each dimension of m-LBS quality scale

    Result of the case study

    Location of the attributes in the IPA diagram


    1. Akter, S. , D’Ambra, J. , and Ray, P. (2013), Development and validation of an instrument to measure user perceived service quality of mHealth, Information & Management, 50(4), 181-195.
    2. Andersson, T. D. (1992), Another model of service quality: A model of causes and effects of service quality tested on a case within the restaurant industry. In: Kunst, P. and Lemmink, J. (eds), Quality Management in Service, van Gorcum, The Netherlands, 41- 58.
    3. Azzopardi, E. and Nash, R. (2013), A critical evaluation of importance-performance analysis, Tourism Management, 35, 222-233.
    4. Babakus, E. and Boller, G. W. (1992), An empirical assessment of the SERVQUAL scale, Journal of Business Research, 24(3), 253-268.
    5. Babakus, E. and Inhofe, M. (1991), The role of expectations and attribute importance in the measurement of service quality, Proceedings of the Summer Educators’ Conference, Chicago, IL, 142-144.
    6. Barnes, S. J. and Vidgen, R. (2002), An integrative approach to the assessment of e-commerce quality, Journal of Electronic Commerce Research, 3, 114- 127.
    7. Basiri, A. , Moore, T. , Hill, C. , and Bhatia, P. (2015), Challenges of location-based services market analysis: current market description. In: Gartner, G. and Huang, H. (eds), Progress in Location-Based Services 2014, Springer, Berlin, 273-282.
    8. Bauer, H. H. , Falk, T. , and Hammerschmidt, M. (2006), eTransQual: A transaction process-based approach for capturing service quality in online shopping, Journal of Business Research, 59(7), 866-875.
    9. Bouman, M. and van der Wiele, T. (1992), Measuring service quality in the car service industry: Building and testing an instrument, International Journal of Service Industry Management, 3(4), 4-16.
    10. Buttle, F. (1996), SERVQUAL: Review, critique, research agenda, European Journal of Marketing, 30(1), 8-32.
    11. Carman, J. M. (1990), Consumer perceptions of service quality: An assessment of the SERVQUAL dimensions, Journal of Retailing, 66(1), 33-55.
    12. Chae, M. , Kim, J. , Kim, H. , and Ryu, H. (2002), Information quality for mobile internet services: A theoretical model with empirical validation, Electronic Markets, 12(1), 38-46.
    13. Chang, S. E. , Hsieh, Y. J. , Lee, T. R. , Liao, C. K. , and Wang, S. T. (2007), A user study on the adoption of location based services. In: Chang, K. C., Wang, W., Chen, L., Ellis, C. A., Hsu, C. H., Tsoi, A. C., Wang, H., Lin, X., Yang, Y., and Xu, J. (eds), Advances in Web and Network Technologies, and Information Management, Springer, Berlin, 276-286.
    14. Chen, K. Y. (2014), Improving importance-performance analysis: The role of the zone of tolerance and competitor performance. The case of Taiwan’s hot spring hotels, Tourism Management, 40, 260-272.
    15. Choi, J. , Seol, H. , Lee, S. , Cho, H. , and Park, Y. (2008), Customer satisfaction factors of mobile commerce in Korea, Internet research, 18(3), 313-335.
    16. Chow, I. H. S. , Lau, V. P. , Lo, T. W. C. , Sha, Z. , and Yun, H. (2007), Service quality in restaurant operations in China: Decision-and experiential-oriented perspectives, International Journal of Hospitality Management, 26(3), 698-710.
    17. Cristobal, E. , Flavián, C. , and Guinaliu, M. (2007), Perceived e-service quality (PeSQ): Measurement validation and effects on consumer satisfaction and Web site loyalty, Managing Service Quality: An International Journal, 17(3), 317-340.
    18. Cronbach, L. J. (1951), Coefficient alpha and the internal structure of tests, Psychometrika, 16, 297-334.
    19. Cronin Jr, J. J. and Taylor, S. A. (1992), Measuring service quality: A reexamination and extension, Journal of Marketing, 56(3), 55-68.
