Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kaur, Kuljit
- A Review Paper on Clustering in Data Mining
Abstract Views :95 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, Punjabi University, Patiala, IN
1 Department of Computer Engineering, Punjabi University, Patiala, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 13 (2014), Pagination: 144-151Abstract
Clustering is a process of keeping similar data intogroups. Objects within the cluster/group have high similarity in comparison to one another but are very dissimilar to objects of other clusters. Clustering is an unsupervised learningtechnique as every other problem of this kind; it dealswith finding a structure in a collection of unlabeleddata. Types of clustering methods are-hierarchical and partitioningbased. Inthis paper clustering and its methodsare discussed.Keywords
Data Mining, Clustering, Partitioning Method, Hierarchical Clustering, Cluster Distance.Full Text
- Refining and Validating Image Congruence, Satisfaction and Loyalty Amongst Mall Shoppers in India
Abstract Views :306 |
PDF Views:0
Authors
Kuljit Kaur
1,
Harmeen Soch
1
Affiliations
1 Department of Management, I. K. Gujral Punjab Technical University, Kapurthala, Punjab, IN
1 Department of Management, I. K. Gujral Punjab Technical University, Kapurthala, Punjab, IN
Source
International Journal on Customer Relations, Vol 6, No 1 (2018), Pagination: 31-37Abstract
Understanding of motives behind the shopping behaviour of mall shoppers can help the mall managers attract new customers and to retain existing shoppers. Mall loyalty is the key factor of interest for managers because a loyal customer will patronise the mall in future and will spread positive word of mouth about the mall. Mall loyalty is considerably influenced by mall satisfaction and similarity between shoppers’ self-image and mall image (image congruence). The relationships between these variables in Indian context are not elaborated in detail. Therefore, the scales measuring these variables are not well explored for Indian context. In the current study, the reliability of scales measuring all the four types of self-image congruence (actual, ideal, social and ideal social), customer satisfaction and mall loyalty are tested using various methods of item-to-total score correlation criteria. The findings also provide implications and limitations for future work.Keywords
Image Congruence, Mall Loyalty, Customer Satisfaction, Shopping Mall.
References
- Athanassopoulos, A., Gournaris, S., & Stathakopoulos, V. (2001). Behavioral responses to customer satisfaction: An empirical study. European Journal of Marketing, 35(5/6), 687-708.
- Babin, B. J., & Attaway, J. S. (2000). Atmospheric affect as a tool for creating value and gaining share of customer. Journal of Business Research, 49(2), 91-99.
- Bearden, W. O., David, M. H., & Randall, L. R. (2001).Consumer self-confidence: Refinement in conceptualization and measurement. Journal of Consumer Research, 28(june), 121-134.
- Chebat, J. C., Hedhli. K. E., & Sirgy, M. J. (2009). How does shopper-based mall equity generate mall loyalty? A conceptual model and empirical evidence. Journal of Retailing and Consumer Services, 16, 50-60.
- Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(Feb), 64-73.
- Das, G. (2014). Factors affecting Indian shoppers attitude and purchase intention: An empirical check. Journal of Retailing and Consumer Services, 21, 561-569.
- El Hedhli, K., Chebat, J. C., & Sirgy, M. J. (2013). Shopping well being at the mall: Construct, antecedents and consequences. Journal of Business Research, 66, 856-863.
- Gilbert, R. G., Veloutsou, C., Goode, M. M. H., & Moutinho, L. (2004). Measuring customer satisfaction in the fast food industry: A cross national approach. Journal of Service Marketing, 18(5), 371-383.
- Ha, S., & Im, H. (2012). Identifying the role of self-congruence on shopping behavior in the context of U.S. shopping malls. Clothing and Textiles Research Journal, 30(2), 87-101.
- Haj-Salem, N., Chebat, J. C., Michon, R., & Oliveira, S. (2016). Why male and female shoppers do not see mall loyalty through the same lens? The mediating role of self-congruity. Journal of Business Research, 69, 1219-1227.
- Han, H., & Back, K.-L. (2008). Relationships among congruence, consumption emotions and customer loyalty in the lodging industry. Journal of Hospitality & Tourism Research, 32(4), 467-490.
- Han, H., & Hyun, S. S. (2013). Image congruence and relationship quality in predicting switching intention: Conspicuousness of product use as a moderator variable. Journal of Hospitality & Tourism Research, 37.
