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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.647-657
DOI : https://doi.org/10.7232/iems.2019.18.4.647

Explanatory Modelling of Factors Influencing Adoption of Smartphone Shopping Application

Ezekiel Bernardo*, Jazmin Tangsoc
Faculty of Gokongwei College of Engineering, Industrial Engineering Department, De La Salle University, Manila, Philippines
Faculty of Gokongwei College of Engineering, Industrial Engineering Department, De La Salle University, Manila, Phillippines
Corresponding Author, E-mail: ezekiel.bernardo@dlsu.edu.ph
May 7, 2019 September 12, 2019 October 18, 2019

ABSTRACT


Smartphone shopping applications are in the spotlight in the current Philippine setting of m-commerce adoption. This momentum flourished over the last decade as its capabilities, accessibility, and functionality became greatly detrimental to the present way of living. However, even with the shift, very few studies have been conducted that aims to explain the rise in the adoption of m-commerce specifically smartphone shopping applications. Qualitative claims are abundant but no quantified measures have been established which leaves a gap in the understanding of the growth. Responding to that, this study proposes a model that aims to explore the factors influencing the adoption of smartphone shopping applications that can be used by the industry. Specifically, factors under the UTAUT2 model with trust construct in terms of security, privacy, and information quality under the condition of behavioral intention and use behavior. Data from Filipino consumers were gathered using online questionnaires and were analyzed using structural equation modeling. The result shows that the proposed model captured 0.643 and 0.712 explaining power for intention and behavior respectively while 0.557 for trust construct which confirms the robustness of the novelty model. The study reveals that perceived trust in the system and having references for the use of commerce are the most important factor for intention and behavior. Moreover, the behavior is also catalyzed by social influence, hedonic motivation and perceived productiveness when using the application. More so, the effort needed to use the application and monetary cost needed to acquire such applications are not relevant to the growth.



초록


    1. INTRODUCTION

    In the modern age of Information and Communication Technology (ICT) and Information System (IS), acceptance and usage of recently developed technologies have been a common practice. This behavior flourished over the last two decades as smartphone devices brought a deep impact on human daily life. People are progressively willing to adopt new technology in their daily lives making technology, more than ever before, become a part of peoples’ everyday interaction (Islam et al., 2013). Globally, the adoption rate of smartphone devices grows quickly and the Philippines is considered to be one of the fastest-growing smartphone markets in Southeast Asia to date with a 20% year-over-year (YoY) growth (Dominguez, 2016)

    With the growing momentum of smartphone adoption and usage which started in Q4 of 2013, Mobile commerce (M-commerce) has recently been on the spotlight in the consumer’s eye given its capabilities, accessibility, and functionality (Groß, 2015). This also includes the development of facilities associated with it (e.g., the introduction of mobile internet service, penetration of broadband internet, etc.). Mcommerce is the subset of Electronic commerce (ecommerce) which includes all e-commerce transactions carried out using a smartphone device (Hung et al., 2012). Popularly, this involves banking and financial activities, location- based services, and sales and advertising (Antovski and Gusev, 2008). Among these, sales services or mobile shopping, specifically mobile shopping, has been on the topmost thriving force and became a critical component of the new digital economy. As recent studies show, 60% of the population in the Philippines are exhausting m-commerce shopping which is a 40% increase from the year 2014 and is expected to follow the trend until the year 2021. Accordingly, numerous qualitative claims that the main reason for this is due to the productivity capabilities, ease of use, and costsaving reasons. Truly, the retail industry has recognized the potential of rising smartphone adoption as a means to interact closely with the customers (Groß, 2015).

    In general, mobile shopping stands for an autonomous channel for shopping and purchasing online via smartphones (Gao et al., 2015). This is a ubiquitous service that calls for a multifaceted understanding due to its wide-ranging context of use (Martin, 2013) which allows its users to conduct shopping activities – browsing, evaluating, and purchasing. – anytime and anywhere as long as the necessary facilities (i.e., smartphones, applications, instructions, and internet access) are available (Hung et al., 2012). Conventionally, mobile shopping can be accessed first using web browsers of any computing devices navigating through the webpage of the retailer with limited functionalities due to restrictions of the browser (Groß, 2015). As smartphone adoption increase, this paved the way to companies taking advantage of it and gradually investing in creating their own smartphone shopping applications as it can further enhance the mobile shopping experience to a more interactive, informative, reliable, and aesthetically pleasing (Magrath and McCormick, 2013) making it more preferred as a means for consumers as it is perceived to be more convenient, faster, and easier to browse. Evidently, this scenario is proven as the smartphone shopping application usage is at a higher pace as compared to web-based shopping means (Bensinger, 2016).

