Application 3: Annotated Bibliography
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Astructuralanalysisoftheenablersofu-commerceproliferationinadevelopingeconomy.pdf
International Journal of Productivity and Performance Management A structural analysis of the enablers of u-commerce proliferation in a developing economy Mohd. Nishat Faisal, Habibullah Khan,
Article information: To cite this document: Mohd. Nishat Faisal, Habibullah Khan, (2016) "A structural analysis of the enablers of u-commerce proliferation in a developing economy", International Journal of Productivity and Performance Management, Vol. 65 Issue: 7, pp.925-946, https://doi.org/10.1108/IJPPM-10-2014-0162 Permanent link to this document: https://doi.org/10.1108/IJPPM-10-2014-0162
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A structural analysis of the enablers of u-commerce
proliferation in a developing economy
Mohd. Nishat Faisal Department of Management and Marketing,
College of Business and Economics, Qatar University, Doha, Qatar, and Habibullah Khan
Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
Abstract Purpose – U-commerce is an emerging paradigm transcending traditional e-commerce boundaries. The purpose of this paper is to highlight those issues that deserve attention in developing successful u-commerce models. Design/methodology/approach – The interpretive structural model technique is adopted to construct a hierarchical structure, and the impact matrix cross-reference multiplication applied to a classification (MIC-MAC) approach is employed to analyze the effect and dependence among these factors. Findings – The research shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and of strategic importance, while another group consists of those variables that have high dependence and are the resultant actions. Practical implications – Organizations that plans to develop a u-commerce model would be benefited from this study. They can understand the difference between the independent and dependent variables and their mutual relationships. This would help them to prioritize their budget and implement suitable strategies to cater to key variables so as to exploit the benefits of u-commerce. Social implications – Most of the GCC countries have very similar business environment. This research can easily be adapted to other GCC nations thereby saving the duplication of time, efforts and money. Originality/value – This research was conducted in a developing economy in a GCC country which is very fast adopter of new technology. The findings of this study would serve as a guide to the businesses who are migrating to a u-commerce model in future. Keywords Qatar, Interpretive structural model, U-commerce Paper type Research paper
1. Introduction The spread of modern wireless technology has opened new vistas in commerce. The last decade has witnessed many such interventions in almost all sectors, which have caused increase in the customer reach as well as profits. One such intervention is ubiquitous computing (Weiser, 1991) is the integration of information processing in the form of miniature sensors, cheap microchips and wireless networks into everyday objects and activities. The term ubiquitous suggests that small devices will be so pervasive in everyday objects that we will not realize them (Serrano and Botia, 2013) while these International Journal ofProductivity and Performance
Management Vol. 65 No. 7, 2016
pp. 925-946 © Emerald Group Publishing Limited
1741-0401 DOI 10.1108/IJPPM-10-2014-0162
Received 22 October 2014 Revised 6 July 2015
Accepted 7 July 2015
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1741-0401.htm
The authors would like to express their sincere gratitude toward the anonymous reviewer(s) and Editors, John Heap and Dr Thomas F. Burgess for their insightful comments which have significantly improved the quality of the final paper.
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developments would affect all spheres of human activity (Langheinrich, 2010). It is expected that the applications of ubiquitous computing would pervade all spheres of life: smart homes, energy efficiency, road safety, work productivity, health monitoring and assistance, transportation, education, etc. (Aarts and Encarnaçao, 2005; Cook et al., 2009). Ubiquitous computing technologies enable both digital and physical objects to be integrated together for the advancement of existing electronic or mobile commerce into the next phase that is often referred to as ubiquitous (u-) commerce (Shi et al., 2012).
U-commerce can be defined as “the integration of e-commerce, by electronically identifying physical products, m-commerce, by allowing users to shop anywhere and anytime, and ubiquitous computing, by allowing users to shop intelligently and intuitively with the help of a smart environment” (Franco et al., 2011, p. 237). According to Roussos et al. (2003), u-commerce is intimately related to e-commerce and m-commerce, employing the infrastructure and the expertise of both. Junglas and Watson (2003) view u-commerce as a conceptual extension of e-commerce and m-commerce. They also identify four main constructs of u-commerce: ubiquity, uniqueness, universality and unison. U-commerce is expected to open new vistas of services that would not only change the way of access and use of information, rather it would facilitate the emergence of a whole new paradigm of services (Sanchez-Pi and Molina, 2010). Major characteristics of u-commerce are (Galanxhi-Janaqi and Nah, 2004):
• customization of information based on variables such as time, place, preference, and even weather and traffic conditions; and
• pervasiveness of the devices which are always connected to the internet via wireless networks or satellites.
Context is a central key in ubiquitous commerce (Coutaz et al., 2005). This content delivery can be adapted to the unique context of the person, the time, the place, the network and can act in unison in order to support smarter and more intelligent delivery (Russell et al., 2005). Companies can utilize u-commerce for developing effective relationships with their customers and provide them with innovative services (Kim et al., 2009; Sheng et al., 2008). What makes the u-commerce model different from the existing e-commerce models is the context awareness and intelligent applications. The technology has the ability to help the business understand the customer as well as its environment thus assisting business to develop and present the customer with innovative products and services (Wang and Wu, 2014).