- He, H., & Mukherjee, A. (2007). I am ergo I shop: does store image congruity explain shopping behaviour of Chinese consumers? Journal of Marketing Management, 23(5-6), 443-460.
- Hyun, S., & Han, H. (2012). A model of Patron’s innovativeness formation toward a chain restaurant brand. International Journal of Contemporary Hospitality Management, 24(2), 175-199.
- Ibrahim, H., & Najjar, F. (2008). Assessing the effects of self-congruity, attitudes and customer satisfaction on customer behavioural intentions in retail environment. Marketing Intelligence and Planning, 26(2), 207-227.
- Jamal, A., & Al-marri, M. (2007). Exploring the effect of self-image congruence and brand preference on satisfaction: The role of expertise. Journal of marketing Management, 23(7-8), 613-629.
- Kaihatu, T. S., & Spence, M. T. (2016). The relationship between shopping mall image and congruity on customer behavior: Evidence from Indonesia. Australian Marketing Journal, 24, 141-145.
- Kline, T. (2005). Psychological testing: A practical approach to design and evaluation. California: Sage Publication.
- Koufteros, X. A. (1999). Testing a model of pull production: A paradigm for manufacturing research using structural equation modeling. Journal of Operations Management, 17(4), 467-488.
- Kumar, K., & Michael, B. (1991). Construction and validation of an instrument for measuring ingratiatory behaviors in organizational settings. Journal of Applied Psychology, 76(5), 619-627.
- Liu, F., Li, J., Mizerski, D., & Soh, H. (2012). Self-congruity, brand attitude, and brand loyalty: A study on luxury brands. European Journal of Marketing, 46(7/8), 922-937.
- Malar, L., Kromer, H., Hoyer, W. D., & Nyffenegger, B. (2011). Emotional brand attachment and brand personality: The relative importance of the actual and the ideal self. Journal of Marketing, 75(July), 35-52.
- Mishra, P., Bakshi, M., & Singh, R. (2016). Impact of consumption emotions on WOM in movie consumption: Empirical evidence from emerging markets. Australasian Marketing Journal, 24, 59-67.
- O’Cass, A., & Grace, D. (2008). Understanding the role of retail store service in light of self-image-store image congruence. Psychology and Marketing, 25(6), 521-537.
- Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 495-507.
- Oliver, R.L. (1997). Satisfaction: A behavioural perspective on the consumer. New York: McGraw-Hill.
- Roy, R., & Rabbanee, F. K. (2015). Antecedents and consequences of self-congruity. European Journal of Marketing, 49(3/4), 444-466.
- Schiffman, L. G., & Kanuk, L. L. (2000). Consumer behavior (7th ed.). Wisconsin: Prentice Hall.
- Sirgy, M. J., Grewal, D., & Mangleburg, T. (2000). Retail environment, self-congruity, and retail patronage: an integrative model and a research agenda. Journal of Business Research, 49(2), 127-138.
- Sirgy, M. J., Grewal, D., Mangleburg, T. F., Park, J., Chon, K., & Claiborne, C. B. (1997). Assessing the predictive validity of two methods of measuring self-image congruence. Journal of the Academy of Marketing Science, 25, 229-241.
- Thomson, M., Macinnis, D. J., & Park, C. W. (2005). The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. Journal of Consumer Psychology, 15(1), 77-91.
- Wright, C., & Sparks, L. (1999). Loyalty saturation in retailing: Exploring the end of retail loyalty cards. International Journal of Retail Distribution Management, 27, 429-440.
- Yi, Y. (1990). A critical review of consumer satisfaction. In: Zeithaml, V. (Ed.), Review of Marketing, American Marketing Association, Chicago, 68-122.
- Yu, Y.-T., & Dean, A. (2001). The contribution of emotional satisfaction to consumer loyalty. International Journal of Service Industry Management, 12(3), 234-250.