    Overall, smartphone shopping applications transformed traditional consumer experience and became a popular way for modern consumers to conduct shopping activities (Chong, 2013). Though numerous studies claim the increase distrust issue as compared to physical retailing (Kim et al., 2008), the factor of this can be limited if proper security, information handling, and privacy protection can be done (Corbitt et al., 2003).

    2. CONCEPTUAL BACKGROUND

    2.1 Research Stream

    The considerable development experienced by smartphone shopping applications and its impact on mcommerce explains the overall interest in the researches in this sector (Abrahão et al., 2016). Explanatory researches that focus on behavioral intention and use behavior is the common path where multiple theoretical models were produced. This research stream focuses on the drivers that propel smartphone application acceptance. Basically, drivers are the term used for the factors that catalyze the adoption (Groß, 2016). Examples of which are usefulness, fitness, facilitating condition, ease of use, and the like. These are motivators which can be both intrinsic and extrinsic in nature that affects user’s decision to use and adapt (Groß, 2015). Moreover, this is further subdivided into a single analysis and multi analysis. The single analysis focuses on a single driver that can motivate a user (Venkatesh et al., 2003). This can be solely focusing on the usability, user experience, or social influence drivers based relations (Gefen et al., 2003). As for the multi analysis, the acceptance model is constructed and simultaneous analysis of the explanatory factors for adoption is done (Venkatesh et al., 2012). In this, adoption models are produced to see and verify whether the unification of different factors can produce another perspective on the study – possibly different effects. Researches use previous theoretical models that were formulated with accordance to other ICT adoption such as reasoned action (Fishbein and Ajzen, 1977), planned behavior (Taylor and Todd, 1995), diffusion theory (Moore and Benbasat, 1991), and socio-cognitive theories (Compeau and Higgins, 1995). Accordingly, the application of such a model in the smartphone field has been the tide in the research stream given the known capabilities of the models. Though studies achieve a better outcome as compared to the single analysis and produced more real-life adoption pictures of the consumers (Venkatesh et al., 2003), the possibility of impediments in the appropriateness is still on hand given that these models are formulated in the employee-toorganization context. With that, the formulation of a unified acceptance model has started in different mcommerce disciplines recently such as Unified Acceptance and Use of Information Technology (UTAUT2) by Venkatesh et al. (2012) (i.e., merging socio-cognitive and reasoned action theories). This model was formulated to provide a more dynamic approach to the analysis and to create a better explanatory theoretical model.

    Relating back to the recent shift and acceptance of smartphone shopping application in the Philippines, few studies have been conducted that aimed to explore the factors playing to the growth as a response from the qualitative claims. Specifically, extensive analysis shows that exploration of the trust claims to be a major barrier for mobile shopping adoption yet to be considered. Understanding these gaps can give several implications to all stakeholders (i.e., online retailers, developers, managers, and consumer public) involved in this consumerism shift. Clearly, it is considered important to continue investigating which factors are significant in fostering users’ intent to use due to the evident increase in the smartphone shopping application count (Dominguez, 2016) and usage of smartphone shopping applications.

    This gap can be resolved with the application of new researches that devises a model that focused on consumers’ context. Moreover, a model with suitable factors, and capability to be used in a smartphone application adoption and can be augmented to feature perceived trust.

    For this study, the application of the recently developed UTAUT2 by Venkatesh et al. (2012) is identified to solve the issues given that it is the most rigid and robust model produced for ICT adoption in consumer settings (Chang, 2012). This model was derived by unifying the idea of the well-tested ICT adoption researches which came up with seven constructs namely performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit (Venkatesh et al., 2012). These factors will be used but operationalized under the smartphone application condition. Moreover, given that it supports augmentation of the model to increase the model fit, this study will also induce perceived trust to give significance to the major factors known in the qualitative research that affect smartphone shopping adoption. Inconsistency with the lapses found, this will be the primary theoretical method to be used and the idea to be followed.

    2.2 Objectives and Contribution

    From the previous discussion, clearly, additional research on the adoption of smartphone shopping application can be done to fully understand and explain the impediments to its acceptance. Thus, this study will stand on the following objectives:

    • O1: To study the constructs affecting the behavioral intention and use behavior towards Smartphone Shopping Applications of UTAUT2 constructs.

