Exploring m-commerce in terms of viability, growth and challenges

Smith, Alan D.

doi:10.1504/ijmc.2006.010362

Cited by 46 publications

(18 citation statements)

References 24 publications

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“…Before any user performance and satisfaction studies in controlled experimental environments can be done for comparison purposes between e-commerce and m-commerce, preferences towards using m-commerce need to be determined from the consumer/user perspective [58]. In the next section, the methodology for our experimentation is detailed.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student population

Ozok

Wei

2010

Electron Commer Res

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To determine the differences in user preferences between electronic and mobile commerce in consumer shopping, a 25-item survey concerning usability aspects of the two media was implemented on one hundred and eighteen college eshoppers with experience in mobile devices. The Mobile Commerce Comparison Survey (MCCS) had four validated comparison factors: human factors-related, interface features, product-, and service-related factor. The comparison concludes that usability issues are perceived to be behind in m-commerce, and that m-commerce should be a shopping medium complementary to classic e-commerce rather than a direct alternative to it. Future studies may include empirical usability experiments for m-commerce tasks.

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Section: Theoretical Frameworkmentioning

confidence: 99%

An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student population

Ozok

Wei

2010

Electron Commer Res

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“…Electronic commerce means conducting financial transaction electronically over the internet, whereas mobile commerce is defined as buying and selling goods and services through wireless handheld devices such as cellular phones and Personal Digital Assistants (PDA) 83 . Others view mobile commerce as the use of wireless technology, particularly handheld mobile devices and mobile Internet, to facilitate transactions, information searches and other user tasks 14 .…”

Section: Introductionmentioning

confidence: 99%

User adoption of mobile commerce in Bangladesh: Integrating perceived risk, perceived cost and personal awareness with TAM

Rahman¹,

Sloan²

2017

ITMR

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This paper develops and tests a model for predicting user adoption of mobile commerce (i.e. e-commerce using mobile phones) in developing countries. The model takes up the Technology Acceptance Model (TAM) determinants of perceived usefulness and perceived ease of use, and extends these through the inclusion of three further determinants: perceived risk, perceived cost and personal awareness to enable prediction of the likelihood of mobile commerce adoption by users in the developing world. 575 usable surveys were gathered from the urban, semiurban and rural areas of Bangladesh. Perceived risk and perceived usefulness were found to be the most influential factors effecting mobile commerce adoption, while the effect of perceived ease of use and personal awareness was found to be low although significant. The study reconfirmed the need of extending TAM model, in the context of mobile commerce, to address both it's transactional and non-transactional components. Service providers need to ensure high levels of security and privacy to reduce user's perceptions of risk. Mobile commerce services and technologies should be upgraded to world standard to make them more useful to users.

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“…If a service is popular, in a time window, it will be visited more often by consumers than other services in the same cluster. Moreover, we adjust the time window to make frequency ∈ [1,50]. Similarly, higher reputation of a service contributes to the consumers' prefer selection, with a value from 1 through 10.…”

Section: ) Ant Colony Navigation Modelmentioning

confidence: 99%

“…Due to the rapid development of wireless communication and hardware techniques, the mobile commerce (m-commerce) [1] has become more and more popular. M-commerce has shown immense potential in the electronic market through the encouragement of the wide promotion of mobile communication technologies and rapid adoption of mobile devices with Internet capabilities.…”

Section: Introductionmentioning

confidence: 99%

An attribute-based scheme for service recommendation using association rules and ant colony algorithm

Chen

Shao

Xie

et al. 2010

2010 Wireless Telecommunications Symposium (WTS)

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With the rapid development of m-commerce, predicting user's navigation and making the service recommendation become more and more important. Most researches focus on predicting user's navigation using context history and user preferences. But, the influence of the attributes of a service has been ignored. Simultaneously, some attributes are variable, so the recommendations are changeable. Therefore, the paper proposes an attribute-based scheme for service recommendation based on CASUP (context-aware system considering user preference). In proposed approach, the services are classified into several service clusters, and the service recommendations are carried out using Apriori algorithm and ant colony algorithm. Finally, the proposed model is validated by several simulation experiments, which demonstrate the effects of the service attributes in mcommerce.

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Exploring m-commerce in terms of viability, growth and challenges

Cited by 46 publications

References 24 publications

An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student population

An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student population

User adoption of mobile commerce in Bangladesh: Integrating perceived risk, perceived cost and personal awareness with TAM

An attribute-based scheme for service recommendation using association rules and ant colony algorithm

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