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Research Article

Rural E-Commerce Model with Attention Mechanism: Role of Li Ziqi’s Short Videos from the Perspective of Heterogeneous Knowledge Management

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References

  • Bosworth, G. (2012). Characterising rural businesses–Tales from the paperman. Journal of Rural Studies, 28(4), 499–506. doi:https://doi.org/10.1016/j.jrurstud.2012.07.002
  • Chao, P., Bm, B., & Chen, Z. C. (2021). Poverty alleviation through e-commerce: Village involvement and demonstration policies in rural China. Journal of Integrative Agriculture, 20(4), 998–1011. doi:https://doi.org/10.1016/S2095-3119(20)63422-0
  • Chen, Z., Xu, K., Wei, J., & Dong, G. (2019). Voltage fault detection for lithium-ion battery pack using local outlier factor. Measurement, 146, 544–556. doi:https://doi.org/10.1016/j.measurement.2019.06.052
  • Cheng, L., Wang, Y., & Ma, X. (2019). A neural probabilistic outlier detection method for categorical data. Neurocomputing, 365, 325–335. doi:https://doi.org/10.1016/j.neucom.2019.07.069
  • Costa, B. S. J., Angelov, P. P., & Guedes, L. A. (2015). Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier. Neurocomputing, 150, 289–303. doi:https://doi.org/10.1016/j.neucom.2014.05.086
  • Cui, M., Pan, S. L., Newell, S., & Cui, L. (2017). Strategy, resource orchestration and e-commerce enabled social innovation in Rural China. The Journal of Strategic Information Systems, 26(1), 3–21. doi:https://doi.org/10.1016/j.jsis.2016.10.001
  • Eisenhardt, K. M. (1989). Building Theories from Case Study Research. Academy of Management Review, 34(4), 532–550. doi:https://doi.org/10.2307/258557
  • Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of Management Journal, 50(1), 25–32. doi:https://doi.org/10.5465/amj.2007.24160888
  • Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47–55. doi:https://doi.org/10.1016/j.chb.2016.03.003
  • Fan, J., Tang, L., Zhu, W., & Zou, B. (2018). The Alibaba effect: Spatial consumption inequality and the welfare gains from e-commerce. Journal of International Economics, 114, 203–220. doi:https://doi.org/10.1016/j.jinteco.2018.07.002
  • Fecke, W., Danne, M., & Musshoff, O. (2018). E-commerce in agriculture–The case of crop protection product purchases in a discrete choice experiment. Computers and Electronics in Agriculture, 151, 126–135. doi:https://doi.org/10.1016/j.compag.2018.05.032
  • Feng, J., Li, X., & Zhang, X. (2019). Online product reviews-triggered dynamic pricing: Theory and evidence. Information Systems Research, 30(4), 1107–1123. doi:https://doi.org/10.1287/isre.2019.0852
  • Gao, J., Ji, W., Zhang, L., Li, A., Wang, Y., & Zhang, Z. (2020). Cube-based incremental outlier detection for streaming computing. Information Sciences, 517, 361–376. doi:https://doi.org/10.1016/j.ins.2019.12.060
  • Gao, P., & Liu, Y. (2020). Endogenous inclusive development of e‐commerce in rural China: A case study. Growth and Change, 51(4), 1611–1630. doi:https://doi.org/10.1111/grow.12436
  • Goh, P. Y., Tan, S. C., Cheah, W. P., & Lim, C. P. (2019). Adaptive rough radial basis function neural network with prototype outlier removal. Information Sciences, 505, 127–143. doi:https://doi.org/10.1016/j.ins.2019.07.066
  • Greenberg, Z., Farja, Y., & Gimmon, E. (2018). Embeddedness and growth of small businesses in rural regions. Journal of Rural Studies, 62, 174–182. doi:https://doi.org/10.1016/j.jrurstud.2018.07.016
  • Herzallah, D., Muñoz-Leiva, F., & Liébana-Cabanillas, F. (2021). Selling on Instagram: Factors that determine the adoption of Instagram commerce. International Journal of Human–Computer Interaction, 1–19. doi:https://doi.org/10.1080/10447318.2021.1976514
  • Huang, N., Burtch, G., Hong, Y., & Polman, E. (2016). Effects of multiple psychological distances on construal and consumer evaluation: A field study of online reviews. Journal of Consumer Psychology, 26(4), 474–482. doi:https://doi.org/10.1016/j.jcps.2016.03.001
  • Huang, N., Sun, T., Chen, P., & Golden, J. M. (2019). Word-of-mouth system implementation and customer conversion: A randomized field experiment. Information Systems Research, 30(3), 805–818.
  • Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., & Jianzhou, Y. (2017). eWOM source credibility, perceived risk and food product customer’s information adoption. Computers in Human Behavior, 66, 96–102.
  • Hussain, S., Guangju, W., Jafar, R. M. S., Ilyas, Z., Mustafa, G., & Jianzhou, Y. (2018). Consumers’ online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, 80, 22–32.