    • O2: To assess the impact of “perceived trust” on the behavioral intention in the adoption of Smartphone Shopping Applications for Shopping purposes.

    In short, the main contribution of this study is to formulate a novelty theoretical model that will explain smartphone shopping application behavioral intention and use behavior that will extend the basic variables of the UTAUT2 model with consideration of the perceived trust. Figure 1 represents the model proposed in the study named Shopping Application Adoption Model (SAAM).

    2.3 Research Model

    The shown model will be used for the study. Constructs under blue and red color are contextually carried from UTAUT2 while green is the new construct contributed to the model. Table 1, summarizes the operational definition that will be carried for the study.

    Perceived trust is the construct that is introduced to SAAM. Trust is an important factor when risk and uncertainty are perceived and takes place in interaction at a social context (Luhmann, 1979). This is the confidence placed in a system where reliability and truth can be perceived (Gefen, 2003) and has been the topic of various works of literature including economics, management and organization, technology, social and institutional contexts, and behavior and psychology (Kim et al., 2008). Among all of these, technology trust has been in the spotlight given the rapid advancement in this field (Joubert and Van Belle, 2013). As such, the introduction of new technology challenges the current idea of trust as it is more exposed to new vulnerabilities and risks (Corbitt et al., 2003).

    Different studies in the context of e-commerce have attributed various definitions to the concept of trust (Gefen et al., 2003). Corbitt et al. (2003) claimed that trust is a vital factor in consumer activities and thereby on its success. It is largely responsible for creating initial consumer activity (Ganesan, 1994) as it is directly dictated the behavioral intention of using it (Jarvenpaa et al., 2000). Gefen (2000) defined trust as the general belief in a seller that results in the behavioral intention which is supported by Pavlou and Fygenson (2006) which extended the definition to the overall belief in which the consumers are in good faith to the sellers after learning of their characteristics. This is also been defined as the subjective belief held by the consumer that the seller will behave non-transitively as expected in the contextdependent transaction (Dasgupta, 1988). In a more general way, trust is the belief that other people involved in the transaction will react in a predictable way (Luhmann, 1979).

    Relating the definitions from previous studies, in a common context, trust is definitely an important factor for any traditional commerce activities (Jarvenpaa et al., 2000). It is essential that customers have trust or confidence in anything involved, most especially the seller, in the overall commerce transaction (Gefen, 2000). In the context of M-commerce through smartphone shopping applications, trust is even more important and necessary to be developed (Gefen, 2002). Generally, smartphone shopping applications replace the traditional physical purchasing of retails from an outlet store. As this happens, the extent of interaction between the seller and consumer is limited in an application, thus, having chances of developing different meanings of perceived trust signals (Jarvenpaa et al., 2000). As the traditional avenue in building trusts such as face-to-face, voice conversation, emails, and personal messaging is removed from the picture, signals can be perceived to be confusing or even false since everything is considered to be of asymmetric information basis (Kim et al., 2008). Also, since most of the smartphone shopping applications stores personal information in a cloud system, a higher degree of risk of privacy and security is observed (Chong, 2013). The personal concern is greater since the multi-directional conflict on data security is possible (Li et al., 2012). For this reason, trust is now centered on the process by which smartphone shopping application carried out the commerce task as compared to the other traditional means since the higher risk is perceived (Kim et al., 2005). Moreover, it can be said that trust is a relevant factor in any interaction in the field of smartphone shopping application as it is critical that consumers have trust or confidence in the avenue of selling and the seller itself (Gefen, 2003).

    From the clear recognition of the importance of trust established from the previous researches for smartphone shopping applications, multiple studies determined and recommended that trust is really a significant antecedent of the behavioral intention of usage. In the prominent explanatory models, trust has been identified to be a substantial explanatory construct in different acceptance model. The study conducted by Luarn and Lin (2005) proved that trust is a significant indicator of adoption using TAM using risk towards security and privacy in ecommerce payments. The study showed that it is the prime indicator as compared to the original constructs of perceived usefulness and ease of technology use. Wei et al. (2009) explored an empirical analysis of the Malaysian adoption of m-commerce data systems and determined that trust is an important indicator of adoption. Chong (2013) identified that trust is an important determinant for m-commerce adoption intention using a twostaged SEM-neural network. This is the same with Vasileiadis (2014) wherein trust valued due to security concerns in m-commerce payments and legislation is a major determinant for adopting. Generally, other studies confirmed the importance of trust in an e-commerce context (i.e., Chang, 2012;Baptista and Oliveira, 2015;Groß, 2015;Miltgen et al., 2013;Escobar-Rodríguez, and Carvajal-Trujillob, 2014;Oliveira et al., 2016;Wu and Wang, 2005).