    20. Cronin, J. J. and Taylor, S. A. (1994), SERVPERF versus SERVQUAL: Reconciling performance based and perceptions-minus expectations measurement of service quality, Journal of Marketing, 58(1), 125-131.
    21. Dabestani, R. , Shahin, A. , Saljoughian, M. , and Shirouyehzad, H. (2016), Importance-performance analysis of service quality dimensions for the customer groups segmented by DEA: The case of four star hotels, International Journal of Quality & Reliability Management, 33(2), 160-177.
    22. Dhar, S. and Varshney, U. (2011), Challenges and business models for mobile location-based services and advertising, Communications of the ACM, 54(5), 121-128.
    23. Duri, S. , Cole, A. , Munson, J. , and Christensen, J. (2001), An approach to providing a seamless end-user experience for location-aware applications, Proceedings of the 1st International Workshop on Mobile Commerce, Rome, Italy, 20-25.
    24. Fitzsimmons, J. A. , Fitzsimmons, M. J. , and Bordoloi, S. (2014), Service Management: Operations, Strategy, and Information Technology (8th ed.), McGraw-Hill, New York, NY.
    25. Gilbert, G. R. and Veloutsou, C. (2006), A cross-industry comparison of customer satisfaction, Journal of Services Marketing, 20(5), 298-308.
    26. Grönroos, C. (1993), Toward a third phase in service quality research: Challenges and future directions. In: Swartz, T. A., Bowen, D. E., and Brown, S. W. (eds), Advances in Services Marketing and Management vol. 2, JAI Press, Greenwich, CT, 49-64.
    27. Heo, J. Y. and Kim, K. J. (2017), Development of a scale to measure the quality of mobile location-based services, Service Business, 11, 141-159.
    28. Huang, E. Y. , Lin, S. W. , and Fan, Y. C. (2015), MSQUAL: Mobile service quality measurement, Electronic Commerce Research and Applications, 14(2), 126-142.
    29. Iacobucci, D. , Grayson, K. A. , and Omstrom, A. L. (1994), The calculus of service quality and customer satisfaction: Theoretical and empirical differentiation and integration. In: Swartz, T. A., Bowen, D. E., and Brown, S. W. (eds), Advances in Services Marketing and Management Vol. 3, JAI Press, Greenwich, CT, 1-68.
    30. Junglas, I. , Abraham, C. , and Watson, R. T. (2008), Tasktechnology fit for mobile locatable information systems, Decision Support Systems, 45(4), 1046-1057.
    31. Knutson, B. , Stevens, P. , Wullaert, C. , Patton, M. , and Yokoyama, F. (1990), LODGSERV: A service quality index for the lodging industry, Journal of Hospitality & Tourism Research, 14(2), 277-284.
    32. Ladhari, R. , Brun, I. , and Morales, M. (2008), Determinants of dining satisfaction and post-dining behavioral intentions, International Journal of Hospitality Management, 27(4), 563-573.
    33. Lee, S. G. , Lee, E. B. , and Yang, C. G. (2014), Strategies for ICT product diffusion: The case of the Korean mobile communications market, Service Business, 8, 65-81.
    34. Lim, H. , Widdows, R. , and Park, J. (2006), M-loyalty: Winning strategies for mobile carriers, Journal of consumer Marketing, 23(4), 208-218.
    35. Loiacono, E. T. , Chen, D. Q. , and Goodhue, D. L. (2002), WebQualTM revisited: Predicting the intent to reuse a Website, Proceedings of the 8th Americas Conference on Information System, Dallas, TX, 301-309.
    36. Lu, Y. , Zhang, L. , and Wang, B. (2009), A multidimensional and hierarchical model of mobile service quality, Electronic Commerce Research and Applications, 8(5), 228-240.
    37. Luo, X. and Homburg, C. (2007), Neglected outcomes of customer satisfaction, Journal of Marketing, 71, 133-149.
    38. Mangold, G. W. and Babakus, E. (1991), Service quality: The front-stage perspective vs. the back-stage perspective, Journal of Services Marketing, 5(4), 59-70.