Qatar is an oil rich nation with a very fast developing economy. Though the country has a very high per capita income, its economy is very much like an emerging nation. Efforts to diversify the economy and reduce reliance on the energy sector have been only moderately successful. The oil and gas industries still contribute about half of GDP. The recent fall in oil prices did have an impact on the economy. The government is trying to reduce dependence on oil and gas and diversify the economy by positioning Qatar as a logistics and financial hub. Most of the large companies in Qatar are typical manufacturing companies though there is an impetus to develop the knowledge-based economy. These traits characterize it as a developing economy much like any other Asian country. However, the country is considered as an early adopter of new technology solutions. This has led the businesses to implement new solutions to facilitate growth. In this context, u-commerce opens new opportunities for retail, healthcare, travel, education, etc. According to Galanxhi-Janaqi and Nah (2004), one of the key issues to accelerate the growth of u-commerce adoption is to develop a plan for
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u-commerce’s path. This requires an understanding of the variables that impact u-commerce adoption and their contextual relationships. To achieve this, interpretive structural modeling (ISM) is an effective modeling technique to ascertain relationships among the variables that characterize a problem or an issue (Warfield, 1974; Sage, 1977). In this paper, factors that can affect the proliferation of u-commerce are modeled using ISM and then also categorized depending on their driving power and dependence. Based on detailed discussions with experts from retail, healthcare, transportation, IT and academia, several variables were identified. Then a critical analysis was carried out which led to the combining of a few variables and deletion of others as they did not find enough support from the literature. Considering the final list of ten enablers that can affect u-commerce, the major objectives of this paper can be stated as:
• to identify and rank the enablers of u-commerce; and
• to understand the relationships among identified enablers using ISM.
Following this introduction, the remainder of the paper is organized as follows. Section 2 provides a background to u-commerce. Section 4 describes various critical success factors or enablers related to u-commerce. Section 4 describes the ISM methodology and its application to develop a model of enablers of u-commerce. Finally, Section 5 presents the discussion of the results.
2. Background U-commerce, being a new phenomenon in the field of commerce, provides unique advantages to the customers – transaction anytime, anywhere and with anyone (Sabati et al., 2010). This can be perceived as an extension of e-commerce, which is a popular way for doing transactions on the internet (Mannan, 2013), and m-commerce where mobile devices and supporting networks facilitate the transaction (Schwiderski-Groshe and Knopse, 2002). The concept of u-commerce is to provide and extend the electronic mode of operations beyond the conventional personal computers and the other audio visual aids like television to a much further horizon immaterial of location, time and infrastructure. Core factors identified as components of u-commerce have the characteristics of uninterrupted availability with respect to power and battery operated devices, customization, and provides individual identity (Wen and Mahatanankoon, 2004).
The multidimensional aspect of u-commerce facilitate consumers to utilize the technology in a much more personalized fashion in a custom made application formats (Sheng et al., 2008). This addresses both transactional and non-transactional parameters. Providing a significant feature of entertainment in u-commerce applications captures the customer’s attention to use u-commerce in a user-friendly and value-adding approach (Anckar et al., 2003). Balasubramanian et al. (2002) stated that depending on the nature of the work, u-commerce applications are classified into two types: content-delivery-related and transaction-related. The first one is aimed at reporting, notification and consultation and the second one at data entry, promotions, and purchasing. Some researchers argue that u-commerce is a new paradigm that extends e-commerce by integrating wireless, television, voice and silent commerce (Galanxhi-Janaqi and Nah, 2004). A comparison of traditional, e-, m- and u-commerce is provided in Table I.
By the end of 2014 mobile commerce sales in USA was forecast to touch $70 billion dollars and is expected to reach a figure of $173 billion by the end of 2018 (Candrlic, 2014). Juniper Research (2014) was expecting a rise of 40 percent in mobile payments in 2014, which would reach $504 billion. These figures are an indication of the changing
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trends of doing business in which u-commerce is perceived as a succeeding wave to earlier technologies (Russell et al., 2005). However, there are also some studies that present a different picture about the acceptance of u-commerce. Mallat and Tuunainen (2005) expressed that in spite of the potential benefits of u-commerce, the anticipated growth was not there in the market. This was because of the stringent setbacks in the process of embracing the technology. Therefore, identifying the enablers will help to overcome the limitations and develop plans to embrace the new models in a seamless fashion.
