► The determinants of m-commerce adoption were examined in this research. ► The TAM model was extended by incorporating perceived enjoyment, trust, cost, network influence, and variety of services. ► ...A two-staged, multi-analytic approach of integrating SEM and neural network was applied to examine the model. ► The multi-analytic approach addressed existing limitations in applying SEM or neural network individually. ► Result showed that perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, variety of services, and network influence m-commerce adoption. ► The integrated approach allows more accurate results that help decision makers develop appropriate strategies to improve m-commerce adoption.
The advancement in wireless and mobile technologies has presented tremendous business opportunity for mobile-commerce (m-commerce). This research aims to examine the factors that influence consumers’ m-commerce adoption intention. Variables such as perceived usefulness, perceived ease of use, perceived enjoyment, trust, cost, network influence, and variety of services were used to examine the adoption intentions of consumers. Data was collected from 376 m-commerce users. A multi-analytic approach was proposed whereby the research model was tested using structural equation modeling (SEM), and the results from SEM were used as inputs for a neural network model to predict m-commerce adoption. The result showed that perceived usefulness, perceived enjoyment, trust, cost, network influence, and trust have significant influence on consumers’ m-commerce adoption intentions. However, the neural network model developed in this research showed that the best predictors of m-commerce adoption are network influence, trust, perceived usefulness, variety of service, and perceived enjoyment. This research proposed an innovative new approach to understand m-commerce adoption, and the result for this study will be useful for telecommunication and m-commerce companies in formulating strategies to attract more consumers.
► Predictors of m-commerce adoption were examined in this research. ► The UTAUT model was extended by incorporating perceived value, trust, perceived enjoyment and personal innovativeness. ► Neural ...network was developed to examine the research model. ► Results showed that neural network can offer better predictions than multiple regression model. ► Our approach helps decision makers formulate appropriate strategies for m-commerce implementation in the Chinese industry.
M-commerce has continued to grow at an explosive rate. This purpose of this paper is to examine the predictors of m-commerce adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extended model incorporates additional constructs such as perceived value, trust, perceived enjoyment and personal innovativeness. A non-linear, non-compensatory model is developed to understand the predictors of m-commerce adoptions. Online survey was used to collect data from 140 Chinese users. Neural network analysis was used to predict m-commerce adoption, and the model was compared with the results from regression analysis. The neural network model outperformed the regression model in adoption prediction, and captured the non-linear relationships between predictors such as perceived value, trust, perceived enjoyment, personal innovativeness, users demographic profiles (e.g. age, gender and educational level), effort expectancy, performance expectancy, social influence and facilitating conditions with m-commerce adoption. This study applied neural network to provide further understanding of m-commerce adoption decisions based on a non-linear, non-compensatory model. The UTAUT model was also extended to examine consumer information systems such as m-commerce. The m-commerce study conducted in this research is in China, one of the fastest growing m-commerce markets in the world.
China is one of the largest and fastest‐growing markets for live streaming, and the purchase of virtual gifts in live streaming is the core for streamers and live streaming platforms in China to ...survive and thrive. Compared to western countries, live streaming in China highlights the lively social atmosphere and heated social interactions among streamers and viewers. This study develops a cultural context‐sensitive model that contextualises the purchase of virtual gifts in live streaming in China. Specifically, we focus on the viewer's social experience and the social atmosphere in live streaming which have received limited attention yet. We introduce viewers' social perceptions with regard to the streamer and other viewers (ie, perceived proximity to the streamer and sense of belonging to the viewer crowd) and show how such social perceptions contribute to the development of flow experience, which subsequently leads to purchase intention. This study also reveals how such social perceptions can be shaped by the contextual setting consisting of the IT‐related factors of live streaming (ie, responsiveness, two‐way communication, social presence, and self‐presentation) and the cultural characteristics of China (ie, social orientation and harmony). Our research offers both theoretical guidance for practitioners into cultivating viewers' purchase of virtual gifts in China's live streaming.
•The relationship between GSCM practices and TI is tested by using PLS-SEM approach.•Internal environmental management is positively and significantly related to TI.•Eco-design is positively and ...significantly linked with TI.•Investment recovery is positively and significantly related to TI.•Green purchasing and cooperation with customers are insignificant linked with TI.
This study sets out to empirically test the research framework and identify the relationship between green supply chain management (GSCM) practices and technological innovation (TI) in manufacturing firms. It is one of the first studies that experimentally validate the relationship between GSCM practices and TI from the perspective of a developing nation. In this study, 133 usable sets of data were collected from manufacturing firms in Malaysia. Results obtained from the partial least square-structural equation modeling (PLS-SEM) approach supported the significant positive relationship between three GSCM practices (i.e. internal environmental management, eco-design and investment recovery) and TI, but the study found that green purchasing and cooperation with customers do not have a significant positive correlation with TI. The importance of GSCM practices has been proven to enhance firms’ TI, in addition to improving the environment, bringing about a positive impact on the manufacturing establishment. Therefore, this study has relevance to all manufactures seeking to enhance their TI through the effective use of GSCM practices.