    39. Martilla, J. A. and James, J. C. (1977), Importanceperformance analysis, The Journal of Marketing, 41(1), 77-79.
    40. Matzler, K. , Bailom, F. , Hinterhuber, H. H. , Renzl, B. , and Pichler, J. (2004), The asymmetric relationship between attribute-level performance and overall customer satisfaction: A reconsideration of the importance– performance analysis, Industrial Marketing Management, 33(4), 271-277.
    41. Nunnally, J. C. and Bernstein, I. H. (1994), Psychometric Theory (3rd ed.), McGraw-Hill, New York, NY.
    42. Oh, H. (2001), Revisiting importance–performance analysis, Tourism Management, 22(6), 617-627.
    43. Özer, A. , Argan, M. T. , and Argan, M. (2013), The effect of mobile service quality dimensions on customer satisfaction, Procedia-Social and Behavioral Sciences, 99, 428-438.
    44. Parasuraman, A. , Berry, L. L. , and Zeithaml, V. A. (1991), Refinement and reassessment of the SERVQUAL scale, Journal of Retailing, 67(4), 420-450.
    45. Parasuraman, A. , Zeithaml, V. A. , and Berry, L. L. (1985), A conceptual model of service quality and its implications for future research, Journal of Marketing, 49(4), 41-50.
    46. Parasuraman, A. , Zeithaml, V. A. , and Berry, L. L. (1988), SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality, Journal of Retailing, 64(1), 12-40.
    47. Parasuraman, A. , Zeithaml, V. A. , and Malhotra, A. (2005), E-S-QUAL: A multiple-item scale for assessing electronic service quality, Journal of Service Research, 7(3), 213-233.
    48. Petrova, K. and Wang, B. (2011), Location-based services deployment and demand: A roadmap model, Electronic Commerce Research, 11, 5-29.
    49. Pramono, S. N. W. , Ulkhaq, M. M. , Trianto, R. , Rasyida, D. R. , Setyorini, N. A. , Setiowati, P. R. , and Jauhari, W. A. (2017), Integrating the analytic hierarchy process and importance-performance analysis into ISO 14001 framework for assessing campus sustainability, AIP Conference Proceedings, 1902(1), 020035.
    50. Pura, M. (2005), Linking perceived value and loyalty in location-based mobile services, Managing Service Quality: An International Journal, 15(6), 509-538.
    51. Rao, B. and Minakasis, L. (2003), Evolution of mobile location-based services, Communications of the ACM, 46(12), 61-65.
    52. Rasyida, D. R. , Ulkhaq, M. M. , Setiowati, P. R. , and Setyorini, N. A. (2016), Assessing service quality: A combination of SERVPERF and importance-performance analysis, MATEC Web of Conferences, 68, 06003.
    53. Richard, M. D. and Allaway, A. W. (1993), Service quality attributes and choice behavior, Journal of Service Marketing, 7(1), 59-68.
    54. Santos, J. (2003), E-service quality: A model of virtual service quality dimensions, Managing Service Quality: An International Journal, 13(3), 233-246.
    55. Shareef, M. A. , Dwivedi, Y. K. , Stamati, T. , and Williams, M. D. (2014), SQ mGov: A comprehensive servicequality paradigm for mobile government, Information Systems Management, 31, 126-142.
    56. Sorrell, S. (2014), Mobile context and location services: Market trends and competitive landscape 2014-2019, Juniper Research, Hampshire.
    57. Stevens, P. , Knutson, B. , and Patton, M. (1995), DINESERV: A tool for measuring service quality in restaurants, Cornell Hotel and Restaurant Administration Quarterly, 36(2), 56-60.
    58. Su, Q. (2014), Research of instrument to measure customer perceived service quality of mobile data services, International Journal of Networking and Virtual Organisations, 14(1-2), 176-196.
    59. Tan, F. B. and Chou, J. P. C. (2008), The relationship between mobile service quality, perceived technology compatibility, and users’ perceived playfulness in the context of mobile information and entertainment services, International Journal of Human-Computer Interaction,
    60. Taplin, R. H. (2012), Competitive importance-performance analysis of an Australian wildlife park, Tourism Management, 33(1), 29-37.