3. Enablers of u-commerce The proliferation of u-commerce that would benefit the user and the provider can be improved if the supporting variables and their relationships are delineated. In the proposed ISM, to identify u-commerce enablers, and to establish mutual relationship, brainstorming sessions were conducted with experts. These experts were from academia and industry. In the beginning 17 experts were identified on the basis of their expertise. As Qatar is not a big country it is not possible to find many experts. These experts were first contacted through e-mail and phone to invite them to participate in the research. Four of these experts expressed their inability to
Characteristic Traditional Commerce E-commerce M-commerce U-commerce
Approach to customer
Face to face approach of selling products/ services
Online selling or buying products/ services
Selling or buying using mobile mode of communication
Using online and mobile mode of selling and buying
Device None Desktop or laptops
Mobile devices but primarily, mobile phones
Combination of various devices, including “nontraditional” devices such as everyday objects
Portability Physical location is required to complete trade
Laptops or desktops are normally required. normal portability
Customer’s device can be taken almost anywhere like cell phone
Good portability of devices like cell phone and laptops
Identification of Customer
Personal identification or organizational identification are required to complete transactions
Need to identify to complete transactions
M-commerce service can identify who customer is
U-commerce has both options. Customers can be identifying automatically or can introduce themselves
Payment method
Cash, credit card Digital money Digital money, mobile credit
Digital money
Reachability Customer are not reachable all the time
Customer will be reachable when they are online
Almost all the time customer are reachable
Customers are reachable anytime anywhere
Accessibility Customer cannot contact business at anytime from anywhere
Mostly customer cannot contact business at anytime from anywhere
Customer can contact business at anytime from anywhere
Customer can contact business at anytime from anywhere
Source: Authors
Table I. A comparative analysis of traditional, e-, m- and u-commerce
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participate citing their busy schedule and two were dropped as they had less than ten years of work experience in the industry. Thus, finally out of 11 experts, seven were from industry and four were from the academia. Out of the seven experts from industry, three were working as IT managers, two as CIO’s, one as head of networking, and one as head of IT security. The four experts from academia had research interests in the areas of e-commerce, IT security, privacy, and e-government. The experts from academia had more than ten years of post-PhD research experience, all of them had public Google scholar accounts and they had h-indexes in range of 15-27. This provided us with confidence that these experts were well researched in their domain and the outcome from their discussion could be used with confidence to develop our model.
At an initial meeting, literature related to u-commerce was circulated among the experts. This literature was based on the comprehensive literature review done by the authors. Within a period of two weeks, a brainstorming session was organized to identify the variables. Unfortunately, three more experts from the industry and one from academia dropped out due to some emergency appointments in their organizations. Thus, the workshop/brainstorming session was conducted with seven experts. In all, 14 variables/enablers were identified in this session. The number was reduced to ten as some variables overlapped, for example, interoperability and compatibility. The literature related to these ten variables was circulated among the experts.
A week after the initial meeting, a second session was organized to establish the relationship among the variables. Before this session, the opinions of individual experts were collected regarding the contextual relationships among the variables. Also before the session, the authors compiled the responses and highlighted those relationships where major differences were found. In the second brainstorming session, relationships among all variables were established. In cases of disagreement, the authors took the lead to work out a consensus among the experts. Thus, ten enablers and their contextual relationships were developed in these brainstorming sessions, which were further utilized to develop the ISM model. These enablers are discussed in the paragraphs below with a final summary presented in Table II.
3.1 Security Security considerations are very important for successful u-commerce applications as it might become the bottleneck as in case of e-Commerce development (Gerber and Von Solms, 2001). A single security breach may result in irreparable damage to firms in terms of corporate liability, loss of credibility and reduced revenues (Cavusoglu et al., 2015). U-commerce brings forth the possibility of a vast number of new applications on the internet that would connect devices, systems, services and even smart objects, with a variety of protocols, domains and applications. These changes make would make it difficult to anticipate and quantify the information security risk (Pfleeger and Caputo, 2012). With so many possibilities for using user information, suitable information security training is an imperative to improve users’ awareness that leads to secure behavior (Safaa et al., 2015). Training courses, workshops, formal presentations, internet pages, e-mails, screen savers, posters, pens, games and meetings are among the ways that experts can improve the knowledge of users’ information security (Albrechtsen and Hovden, 2010). Further, security issues like legal security, physical security and managerial security should be take into consideration to increase the whole security (Zhang et al., 2012).
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S. No.