    Although numerous studies suggest the intrinsic significance of trust for behavioral intention usage in the field of e-commerce, there are still no studies that value trust as the antecedent for the behavioral intention for a smartphone shopping applications. A necessary study is needed to fill in the research gap since the other mcommerce studies cannot be conclusive given that smartphone shopping applications render multiple facets of m-commerce (Chong, 2013) (i.e., mobile payments, information data systems, and risk and security). Previous studies contextualized trust based on their m-commerce type only making a disjoint conclusion since inappropriate scoping. Incorporating all of the facets may produce a different level of significance (Campbell and Fiske, 1959) and different contribution towards explanation capabilities (Brown, 2015). Also, there is no study for smartphone shopping applications that viewed trust recognizing security, privacy and information quality as risk contributors and only valuing trust as a general concept (Antovski and Gusev, 2008).

    From the identified significant results, recommendations, and lapses, perceived trust is incorporated in this study. Trust will focus on the consumers and the technology inherent in the smartphone shopping applications’ information quality, security, and privacy (Kim et al., 2008). Adapting the definition, this is operationalized as the overall reliability in presented information, trustworthiness in the transactional security, and integrity in storing private and confidential information of the smartphone shopping application providers for the consumers (Groß, 2015). Hence, it is hypothesized that perceived trust in the use of smartphone shopping applications affects the consumers’ behavioral intention and added to complete the picture of smartphone shopping application adoption.

    3. METHODOLOGY

    3.1 Measurements

    To verify the hypothesis that was proposed for this study, a set of measurements was formulated that adapts previous pieces of literature in ICT acceptance, smartphone applications, M-Commerce, and UTAUT2 model. This constitutes the survey that was operationalized for the information demand to spearhead the statistical test in the study. From the 13 constructs, 51 questions were devised which were drafted in English as it is considered as the domain language in the smartphone shopping applications. These questions were rated with a multiitem scale as a means of quantifying the constructs considered (Churchill and Iacobucci, 2002). The responses in each of the questions were measured using a seven-point Likert scale as recommended by Venkatesh et al. (2012) following the response anchors of the level of agreement developed by Vagias (2006).

    3.2 Data Collection

    The data of Filipino subjects is collected through an internet-mediated self-administered questionnaire under the platform of Google Forms. The total number of questionnaires accomplished was 326; of these, the total number of users for the data analysis was 287 (88% of the total) after eliminating those that had not been completed correctly. Though far greater than the needed sample size of 267 as computed from the power analysis, a deduction will not be done since it is expected that the data will be further mitigated in the later statistical analysis.

    Moreover, the characteristics of usable data statistically match the population with at most 1.92% deviation from the quota established. The data supports the proportions in which there are more females (53.31%) as compared to males (46.69%). For the age group, the largest proportion are those with an age of 25 up to 34 (31.36%), followed by 16 to 24 (30.66%), those aged between 35 to 44 (20.91%), next is 45 to 54 (11.15%), and those whose age are more than 55 (5.92).

    4. RESULTS AND DISCUSSION

    4.1 Data Results

    The analysis was done in a two-stage methodology following Anderson and Gerbing’s (1988) guidelines where in the first stage is the measurement model evaluation based the consistency and reliability of the indicators by exploratory and confirmatory factor analysis and followed by the development of the full structural model for hypothesis testing using explanatory modeling. These steps are presented in the following section.

    The exploratory and confirmatory analysis shows that the model produced is robust and no outliers are present. All statistical values for such a claim belong to the acceptance region. In summary, 13 constructs were established and verified; Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Habit (HT), Perceived Trust (PT), Information Quality (IQ), Perceived Security (PS), Perceived Privacy (PP), Behavioral Intention (BI), and Use Behavior (UB) was identified to be relevant to be analyzed under SEM in which will be established under the hypothesis formulated for the study.