    61. Teas, K. R. (1993), Consumer expectations and the measurement of perceived service quality, Journal of Professional Services Marketing, 8(2), 33-54.
    62. Teas, R. K. (1994), Expectations as a comparison standard in measuring service quality: An assessment of a reassessment, The Journal of Marketing, 58(1), 132-139.
    63. Thompson, B. , Cook, C. , and Heath, F. (2000), The Lib- QUAL+ gap measurement model: The bad, the ugly, and the good of gap measurement, Performance Measurement and Metrics, 1(3), 165-178.
    64. Ulkhaq, M. M. , Nartadhi, R. L. , and Akshinta, P. Y. (2016a), Evaluating service quality of Korean restaurants: A fuzzy analytic hierarchy approach, Industrial Engineering & Management Systems, 15(1), 77- 91.
    65. Ulkhaq, M. M. , Prakoso, M. F. A. , Sari, V. K. , and Maduma, L. (2018), Evaluating hospital service quality: An application of CZSQ and CZIPA, International Journal of Innovation, Management and Technology, 9(6), 246-251.
    66. Ulkhaq, M. M. , Putra, B. E. , Arianie, G. P. , Amalia, A. N. , and Pramono, S. N. W. (2016b), Applying CZSQ and CZIPA for assessing service quality of domestic low-cost carriers, Journal of Economic, Business and Management, 4(9), 538-545.
    67. Ulkhaq, M. M. , Rabbani, M. , Rachmania, B. A. , Wibowo, A. T. and Ardi, F. (2019a), Integrating importanceperformance analysis into E-S-QUAL and E-RecSQUAL scales for assessing electronic service quality, IOP Conference Series: Materials Science and Engineering, 598, 012002.
    68. Ulkhaq, M. M. , Widodo, A. K. , Widhiyaningrum Yulianto, M. F. A. and Gracia, M. O. (2019b), An integrated M-S-QUAL and importance-performance analysis approach for assessing service quality of mobile commerce application, AIP Conference Proceedings, 2114(1), 060001.
    69. Ulkhaq, M. M. , Wijayanti, W. R. , Kusumawati, A. , Aulia, F. S. , Wijayanti, R. S. , and Wiganingrum, R. (2017), Combining the eTransQual scale and importanceperformance analysis to assess service quality of online shopping, Proceedings of the 4th International Conference on Industrial Engineering and Applications, Nagoya, Japan, 146-150.
    70. Vlachos, P. A. and Vrechopoulos, A. P. (2008), Determinants of behavioral intentions in the mobile internet services market, Journal of Services Marketing, 22(4), 280-291.
    71. Wang, Y. S. and Liao, Y. W. (2007), The conceptualization and measurement of m-commerce user satisfaction, Computers in Human Behavior, 23(1), 381-398.
    72. Wolfinbarger M. and Gilly, M. C. (2003), eTailQ: Dimensionalizing, measuring and predicting etail quality, Journal of Retailing, 79(3), 183-198.
    73. Woo, K. S. and Fock, H. K. Y. (1999), Customer satisfaction in the Hong Kong mobile phone industry, Service Industries Journal, 19(3), 162-174.
    74. World Bank (2017), Employment in services (% of total employment) (modeled ILO estimate), cited 2017 December 28, Available from:
    75. Wotruba, T. R. and Tyagi, P. K. (1991), Met expectations and turnover in direct selling, Journal of Marketing, 55(3), 24-35.
    76. Yeh, Y. S. and Li, Y. M. (2009), Building trust in mcommerce: Contributions from quality and satisfaction, Online Information Review, 33(6), 1066-1086.
    77. Yoo, B. and Donthu, N. (2001), Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL), Quarterly Journal of Electronic Commerce, 2(1), 31-45.
    78. Zeithaml, V. A. , Parasuraman, A. , and Malhotra, A. (2001), A conceptual framework for understanding e-service quality: Implications for future research and managerial practice, Report No. 00-115, Marketing Science Institute, Cambridge, MA.
    79. Zickuhr, K. (2013), Location-based Services, Pew Research Center, Washington, Available from:
    Do not open for a day Close