Enablers of u-commerce Supported By Comments
1. Security Teo et al. (2005), Koenig-Lewis et al. (2010), Mattila (2003), Harma and Dubey (2009), Yu (2012), Liou (2008), Ketkar et al. (2012) and Amin (2008)
Security can be considered as a state of being free from any sort of threat or danger. The need of the solutions with the value added features like security and customer data confidentiality for better returns to u-commerce
2. Compatibility/ interoperability
Koenig-Lewis et al. (2010), Mattila (2003), Wu and Wang (2005), Lu and Su (2009), Balaji et al. (2013) and Khraim et al. (2011)
Compatibility is considered as ability of one computer or device or software to work with each other. Compatibility is one of the main concerns while adopting or selecting any new technology like u- commerce. It is stated by the diffusion of innovation model that the degree of novelty in the service or product would be estimated by its compatibility
3. Ease of access Lu and Su (2009), Balaji et al. (2013) and Ketkar et al. (2012)
Ease of access considered as sending and receiving information from different locations, without any problem. It presents the relevant and specific choices to the consumers at the specific location and time in order to make transactions, irrespective of the present location and the location required Ease of access is one of the primary factors for the consumers of u-commerce to adopt these technologies
4. Flexibility of time
Anckar et al. (2003), Fraunholz and Unnithan (2005), Carlsson and Walden (2002), Mattila (2003), Suoranta et al. (2005) and Kim et al. (2010)
Flexibility of time can be understood as variable work schedule. It is against traditional working hours to complete certain task. U-commerce is providing chance to customers to complete their transactions under flextime option. There are no certain working hours to complete these u-commerce transactions
5. Lower transaction cost
Mattila (2003), Suoranta et al. (2005), Ketkar et al. (2012), Harma and Dubey (2009) and Yu (2012)
Cost benefit trend plays a significant role in customer’s perception of utility and usage of technology. Proper understanding of costs can make them realize the benefits of adopting u-commerce
6. Convenience and ease of use
Mattila (2003), Suoranta et al. (2005), Shen et al. (2010), Harma and Dubey (2009), Ketkar et al. (2012), Luo et al. (2010), Gu et al. (2009), Amin (2008) and Kim et al. (2010)
Customer belief in accepting new technology depends on his perceived usefulness. Perceived ease of use not only helps in understanding the thought process of customer as an enabler of u-commerce, but also explains the variation in the user intentions
(continued)
Table II. Enablers of U-commerce
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3.2 Compatibility/interoperability Innovation diffusion model (Rogers, 1995), identifies “compatibility” as a critical factor in consumer adoption decision and defines it as “the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters” (Yang, 2005). Detlor et al. (2013) in their research affirmed that compatibility is the degree to which an innovation is seen to be compatible with existing values, beliefs, experiences and needs of adopters. As u-commerce needs many applications to work cohesively, a relevant aspect for environments with multiple independent systems is interoperability. Interoperability is the ability of two or more systems or components to exchange information and use the information that has been exchanged (Geraci, 1991). It is widely believed that the establishment of interoperability of the information systems of a firm with the ones of other cooperating firms (e.g. customers, suppliers and business partners) can generate significant business value (Loukis and Charalabidis, 2013). According to Jardim-Goncalves et al. (2012), interoperability is a key enabler for unlocking the full potential of organizations, processes and systems enabling seamless cooperation among organizations in all stages of development and production of goods and services, reducing barriers to
S. No.
Enablers of u-commerce Supported By Comments
7. Privacy Koenig-Lewis et al. (2010), Amin (2008) and Efraimidis et al. (2009)
Personal information is always sensitive; it is the responsibility of the service providers to enhance the security capabilities in this emerging technology era. This enhances the trust on the privacy provided
8. Saving time and efforts
Mattila (2003), Suoranta et al. (2005) and Ketkar et al. (2012)
Saving time and efforts can be understood as less time and energy required by individual to complete certain task. U-commerce can help people to complete their transactions in less time as compared to traditional manual transactions
9. Perceived usefulness
Wei et al. (2009), Wen and Mahatanankoon (2004), Koenig-Lewis et al. (2010), Wu and Wang (2005), Lu and Su (2009), Balaji et al. (2013), Yang (2005), Zhou (2011), Yu (2012), Khraim et al. (2011), Luo et al. (2010), Gu et al. (2009), Amin (2008) and Kim et al. (2010)
Perceived usefulness can be understood as a belief of a person to enhance his or her performance, after the use of a certain system. Estimating the perceived usefulness and the interest of the individual to perform online transaction using u-commerce can be a good enabler
10. Technology innovation
Anckar et al. (2003), Carlsson and Walden (2002), Wen and Mahatanankoon (2004) and Zhang et al. (2009)
Technology innovation is considered as finding a better way of doing things with the support of technology. Overall it can be viewed as a technology which can provide better and new solution to meet the requirement. U-commerce is giving technological innovation to the current traditional markets Table II.
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communication and fostering a new networked business culture leading to the growth of u-commerce. Study of Lin (2011), investigating the effect of innovation attributes and knowledge-based trust in online banking, has highlighted the importance of compatibility in a u-commerce system.
3.3 Ease of access Ease of access is the degree to which the consumer believes that accessing the internet through any mobile will be free of any effort and will yield hassle- free transactions (Lu and Su, 2009). People will no longer be constrained by time or place in accessing e-commerce activities. Rather, u-commerce could be accessed in a manner that may eliminate some of the labor of life’s activities (Mahatanankoon et al., 2005). It is one of the primary factors driving the consumers of u-commerce to adopt these technologies (Sharma and Lijuan, 2014). It enables the users to seek location-specific information through global positioning systems technology (Zhang et al., 2010). Delivering personalized information through devices like mobiles empowers the customers’ to adopt a much user-friendly approach in embracing u-commerce (Zhou, 2011). It presents the relevant and specific choices to the consumers at the specific location and time in order to make transactions, irrespective of the present location and the location required (Mahatanankoon et al., 2005).