    The analysis of hypotheses and constructs relationships was based on the examination of the standardized paths' significance levels based on a bootstrap resampling method. 500 resampling iterations were done to ensure the stability in the generalization of results. The results of the PLS-SEM bootstrap are represented in Table 2 indicates the relationship between the formulated hypotheses. Moreover, this was tested again for 50 iterations for the validity of the results wherein the same results were derived. All in all, the coefficient of determination or RSquared Value (RSV) of each construct is above the threshold value of 0.10 signifying satisfactory explanatory capability.

    As for modification, the additional path was recommended for the hedonic motivation to information quality. However, the model was not modified given that the overall synthesis of the model has an acceptable covariance, variances, and regression weights to the explanatory construct for behavioral intention and use behavior.

    4.2 Discussion and Analysis

    4.2.1 SAAM (Base Model)

    The model explains 64.3% of the variation in behavioral intention to adopt smartphone shopping applications. From the results, hypotheses related to the behavioral intention, in order of importance are: Perceived trust (H9a); Habit (H7a); Facilitating conditions (H4a); – all are confirmed with significance of p < 0.001 – Performance expectancy (H1); Social Influence (H3); Effort expectancy (H2); and Hedonic Motivation (H5) – confirmed with of p < 0.01 significance. Thus, the adoption of smartphone shopping applications depends on the perceived trust towards in the shopping activity; the habit of using the application; the facilitating conditions available; the performance benefits in the actual usage; the effort required in completing shopping activities; and the hedonic experiences that the users enjoy when using the application. In the opposite situation, the hypothesis for the price value to behavioral intention (H6) is not statistically significant which shows that trade-off benefits from the stipulated cost of using to the perceived benefits are irrelevant.

    The results also highlight 71.2% of the variation for use behavior wherein primarily dictated, in order of importance, by Behavioral intention (H8a); Habit (H7b); and Facilitating conditions (H4b) – all of which are confirmed with the significance of p < 0.001. This means that the actual use of smartphone shopping applications rest on the intention of using it; the user’s habit in using the application; and the availability of the facilitating conditions for the use.

    4.2.2 Perceived Trust (Added Construct)

    The additional construct for the augmented model, perceived trust, realized 55.7% of variation which is supported, in order of importance, by all of the hypotheses proposed an antecedent of it – Perceived security (H9c), Information quality (H9b), and Perceived privacy (H9d), wherein all determined to be significant with p < 0.001. Thus perceived trust depends on the security measures that users perceive; the quality of the information provided in the application; and the privacy protection provided by the application.

    Perceived trust is the strongest explanatory construct for behavioral intention to use smartphone shopping applications for shopping purposes. This result is consistent with most of the studies in m-commerce that incorporated trust in their explanatory model. However, the level of significance in explanation varies and is only consistent with m-commerce affiliated in banking and other payment services (Wei et al., 2009). Logically, the coinciding results with banking and payment commerce can be justified as the criticality of money is on hand. Also, intuition towards security and privacy can also be attributed thus making coinciding results towards m-commerce of banking. Moreover, this can also be attributed to the element of disposition in terms of consumers not trusting transactions that do not have face-to-face interactions (Chong, 2013).

    As the SEM result shows, as the user perceives a high level of trust towards the application the more likely the consumer to open the application and do shopping activities. Looking on the identified antecedents (i.e., information quality, perceived security, and perceived privacy), the level of perceived trust can be built as greater information quality is presented in the application for the retailed product and services, better perception of the security measures installed, and the appreciation of the procedures implemented for the overall privacy protection of the consumers’ information. The general level of trust of the consumer towards the application is at high grounds as these antecedents are perceived to be elevated as compared to consumers’ acceptance level.

    Looking at each specific, developers should focus on the idea of perceived trust in order to achieve a better adoption rate. It is suggested that smartphone shopping application developers and planners should prioritize building strategies towards creating and maintaining consumer trust in the shopping activities that they do. This can be developed by focusing on the three significant antecedents of trust in this study which is (1) information quality, (2) perceived security, and (3) perceived privacy.

    • (1) Information quality can be improved by targeting the factor loadings of each indicator. Having accurate and up-to-date information (e.g., product and services information, availability, conditions, transaction process, etc.). Developers and company software handlers should focus on uploading accurate and complete information that is valuable for the consumer’s decision (e.g., price, dimension, rate, etc.) since this can be a major criterion for them. Moreover, as the application cannot give accurate and reliable information, consumers might develop distrust towards the application and further evaluating it to be incapable, unreliable, and illegitimate towards complying on the prompted shopping activity. Thus, the information given on the application should be clear and reliable, adequate in terms of contents depth, accurate, and timely in order to help in the shopping process.