  • Jalali, A. A., Okhovvat, M. R., & Okhovvat, M. (2011). A new applicable model of Iran rural e-commerce development. Procedia Computer Science, 3, 1157–1163.
  • Jayaprakash, P., & Pillai, R. R. (2021). The role of ICT and effect of national culture on human development. Journal of Global Information Technology Management, 24(3), 183–207.
  • Jiang, X., Ma, J., Jiang, J., & Guo, X. (2019). Robust feature matching using spatial clustering with heavy outliers. IEEE Transactions on Image Processing, 29, 736–746.
  • Kalinic, Z., & Marinkovic, V. (2016). Determinants of users’ intention to adopt m-commerce: An empirical analysis. Information Systems and e-Business Management, 14(2), 367–387.
  • Kaurin, A., Heil, L., Wessa, M., Egloff, B., & Hirschmüller, S. (2018). Selfies reflect actual personality–Just like photos or short videos in standardized lab conditions. Journal of Research in Personality, 76, 154–164.
  • Kim, T. Y., Dekker, R., & Heij, C. (2017). Cross-border electronic commerce: Distance effects and express delivery in European Union markets. International Journal of Electronic Commerce, 21(2), 184–218.
  • Kumar, A., & Hosanagar, K. (2019). Measuring the value of recommendation links on product demand. Information Systems Research, 30(3), 819–838.
  • Li, J., Zhang, J., Qin, X., & Xun, Y. (2019). Feature grouping-based parallel outlier mining of categorical data using spark. Information Sciences, 504, 1–19.
  • Lin, G., Xie, X., & Lv, Z. (2016). Taobao practices, everyday life and emerging hybrid rurality in contemporary China. Journal of Rural Studies, 47, 514–523.
  • Liu, M., Min, S., Ma, W., & Liu, T. (2021). The adoption and impact of e-commerce in rural China: Application of an endogenous switching regression model. Journal of Rural Studies, 83(1), 106–116.
  • Liu, M., Zhang, Q., Gao, S., & Huang, J. (2020). The spatial aggregation of rural e-commerce in China: An empirical investigation into taobao villages. Journal of Rural Studies, 80, 403–417.
  • Ma, M. X., Ngan, H. Y., & Liu, W. (2016). Density-based outlier detection by local outlier factor on largescale traffic data. Electronic Imaging, 2016(14), 1–4.
  • Maloney, M., & McCarthy, A. (2017). Understanding pension communications at the organizational level: Insights from bounded rationality theory & implications for HRM. Human Resource Management Review, 27(2), 338–352.
  • Mazgualdi, C. E., Masrour, T., El Hassani, I., & Khdoudi, A. (2021). Machine learning for KPIs prediction: A case study of the overall equipment effectiveness within the automotive industry. Soft Computing, 25(4), 2891–2909.
  • Miethe, T. D., Venger, O., & Lieberman, J. D. (2019). Police use of force and its video coverage: An experimental study of the impact of media source and content on public perceptions. Journal of Criminal Justice, 60, 35–46.
  • Ogura, T., & Tsuda, K. (2019). Estimating the supply and demand balance of the market by exploring the gross profit transition in E-commerce business. Procedia Computer Science, 159, 1339–1346.
  • Oh, J. H., Hong, J. Y., & Baek, J. G. (2019). Oversampling method using outlier detectable generative adversarial network. Expert Systems with Applications, 133, 1–8.
  • Ozkan, H., Ozkan, F., & Kozat, S. S. (2016). Online anomaly detection under Markov statistics with controllable type-I error. IEEE Transactions on Signal Processing, 64(6), 1435–1445.
  • Peiffer, C., & Armytage, R. (2019). Searching for success: A mixed methods approach to identifying and examining positive outliers in development outcomes. World Development, 121, 97–107.
  • Philip, L., & Williams, F. (2019). Remote rural home based businesses and digital inequalities: Understanding needs and expectations in a digitally underserved community. Journal of Rural Studies, 68, 306–318.
  • Radovanović, M., Nanopoulos, A., & Ivanović, M. (2015). Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Transactions on Knowledge and Data Engineering, 27(5), 1369–1382.
  • Räisänen, J., & Tuovinen, T. (2020). Digital innovations in rural micro-enterprises. Journal of Rural Studies, 73, 56–67.
  • Rajasegarar, S., Leckie, C., & Palaniswami, M. (2014). Hyperspherical cluster based distributed anomaly detection in wireless sensor networks. Journal of Parallel and Distributed Computing, 74(1), 1833–1847.
  • Ravichandran, T., & Giura, S. I. (2019). Knowledge transfers in alliances: Exploring the facilitating role of information technology. Information Systems Research, 30(3), 726–744.
  • Razzak, I., Saris, R. A., Blumenstein, M., & Xu, G. (2020). Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection. Neural Networks, 121, 441–451.
  • Saunila, M., Ukko, J., & Rantala, T. (2019). What determines customers’ engagement in the digital service process? Journal of Manufacturing Technology Management. 30(8), 1216–1229 .
  • Shah, N. J., & Patil, H. A. (2019). A novel approach to remove outliers for parallel voice conversion. Computer Speech & Language, 58, 127–152.