3.4 Flexibility of time Keen and Mackintosh (2001) highlighted that among all other factors flexibility of time is the most important benefit of the concept of commerce in online transactions. Time flexibility is found to be the most acceptable factor for u-commerce (Teo et al., 2005). Many researchers discussed that with the help of improved methods, this can be a vital factor that influences customers and enables them to adopt u-commerce (Carlsson and Walden, 2002; Gu et al., 2009).
3.5 Lower transaction cost A transaction is a process by which a good or service is transferred across a technologically separable interface and the cost involved with such transaction-related activities represent transaction cost (Chen et al., 2006). McEachern (2000) argued that the transaction costs are the costs of time and information required to carry out market exchange. Transaction costs occur in all steps of a consumer’s purchase decision: need recognition, search, alternative evaluation, purchase and outcome. To acquire products, or resources, customers go through a resource lifecycle that includes several stages, each with associated costs: establishing and specifying requirements, identifying the source, ordering, paying for, acquiring and testing, integrating, updating, monitoring and maintaining, and retiring the product (Chircu and Mahajan, 2006). The major sources of value creation for a firm are obtained by cost reduction on account of efficiencies in the management of transaction costs. Hence the transaction cost is considered as critical in order to understand and interpret the value creation proposition of a u-commerce application (Andoh-Baidoo et al., 2012). Cost benefit plays a significant role in customer’s perception of utility and usage of technology. According to Zhang et al. (2010), the reason for high number of users for SMS and WAP is due to the user-friendly technology which is inexpensive. Chen and Hitt (2002) opines that a proper understanding of costs handling and weighing can make them realize the benefits of adopting u-commerce technology.
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3.6 Convenience and ease of use Perceived ease of use can be described as the degree to which a person believes that using a particular system is free of effort (Saadé and Bahli, 2005). For any emerging IT/IS, perceived ease of use is an important determinant of users’ intention to accept and usage behavior (Venkatesh, 1999; Agarwal and Karahanna, 2000; Henderson and Divett, 2003). Perceived ease of use not only helps in understanding the thought process of customer as an enabler of u-commerce, but also explains the variation in the user intentions (Khraim et al., 2011; Tsiaousis and Giaglis, 2014). It is stated by Kim et al. (2010) that perceived ease of use, being influenced by the innovativeness, influences individual decision making attitude.
3.7 Privacy Privacy is a strategic issue that deserves great attention from both scholars and practitioners because customer information is used in a variety of business processes and can be used in response to competitive pressures (Wang and Wu, 2014). Online privacy concerns among the general public originated with the rise of database systems in the 1980s and the internet in the 1990s (Baek, 2014). On the internet, people’s online activities can be traced, stored, saved and even traded to unknown third parties (Lessig, 2002) thereby making individuals worried about engaging in e-commerce (Belanger et al., 2002). Today, collecting information related to individual customer preferences and choices is a competitive necessity for organizations (Lee et al., 2011). This is due to saturated markets and intense competition thereby forcing the organizations to use consumers’ personal information to develop better marketing strategies (Schwaig et al., 2013). The threat of the accidental or deliberate dissemination and use/reuse of personal information for unauthorized purposes is a critical impediment to u-service development and adoption (Ryan, 2011). Many surveys have revealed that for consumers of mobile or e-services, privacy is a key concern (Miltgen and Smith, 2015). These concerns are more prominent for u-commerce as u-commerce applications are more pervasive and ubiquitous. Based on a study done in Singapore, Yang (2005) opined that privacy is important throughout the world for the success of u-commerce.
3.8 Saving of time and effort Ubiquity of u-commerce facilitates providers to reach their customers anywhere, anytime while consumers can obtain information whenever, and wherever they want (Chong, 2013) thereby saving time and effort of both groups. The characteristics of the customer such as saving time and efforts, zeal toward new technologies and flair for novelty are part of personality construct (Keen and Mackintosh 2001). Consumer approaches differs from person to person. Apart from the above, the socio-economic factors also play a vital role in nurturing such attitudes of the customer that form the base for accepting the technology of u-commerce (Liou, 2008).
3.9 Perceived usefulness Prior research indicates that perceived usefulness is an important indicator for technology acceptance (Bhattacherjee and Premkumar, 2004; Venkatesh and Davis, 2000). Mawhinney and Lederer (1990) state that user satisfaction is strongly related to the perceived usefulness of the technology-based system. Wei et al. (2009) found that perceived usefulness plays an important role in influencing a user’s decision to adopt mobile internet activities and m-commerce. Similarly, consumers would adopt
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u-commerce applications only when they perceive it to be useful as compared to existing e-commerce applications. The technology acceptance model identified the role of perceived usefulness and perceived ease of use as the prime enablers for adopting u-commerce (Anckar et al., 2003; Lee and Chang 2013). Bhattacherjee (2002) added that by estimating the perceived usefulness, the interest of the individual to perform online transaction using u-commerce can be revealed.