    • (2) The developers can also look at the consumers’ perception of the security measures of the application. As the results show, the higher the perception of the consumers on the security features, the more likely that they will use the application. This means developers can increase the awareness of the consumers by imposing security features along with the access of the application. This can be beneficial in two ways as it first increases the awareness of consumers on the security features deployed by the application and also it can help the developers to maintain the application system for the possibility of impediments of hackers. This can be in the form of encryption on the access information and authentication of users. Also, to alleviate the awareness, developers can design to market out security features such as safe-shopping, verified access, and alike. For example, digital authentication for user identification can be used for verification of the user. This can be deployed as a preventive security measure for irregular access of the application on an unknown device by prompting notifications in the access. As verification is completed, safe-shopping feedback can be given to the user to give a better perception of the security system of the application. This can also be on the payment part of the shopping process as security measures are asked for verifying the credit card information and other payment details. Developers can partner out data to banks to have an authentication process that is prompted to the user as information is inputted to the system and penultimately confirmed to the servers. Having this can create a perception of security towards access to the application. As tested by Lee et al. (2015), such recommendations will work and viably increase perceptions by the users. Also, consistent feedback and confirmation towards the transaction (e.g., the addition of items in the shopping cart, confirmation for the payment information, and alike) can boost the security perception of the users in the whole shopping process.

    • (3) Intensifying privacy measures can also contribute to the overall perception of trust in the application. Basically, this is protecting all of the private information stored in the system captured in the shopping activities that are perceived by the user. Ideally, consumers need to be sure that the application will protect and preserve their privacy by deploying policies and assurance that the data will solely be used in the system only. This can be done by stipulating agreements in the process of making user accounts and when the majority of the private information is needed (e.g., terms and conditions in the use of private information). Also, certifications from accredited thirdparties can be implemented to give assurances to the user with regards to the privacy measures towards selling and unpermitted use of personal information stored in the system.

    5. CONCLUSION

    Smartphone shopping applications are receiving growing attention in the current Philippine setting of mcommerce adoption. However, there have been very few milestones in the research stream that can explain this behavior in a proper consumer context especially regarding the security, privacy, and information quality, as a response to the qualitative researches that explains the increase in adoption of smartphone shopping applications. With that, this study proposes an extended UTAUT2 model that constitutes perceived trust and cost-saving orientation to predict the behavioral intention and actual usage of the current consumer pool. To explain the significance of new constructs, questionnaires were deployed and analyzed using SEM capturing the current demographic pool of Filipino consumers. Answering the objective of the study, the results revealed that the proposed model (UTAUT2-T) has good explanatory power, confirming its robustness for an explanation. Looking at the specifics, the introduced factor of perceived trust is the strongest explanatory construct for behavioral intention. Supported by the antecedents of it, it shows that the primary contributing factor to explain the impact of it towards the adoption is greatly dependent on the perceived security, information quality, and perceived privacy respectively. Developers should focus on those antecedents to be able to capture a higher adoption rate. Additionally, original UTAUT2 factors of habit, facilitating conditions social influence, hedonic motivation, and performance expectancy were also significant in their respective paths. This study also reveals that effort expectancy and price value are not significant contributors to an explanation. Overall, the various managerial recommendation is directed on the strongest explanatory construct and theoretical implications are suggested to expand the current limitation of the study.

    6. LIMITATIONS AND RECOMMENDATIONS

    6.1 Limitations

    This study observed multiple limitations. First, the data was only obtained in the context of Filipino consumers. This limits the possible significance and contributions of each constructs’ findings. Second, the base model for this study is UTAUT2 which is later augmented to fit in the smartphone shopping context. With that, mediating factors are not introduced in the study. Also, the study was conducted on the initial rise of the smartphone shopping application adoption in the Philippines and still far from the saturation stage.

    6.2 Recommendations

    Given the limitations, it is recommended that future studies can look at the other possible constructs that can affect behavioral intention and actual use. Constructs that can constitute the arguments regarding user level of experience towards smartphone, technology self-efficacy, consumer innovativeness for new technology, and another emotional state. Previous studies have incorporated these factors in other adoption models and show significant results that can further strengthen the current model in this study given that various implications involve such ideas. Another possible path for future studies is the usage of these constructs as a mediating demographical features to support the possible cross-cultural differences that can create different significance for an explanation. This can show specific explanation analysis on each category (e.g., gender, age-range, experience, etc). It is also recommended to view the factors related to price and cost to further explain the insignificance of a price value in the study and the immense significance of cost-saving since no previous studies have addressed that situation. Also, the possibility of the longitudinal approach of the study can be done in order to determine the possible theoretical and managerial implications in the different stages of smartphone application adoption. This has been a significant point of study in previous studies and shows the varying results.