  • Silva, M. M., Ramos, W. L., Chamone, F. C., Ferreira, J. P., Campos, M. F., & Nascimento, E. R. (2018). Making a long story short: A multi-importance fast-forwarding egocentric videos with the emphasis on relevant objects. Journal of Visual Communication and Image Representation, 53, 55–64.
  • Sousa, M. J., & Rocha, Á. (2019). Skills for disruptive digital business. Journal of Business Research, 94, 257–263.
  • Sun, X. L., Wu, Y. J., Zhang, C., & Wang, H. L. (2019). Performance of median kriging with robust estimators of the variogram in outlier identification and spatial prediction for soil pollution at a field scale. Science of the Total Environment, 666, 902–914.
  • Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47–65.
  • Tang, B., & He, H. (2017). A local density-based approach for outlier detection. Neurocomputing, 241, 171–180.
  • Tang, W., & Zhu, J. (2020). Informality and rural industry: Rethinking the impacts of E-Commerce on rural development in China. Journal of Rural Studies, 75, 20–29.
  • Titouna, C., Naït-Abdesselam, F., & Khokhar, A. (2019). DODS: A distributed outlier detection scheme for wireless sensor networks. Computer Networks, 161, 93–101.
  • Törhönen, M., Giertz, J., Weiger, W. H., & Hamari, J. (2021). Streamers: The new wave of digital entrepreneurship? Extant corpus and research agenda. Electronic Commerce Research and Applications, 46, 101027.
  • Tran, L., Fan, L., & Shahabi, C., 2016. Distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 9( 12), pp.1089–1100.
  • Tseng, S. Y., & Wang, C. N. (2016). Perceived risk influence on dual-route information adoption processes on travel websites. Journal of Business Research, 69(6), 2289–2296.
  • Wang, S., Hung, K., & Huang, W. J. (2019). Motivations for entrepreneurship in the tourism and hospitality sector: A social cognitive theory perspective. International Journal of Hospitality Management, 78, 78–88.
  • Wang, B., & Mao, Z. (2019). Outlier detection based on Gaussian process with application to industrial processes. Applied Soft Computing, 76, 505–516.
  • Wang, E., Yang, Y., Wu, J., Lou, K., Liu, W., & Xu, Y. (2020). Budgeted video replacement policy in mobile crowdsensing. Journal of Parallel and Distributed Computing, 136, 1–13.
  • Xia, H., Pan, X., Zhou, Y., & Zhang, Z. J. (2020). Creating the best first impression: Designing online product photos to increase sales. Decision Support Systems, 131, 113235.
  • Xia, H., Wang, Q., & Zhang, Z. (2019). Knowledge heterogeneity in university-industry knowledge transfer: A case analysis of Xu’s Ruyi textile. Knowledge Management Research & Practice, 17(4), 486–498.
  • Xie, C., & He, D. (2020). Research on the construction of E-commerce precision poverty alleviation system based on geographic information. Procedia Computer Science, 166, 111–114.
  • Xie, X. Z., Tsai, N. C., Xu, S. Q., & Zhang, B. Y. (2019). Does customer co-creation value lead to electronic word-of-mouth? An empirical study on the short-video platform industry. The Social Science Journal, 56(3), 401–416.
  • Xu, P., Chen, L., & Santhanam, R. (2015). Will video be the next generation of e-commerce product reviews? Presentation format and the role of product type. Decision Support Systems, 73, 85–96.
  • Xu, H., Deng, W. S., Feng, Y. C., & Lei, X. Y. (2017). Study on the growth path and mechanism of brand ecosystem a longitudinal case study of Yunnan Baiyao from 1999 to 2015. Management World, 06, 122–140,188. In Chinese
  • Yan, L., Zhuo, C., & Hua, Z. (2012). Improving sharing efficiency in online short video system through using P2P based mechanism. Procedia Engineering, 29, 3207–3211.
  • Zhang, W., Wang, B., Ma, S., Zhang, Y., & Zhao, Y. (2021). I2net: Mining intra-video and inter-video attention for temporal action localization. Neurocomputing, 44, 16–29.
  • Zhang, G., Wei, F., Jia, N., Ma, S., & Wu, Y. (2019). Information adoption in commuters’ route choice in the context of social interactions. Transportation Research Part A: Policy and Practice, 130, 300–316.
  • Zhao, J., Huang, Y., Xi, X., & Wang, S. (2020). How knowledge heterogeneity influences business model design: Mediating effects of strategic learning and bricolage. International Entrepreneurship and Management Journal,17, 889–919.
  • Zhou, L., Wang, W., Xu, J. D., Liu, T., & Gu, J. (2018). Perceived information transparency in B2C e-commerce: An empirical investigation. Information & Management, 55(7), 912–927.
  • Zhu, D. H., Chang, Y. P., & Luo, J. J. (2016). Understanding the influence of C2C communication on purchase decision in online communities from a perspective of information adoption model. Telematics and Informatics, 33(1), 8–16.

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