3.10 Technology innovation Today’s consumer can be considered an active information seeker and they use the information to adopt new ideas (Lu et al., 2005). Liou (2008) says that in the process of enabling the u-commerce, right from conceiving to completion, every player has a significant role. In particular, the technological development should always accommodate better interface with the customer. Wu and Wang (2005) concluded from their study that on the one hand u-commerce users get awareness of the technological development while on the other, user interface problems are being nullified by the service providing organizations. This is the reason for the rapid spread of u-commerce.
4. Building the ISM model 4.1 ISM ISM as a modeling technique has gained popularity as it provides a digraph model which makes it easier to understand the implicit relationships among various variables. ISM has been applied by a number of researchers in various fields like m-commerce (Khan et al., 2015), green supply chain management (Diabat and Kannan, 2011), supply chain agility (Agarwal et al., 2007), transparency in food supply chain (Faisal, 2015), e-government (Faisal and Rahman, 2008). In ISM, identification of the variables and the type of relationships among them is defined by a group to develop the hierarchical structure (Bolaños et al., 2005).
ISM model allows the managers to prioritize resources of the firm accordingly in managing the issue at hand. Models developed using ISM technique facilitates effective planning, scheduling, monitoring and control, thereby improving the effectiveness of the strategic process (Faisal, 2010). ISM has the strength that it can be either used as group learning process, or individually. Various steps involved in the ISM methodology can be summarized as (Faisal and Al-Esmael, 2014; Joshi et al., 2009):
• Variables that are relevant to the problem or issues are identified by exhaustive literature review, opinion of experts or survey.
• Brainstorming is carried out to arrive at contextual relationships among the variables leading to the development of Structural Self-Interaction Matrix (SSIM).
• Initial and final reachability matrices are developed from the SSIM keeping in view the transitive links. Transitive links are investigated by applying that if a variable X impacts Y and Y impacts Z, then X necessarily has an impact on Z.
• Based on the relationships as deducted in the reachability matrix, directed graph (DIGRAPH) is drawn, and transitive links are removed.
• The resultant digraph is converted into an ISM, by replacing element nodes with statements.
• ISM model is reviewed to check for conceptual inconsistency, and the necessary modifications are made.
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4.2 SSIM For analyzing the enablers of u-commerce, a contextual relationship of the “positive impact” type is considered. The relationship between any two enablers (i and j) and the direction of this relationship is developed for all the variables. This would lead to the development of SSIM. Four symbols as shown in Table III are used to denote the direction of relationship between the enablers (i and j).
Using the above analogies, Table IV depicts the existence and nature of relationships among the ten enablers of u-commerce.
4.3 Reachability matrix The SSIM as shown in Table IV is transformed into a binary matrix, called the initial reachability matrix (Table V), by substituting V, A, X, O by 1 and 0 as per the rules mentioned in Table VI.
Nature of relationship Symbol
i positively impact j V j positively impact i A i and j positively impact each other X i and j are unrelated O
Table III. Nature of
relationship and the symbol
10 9 8 7 6 5 4 3 2
1. Security V O V V O O O V O 2. Compatibility/interoperability A O V O X V X X 3. Ease of access A V V A X V X 4. Flexibility of time A V V A X V 5. Lower transaction cost O O A O A 6. Convenience and ease of use A O V O 7. Privacy O V O 8. Saving of time and effort A V 9. Perceived usefulness A 10. Technology innovation
Table IV. Structural
self-interaction matrix (SSIM)
1 2 3 4 5 6 7 8 9 10
1. Security 1 0 1 0 0 0 1 1 0 1 2. Compatibility/interoperability 0 1 1 1 1 1 0 1 0 0 3. Ease of access 0 1 1 1 1 1 0 1 1 0 4. Flexibility of time 0 1 1 1 1 1 0 1 1 0 5. Lower transaction cost 0 0 0 0 1 0 0 0 0 0 6. Convenience and ease of use 0 1 1 1 1 1 0 1 0 0 7. Privacy 0 0 1 1 0 0 1 0 1 0 8. Saving of time and effort 0 0 0 0 1 0 0 1 1 0 9. Perceived usefulness 0 0 0 0 0 0 0 0 1 0 10. Technology innovation 0 1 1 1 0 1 0 1 1 1
Table V. Initial reachability
matrix
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Next step is to explore the transitive links exist among the variables. Though in Table IV several entries are O, indicating that there exist no direct relationships among these variables and thus the corresponding entries in the initial reachability matrix is 0 in both the column and the row. But in reality when we apply the transitive impact rule several entries might change. For example, in the SSIM (Table IV) there is no direct relationship between enabler 1 and enabler 5, thus in the initial reachability matrix the cell entry ( p15) is 0. But on examining the transitive links in SSIM, it was found that enabler 1 impacts enabler 8 and enabler 8 impacts enabler 5. Hence according to step 4 of the ISM methodology, it can be inferred that enabler 1 has an impact on enabler 5. Thus in final reachability matrix (shown in Table VII) the cell entry ( p15) is 1. Several other entries (marked with an * in Table VII) were similarly changed.