Purpose
Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares ...structural equation modeling (PLS-SEM) in Industrial Management & Data Systems (IMDS) and extend MIS Quarterly (MISQ) applications to include the period 2012-2014.
Design/methodology/approach
Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM.
Findings
The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data.
Research limitations/implications
Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction.
Practical implications
This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures.
Originality/value
Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.
Purpose
The purpose of this paper is to investigate what factors can affect people’s continuous watching and consumption intentions in live streaming.
Design/methodology/approach
This research ...conducted a mixed-methods study. The semi-structured interview was deployed to develop a research model and a live streaming typology. A survey was then used for quantitative assessment of the research model. Survey data were analyzed using partial least squares-structural equation modeling.
Findings
The results suggest that sex and humor appeals, social status display and interactivity play considerable roles in the viewer’s behavioral intentions in live streaming and their effects vary across different live streaming types.
Research limitations/implications
This research is conducted in the Chinese context. Future research can test the research model in other cultural contexts. This study can also be extended by incorporating the roles of viewer gender and price sensitivity in the future.
Practical implications
This study provides managerial insights into how live streaming platforms and streamers can improve their popularity and profitability.
Originality/value
The paper introduces a novel form of social media and a new business model. It illustrates what will affect people’s behavioral intentions in such a new context.
There are both formal and informal cries that UTAUT and, by association, the stream of research on technology adoption has reached its limit, with little or no opportunities for new knowledge ...creation. Such a conclusion is ironic because the theory has not been sufficiently and suitably replicated. It is possible that misspecifications in the various replications, applications, and extensions have led to the incorrect conclusion that UTAUT is more robust than it really is, leaving limited opportunities for future work. Although work on UTAUT has included important variables, predictors, and moderators, absent a faithful use of the original specification, it is impossible to assess the true nature of the effects of the original and additional variables. The present meta-analysis uses 25,619 effect sizes reported by 737,112 users in 1,935 independent samples to address this issue. Consequently, we develop a clear current state-of-the-art and revised UTAUT that extends the original theory with new endogenous mechanisms from different, other theories (i.e., technology compatibility, user education, personal innovativeness, and costs of technology) and new moderating mechanisms to examine the generalizability of UTAUT in different contexts (e.g., technology type and national culture). Based on this revised UTAUT, we present a research agenda that can guide future research on the topic of technology adoption in general and UTAUT in particular.
This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to ...predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands.
•Social presence of sellers and others significantly influence trust in s-commerce.•Information support significantly influences trust in s-commerce.•Social presence of web has no significant effect ...on trust in s-commerce.•There is nonlinear relationship between social presence of sellers and trust.•The SEM-ANN model explained 76.9% total variance of trust in s-commerce.
Trust is an essential concern in s-commerce. Though existing research has studied the association between trust and purchasing intention; the determinants of the formation of trust in s-commerce remain largely unexplored. This study examines the determinants of trust in s-commerce based on social presence and social support. Unlike most business research, we applied a hybrid SEM-ANN approach that can detect non-linear and non-compensatory relationships. Linear and compensatory models assume that a shortfall in one factor may be compensated by other factors. However, consumer decision-making processes are complicated and non-compensatory and linear models tend to oversimplify these processes. Criterion sampling was used to gather 462 datasets of social commerce users using a mall intercept technique. Information support has the strongest effect followed by the social presence of interaction with the sellers, income and social presence of others. The integrated model predicts 76.9% trust in s-commerce. Theoretical and managerial contributions are discussed.
Advancements in wireless communications have increased the number of people using mobile devices, and have accelerated the growth of mobile commerce (m-commerce). This study aims to investigate the ...factors that predict consumer intention to adopt m-commerce in Malaysia and China. The work extends the traditional technology acceptance model (TAM) and diffusion of innovation (DOI) model, and includes additional variables such as trust, cost, social influence, variety of services, and control variables such as age, educational level, and gender of consumers. By comparing consumers from both Malaysia and China, this research is able to form a prediction model based on two different cultural settings. Data was collected from 172 Malaysian consumers and 222 Chinese consumers, and hierarchical regression analysis was employed to test the research model. The results showed that age, trust, cost, social influence, and variety of services are able to predict Malaysian consumer decisions to adopt m-commerce. Trust, cost, and social influence can be used to predict Chinese consumer decisions to adopt m-commerce. This research confirms the need to extend the traditional TAM and DOI models when studying technology such as m-commerce. The results from this study will be useful for telecommunication and m-commerce companies in formulating marketing strategies.
► This research compares the m-commerce adoption decisions in China and Malaysia. ► TAM was extended by incorporating trust, cost, social influence and variety of services. ► Hierarchical regression analyses were performed to examine the conceptual model. ► Traditional TAM variables are unable to predict m-commerce adoption. ► Cultural factors played an important role in predicting m-commerce adoptions.
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