    ACKNOWLEDGEMENTS

    I am grateful to the online retailers that helped in providing guidance and recommendation in data capturing and analysis of the data as well as to numerous smartphone shopping application users that participated in the study. I would also like to thank the Department of Science and Technology Engineering- Research and Development for Technology for the grant given to me to pursue such a study. Lastly, I am thankful to Jazmin Tangsoc and Angelica Olan for continuous guidance in order to improve this study.

    Figure

    IEMS-18-4-647_F1.gif

    Shopping application adoption model (SAAM).

    Table

    The operational definition of SAAM constructs

    Path analysis result

    REFERENCES

    1. Abrahão, R. S. , Moriguchi, S. N. , and Andrade, D. F. (2016), Intention of adoption of mobile payment: An analysis in the light of the unified theory of acceptance and use of technology (UTAUT), RAI Revista de Administração e Inovação, 13(3), 221-230.
    2. Anderson, J. C. and Gerbing, D. W. (1988), Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin, 103(3), 411-423.
    3. Antovski, L. and Gusev, M. (2003), M-Commerce Services, Institute of Informatics, Faculty of Natural Sciences and Mathematics.
    4. Baptista, G. and Oliveira, T. (2015), Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators, Computers in Human Behavior, 50, 418-430.
    5. Bensinger, G. (2016), The Wall Street Journal, cited 2016 Oct 2, Available from: http://www.wsj.com/articles/shoppers-flock-to-apps-shaking-up-retail-1460539801.
    6. Brown, T. (2015), Confirmatory Factor Analysis for Applied Research (2 ed.), The Guildford Press Publishers, New York.
    7. Campbell, D. T. and Fiske, D. W. (1959), Convergent and discriminant validation by the multitrait-multimethod matrix, Psychological Bulletin, 56(2), 81-105.
    8. Chang, A. (2012), UTAUT and UTAUT 2: A review and agenda for future research, The Winners, 13(2), 106-114.
    9. Chong, A. Y. L. (2013), A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption, Expert Systems with Applications, 40(4), 1240-1247.
    10. Churchill, G. A. and Iacobucci, D. (2002), Marketing Research Methodological Foundations (8th ed.), Thomson: Mason Press.
    11. Compeau, D. and Higgins, C. (1995), Computer selfefficacy: Development of a measure and initial test, MIS Quarterly, 19(2), 189-211.
    12. Corbitt, B. , Thanasankita, T. , and Yi, H. (2003), Trust and e-commerce: A study of consumer perceptions, Electronic Commerce Research and Applications, 2(3), pp. 203-215.
    13. Dasgupta, P. (1988), Trust: Making and breaking cooperative relations, In: Trust as a commodity. Oxford: Basil Blackwell, 49-72.
    14. Dominguez, J. (2016), International Data Corporation, cited 2016 Oct 25, Available from: https://www.idc.com/getdoc.jsp?containerId=prAP41533516.
    15. Escobar-Rodríguez, T. and Carvajal-Trujillob, E. (2014), Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model, Tourism Management , 43, 70-88.
    16. Fishbein, M. and Ajzen, I. (1977), Belief, attitude, intention and behavior: An introduction to theory and research, Philosophy & Rhetoric, 10(2), 130-132.
    17. Ganesan, S. (1994), Determinants of long term orientation in buyer–seller relationships, Journal of Marketing, 58(2), 1-19.
    18. Gao, L. , Waechter, K. A. , and Bai, X. (2015), Understanding consumers’ continuance in-tention towards mobile purchase: A theoretical framework and empirical study–A case of China, Computer Human Behavoir, 53, 249-262.
    19. Gefen, D. (2000), E-commerence: The role of familiarity and trust, Omega, 28(6), 725-737.
    20. Gefen, D. (2002), Reflections in the dimensions of trust and trustworthiness among online consumers, ACM SIGMIS Database, 33(3), 38-53.
    21. Gefen, D. (2003), TAM or just plain habit: A look at experienced online shoppers, Journal of End User Computing, 15(3), 1-13.
    22. Gefen, D. , Karahanna, E. , and Straub, D. W. (2003), Trust and TAM in online shopping: An integrated model, MIS Quarterly, 27(1), 51-90.