Table VII which is the final reachability matrix also provides the driving power and the dependence of each enabler which are the sum of entries across row and column for each enabler. Driving power indicates the total number of enablers (including self) which an enabler can positively impact. Dependence of an enabler is the total number of enablers (including self) which may be positively impacting it.
4.4 Level partitions Final reachability matrix as shown in Table VII is utilized to develop the reachability set and antecedent set for each enabler. The reachability set can be found by examining the row of the reachability matrix while antecedent set consists of all the elements found in the column of each variable (Warfield, 1974). Further, an intersection of these two sets is also developed. Once this is completed for all the elements, an analysis is done to find out the element for which the entries of the intersection set and the reachability are identical. This element(s) would
(i, j) entry in SSIM (i, j) entry (j, i) entry
V 1 0 A 0 1 X 1 1 O 0 0
Table VI. Rules for transforming SSIM into reachability matrix
1 2 3 4 5 6 7 8 9 10 Driving power
1. Security 1 1* 1 1* 1* 1* 1 1 1* 1 10 2. Compatibility/interoperability 0 1 1 1 1 1 0 1 1* 0 7 3. Ease of access 0 1 1 1 1 1 0 1 1 0 7 4. Flexibility of time 0 1 1 1 1 1 0 1 1 0 7 5. Lower transaction cost 0 0 0 0 1 0 0 0 0 0 1 6. Convenience and ease of use 0 1 1 1 1 1 0 1 1* 0 7 7. Privacy 0 1* 1 1 1* 1* 1 1* 1 0 8 8. Saving of time and effort 0 0 0 0 1 0 0 1 1 0 3 9. Perceived usefulness 0 0 0 0 0 0 0 0 1 0 1
10. Technology innovation 0 1 1 1 1* 1 0 1 1 1 8 Dependence 1 7 7 7 9 7 2 8 9 2 Note: *Indicates a transitive link
Table VII. Final reachability matrix
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be considered as the topmost element(s) in the hierarchy. This element would then be removed from the reachability set and the antecedent set of all the remaining elements. This iterative process is continued till the levels of all the variables under study are identified (Tables VIII and IX).
4.5 Building the ISM-based model From the literature review it is clear that u-commerce proliferation may be affected by a number of variables and thus in place of considering their individual affect it would be helpful if the relationship among these variables are presented in a form of a model. To facilitate this understating ISM emerges as a preferred methodology. ISM is capable of representing implicit relationships in a well-defined structure. A digraph is developed utilizing the entries in Table V, after removal of transitive links the ISM model emerges as shown in Figure 1.
4.6 MIC-MAC analysis MIC-MAC (Matrice d’Impact Croisés – Multiplication Appliqueé à un Classement or Matrix of Cross-Impact – Multiplications Applied to Classification) analysis (Godet, 1986, 1987), is a methodology to classify the enablers into four clusters (Diabat and Kannan, 2011). “Autonomous category” of variables are those that are weak on driver power and dependence. In contrast to these “connecting variables” are strong on both of these dimensions. This indicates that they are influenced by lower level variables and affects the variables higher in the hierarchy. Those variables that exhibit high dependence and very low driving power can be called “dependent enablers.” They can
Enabler pi Reachability set R( pi) Antecedent set A( pi) Intersection set R( pi) ∩ A( pi) Level
1 1,2,3,4,5,6,7,8,9,10 1 1 2 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6 3 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6 4 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6 5 5 1,2,3,4,5,6,7,8,10 5 I 6 2,3,4,5,6,8,9 1,2,3,4,6,7,10 2,3,4,6 7 2,3,4,5,6,7,8,9 1,7 7 8 5,8,9 1,2,3,4,6,7,8,10 8 9 9 1,2,3,4,6,7,8,9,10 9 I 10 2,3,4,5,6,8,9,10 1,10 10
Table VIII. Iteration i
Enabler pi Reachability set R( pi) Antecedent set A( pi) Intersection set R( pi) ∩ A( pi) Level
1 1,2,3,4,6,7,8,10 1 1 V 2 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III 3 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III 4 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III 6 2,3,4,6,8 1,2,3,4,6,7,10 2,3,4,6 III 7 2,3,4,6,7,8 1,7 7 IV 8 8 1,2,3,4,6,7,8,10 8 II 10 2,3,4,6,8,10 1,10 10 IV
Table IX. Iteration ii-iv
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be thought of as the resultant action of all the lower level variables. Lastly, those variables that rank very high on driving power dimension are known as “strategic variables.” A driving power and dependence diagram is constructed using Table V as shown in Figure 2.
Flexibility of Time
Compatibility/ Interoperability
Technology Innovation
Convenience and Ease of use
Security
Privacy
Saving of time and effort
Lower Transaction Cost
Perceived Usefulness
Ease of Access
Figure 1. ISM-based model for the enablers of u-commerce
10 1
9
8 7 IV III
7 2, 3 4, 6
6
5
4
3 I 8 II
2
1 5
1 2 3 4 5 6 7 8 9 10
Dependence
D ri
ve r
P o w
e r
Figure 2. Driver power and dependence diagram
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5. Discussion The driver power-dependence diagram shown in Figure 2 helps to classify various enablers of u-commerce in a developing economy. It is found that none of the enablers have a low driving power and low dependence and thus it can be inferred that all the variables are important and the management need to consider them all if they really want to have a successful u-commerce model. In the next cluster we have variables like privacy, trust, and security. These variables have high driving power and low dependence which indicates their importance in the whole model. These variables are most important for u-commerce to be adopted by the consumers. U-commerce is generally supported by open platforms which are very dynamic and distributed thereby increasing the security concerns of the customers. Also due to concerns of these systems falling prey to security breaches, customers might be apprehensive about the loss of their private data. The model explicitly highlights this concern of the customers as these two variables form the base of the model and any threat of the leakage of and use/reuse of personal information for unauthorized purposes is a critical barrier to u-service development and adoption (Ryan, 2011). Among this cluster security emerges as the enabler with the highest driver power indicating that appropriate enforcement of security protection is vital for wider acceptance of u-commerce systems (Shi et al., 2012). Robust security systems expedite the process of innovation in technology leading organizations to invest in new technologies. Further, u-commerce technology platform has emerged as today’s prominent computing paradigm as a result of advances in related technologies, especially, wireless, mobile and sensor technologies coupled with the dissemination of these technologies in prices affordable by the masses (Cayci et al., 2013).
The second cluster is of connecting variables and consists of variables like flexibility of time, convenience and ease of use, ease of access and compatibility/interoperability. These factors form a connection among the lower and upper level variables in the model. These variables are the ones which are influenced by lower level variables and in turn impact other variables in the model. All of these variables would help in the saving of time and effort by the consumer of u-commerce services.
The last cluster consists of variables such as lower transaction cost and perceived usefulness. These variables have high dependence indicating that they are the resultant actions. U-commerce provides customers the opportunity to be connected seamlessly in context-aware networks, allowing personalized services to be delivered in a timely manner (Kim et al., 2009). This would ultimately result in lower cost and saving of time and effort for the end customer and improvement in the perception of the usefulness of u-commerce services. Though perceived usefulness is critical for the adoption of u-commerce model of business by customers, the model presented in this paper indicates that perceived usefulness cannot be improved independently rather it requires working on other lower level variables which in turn have an impact on this variable.
U-commerce is based on the emerging paradigm of ubiquitous computing which is thought to impact the quality of life in a positive manner and augment the capabilities of humans by providing an integrated framework of computers, humans and objects (Fano and Gershman, 2002). But ubiquitous computing and u-commerce are still in the early stages of development (Martínez-Torres et al., 2015) and thus the ISM model developed in this paper helps to provide an understanding of mutual relationships among the variables. The model delineates those aspects that need attention from the strategists to make u-commerce ventures successful and provide better customer value by improving customer satisfaction and developing sustainable relationships.
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Though there are privacy and security concerns, there is research that motivates the provider of u-commerce as it is expected that in lieu of benefits provided by an organization customers are willing to share their personal information (Wang and Wu, 2014). A classification of people on the basis of privacy concerns categorizes them into three groups: privacy fundamentalists, privacy pragmatists and unconcerned customers. Most customers fall in privacy pragmatists category, those who are likely to assess the potential benefits and privacy risks of providing their information before deciding whether to disclose it (Kobsa, 2007; Angst and Agarwal, 2009). Thus, organizations need to present to the customers the potential benefits of u-commerce and devise suitable strategies to solicit customer data and use it effectively to gain competitive advantage.
By eliminating specific time and position to collect customer information, in future it is expected that u-commerce would provide new opportunities for businesses and would emerge as the key to gather relevant customer information to improve their service (Wang and Wu, 2014). It is hoped that the result of this research may be of benefit to organizations in retail, healthcare, and logistics among others that are intending to migrate to a u-commerce model in future. It will help the managers in three ways:
(1) develop suitable strategies to cater those factors that are most critical for the successful u-commerce venture;
(2) understand interrelationships among the factors to prioritize time and resources for implementing u-commerce applications; and
(3) developing a u-commerce strategy for all the players in the value chain.
6. Limitations and scope for future research Similar to other research, the present study also has several limitations. First, the ISM model’s accuracy is dependent on the decision makers’ knowledge about topic and thus may have an element of bias due to the emerging nature of the issue. Second, the model developed in the study has not been statistically validated. Co-variance-based structural equation modeling (SEM) approaches can be used to test the validity of the model developed through ISM approach. These models can be tested using AMOS or LISREL software to further examine the relationships. In case it is not possible to collect a large amount of data as required by the co-variance-based SEM, partial least squares-based model using the software PLF-Graph can be applied to test the validity of the model derived through ISM. Future work may consider developing a similar model for other GCC countries that have a very similar economic environment to Qatar. Furthermore, ISM does not provide any quantitative information about the linked variables. Thus, in future work a graph theoretic approach can be applied to develop a quantitative value for the system.
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Corresponding author Mohd. Nishat Faisal can be contacted at: [email protected]
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