    23. Groß, M. (2015), Exploring the acceptance of technology for mobile shopping: An empirical investigation among Smartphone users, The International Review of Retail, Distribution and Consumer Research, 25(3), 215-235.
    24. Groß, M. (2016), Impediments to mobile shopping continued usage intention: A trust-risk-relationship, Journal of Retailing and Consumer Services, 33, 109-119.
    25. Hung, M. C. , Yang, S. T. , and Hsieh, T. C. (2012), An examination of the determinants of mobile shopping continuance, International Journal of Electronic Business Management, 10(1), 29-37.
    26. Islam, Z. , Kim Cheng Low, P. , and Hasan, I. (2013), Intention to use advanced mobile phone services (AMPS), Management Decision, 51(4), 824-838.
    27. Jarvenpaa, S. L. , Tractinsky, N. , and Vitale, M. (2000), Consumer trust in an internet store, Information Technology and Management, 1(1-2), 45-71.
    28. Joubert, J. and Van Belle, J. (2013), The role of trust and risk in mobile commerce adoption within South Africa, International Journal of Business, Humanities and Technology, 3(2), 27-38.
    29. Kim, D. J. , Ferrin, D. L. , and Rao, H. R. (2008), A trustbased consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents, Decision Support Systems, 44(2), 544-564.
    30. Kim, D. J. , Song, Y. I. , Braynov, S. B. , and Rao, H. R. (2005), A multi-dimensional trust formation model in B-to-C e-commerece: A conceptual framework and content analyses of academia/practioner perspective, Decision Support Systems, 40(2), 143-165.
    31. Lee, J. , Lee, S. , and Lee, H. (2015), Screening smartphone applications using malware family signatures, Computers and Security, 52, 234-249.
    32. Li, F. , Pieńkowski, D. , Moorsel, A. V. , and Smith, C. (2012), A holistic framework for trust in online transactions, International Journal of Management Reviews, 14(1), 85-103.
    33. Luarn, P. and Lin, H. (2005), Toward an understanding of the behavioral intention to use mobile baking, Computers in Human Behaviour, 21(6), 873-891.
    34. Luhmann, N. (1979), Trust and Power, Chichester, John Wiley.
    35. Magrath, V. and McCormick, H. (2013), Marketing design elements of mobile fashion retail apps, Journal of Fashion Marketing and Management: An International Journal, 17(1), 115-134.
    36. Martin, C. (2013), Mobile Influence: The New Power of the Consumer, Palgrave, New York.
    37. Miltgen, C. L. , Popovič, A. , and Oliveira, T. (2013), Determinants of end-user acceptance of biometrics: integrating the “Big 3” of technology acceptance with privacy context, Decision Support Systems , 56, 103-114.
    38. Moore, G. C. and Benbasat, I. (1991), Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems Research, 2(3), 192-222.
    39. Oliveira, T. , Thomas, M. , Baptista, G. , and Campos, F. (2016), Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology, Computers in Human Behavior , 61, 404-414.
    40. Pavlou, P. A. and Fygenson, M. ,2006. Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Q, 30(1), pp. 115-143.
    41. Taylor, S. and Todd, P. A. (1995), Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions, Int. J. Res. Mark, 12(2), 137-155.
    42. Vagias, W. (2006), Likert-type Scale Response Anchors, South Carolina: Clemson University.
    43. Vasileiadis, A. (2014), Security concerns and trust in the adoption of m-commerce, Social Techonologies, 4(1), 179-191.
    44. Venkatesh, V. , Morris, M. G. , Davis, G. B. , and Davis, F. D. (2003), User acceptance of information technology: toward a unified view, MIS Quartely, 27(3), 425-478.
    45. Venkatesh, V. , Thong, J. , and Xu, X. (2012), Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Quartely, 36(1), 157-178.
    46. Wei, T. T. , Marthandan, G. , Chong, A. Y. , Ooi, K. , and Arumugam, S. (2009), What drives Malaysian mcommerce adoption? An emprical analysis, Industrial Management and Data Systems, 109(3), 370-388.
    47. Wu, J. and Wang, S. (2005), What drives mobile commerce? An empirical evaluation of the revised technology acceptance model, Information and Management , 42 (5), 719-729.