- Identifying Usability Issues of the Most Popular Mobile Education Apps from User Reviews
Abstract Views :125 |
PDF Views:0
Authors
Kiranbir Kaur
1,
Kuljit Kaur
2
Affiliations
1 Research Scholar, Guru Nanak Dev University, Amritsar, IN
2 Professor, Guru Nanak Dev University, Amritsar, IN
1 Research Scholar, Guru Nanak Dev University, Amritsar, IN
2 Professor, Guru Nanak Dev University, Amritsar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 352-365Abstract
The study of mobile apps is a hot research topic because of the expanding use of mobile devices around the world. App user reviews provide a channel of communication between app developers and their end users. They are the best source for understanding user opinions and concerns. Analysis of mobile education apps from various angles and contexts is revealed by several literature reviews. However, there is hardly any study analyzing the emerging issues of Adults education apps in the body of literature. The goal of this study is to identify the main topics related to emerging usability issues in user reviews of the apps on the Google Play Store. The study analyses 2,28,660 negative reviews and 9,93,460 positive user reviews of the top five popular education-related apps (Coursera, Edx, LinkedIn, Skillshare, and Udemy) using Natural Language Processing tasks such as Latent Dirichlet Algorithm (LDA). The results identify several inherent topics in the negative reviews such as account login issues, security and privacy issues, payment issues, content-related issues, app usability issues, customer service issues, and bug-fixing issues. On the other way, the analysis of positive reviews reveals ease of use, the best learning app, user-friendly and great platform.Keywords
Mobile Education, Adult Apps, Topic Modelling, Latent Dirichlet Allocation (LDA), User Reviews.Full Text
References
- R. Ariana, “What you need to know about digital learning and transformation of education,” 2016. [Online]. Available: https://www.unesco.org/en/digital-education/needknow
- E. O. C. Mkpojiogu, A. Hussain, and M. O. Agbudu, “Security Issues in the Use of Mobile Educational Apps: A Review,” Int. J. Interact. Mob. Technol., vol. 15, no. 6, pp. 124–137, 2021, doi: 10.3991/ijim.v15i06.20631.
- B. Liu, J. Lin, and N. Sadeh, “Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?,” WWW 2014 - Proc. 23rd Int. Conf. World Wide Web, pp. 201–211, 2014, doi: 10.1145/2566486.2568035.
- S. Okuboyejo, “Examining Users’ Concerns while Using Mobile Learning Apps,” Int. J. Interact. Mob. Technol., vol. 15, no. 15, pp. 47–58, 2021, doi: 10.3991/ijim.v15i15.22345.
- Y. Singh and P. K. Suri, “An empirical analysis of mobile learning app usage experience,” Technol. Soc., vol. 68, no. January, p. 101929, 2022, doi: 10.1016/j.techsoc.2022.101929.
- M. Vásquez-Carbonell, “A Systematic Literature Review of Educational Apps: What Are They Up To?,” J. Mob. Multimed., vol. 18, no. 2, pp. 251–274, 2022, doi: 10.13052/jmm1550-4646.1825.
- X. L. Pham and G. D. Chen, “PACARD: A New Interface to Increase Mobile Learning App Engagement, Distributed Through App Stores,” J. Educ. Comput. Res., vol. 57, no. 3, pp. 618–645, 2019, doi: 10.1177/0735633118756298.
- S. Batista and G. Barcelos, “Considerations on the use of mobile phones in educational context,” Int. J. New Trends Educ. Their Implic., vol. 5, no. 1, pp. 1–10, 2014.
- T. Cherner, J. Dix, and C. Lee, “Cleaning up that mess: A framework for classifying educational apps,” Contemp. Issues Technol. Teach. Educ., vol. 14, no. 2, pp. 158–193, 2014, [Online]. Available: http://www.apptrace.com
- D. Menon, “Uses and gratifications of educational apps: A study during COVID-19 pandemic,” Comput. Educ. Open, vol. 3, p. 100076, 2022, doi: 10.1016/j.caeo.2022.100076.
- W. H. Wu, Y. C. Jim Wu, C. Y. Chen, H. Y. Kao, C. H. Lin, and S. H. Huang, “Review of trends from mobile learning studies: A meta-analysis,” Comput. Educ., vol. 59, no. 2, pp. 817–827, 2012, doi: 10.1016/j.compedu.2012.03.016.
- I. S. H. Wai, S. S. Y. Ng, D. K. W. Chiu, K. K. W. Ho, and P. Lo, “Exploring undergraduate students’ usage pattern of mobile apps for education,” J. Librariansh. Inf. Sci., vol. 50, no. 1, pp. 34–47, 2018, doi: 10.1177/0961000616662699.
- M. Meyer et al., “How educational are ‘educational’ apps for young children? App store content analysis using the Four Pillars of Learning framework,” J. Child. Media, vol. 15, no. 4, pp. 526–548, 2021, doi: 10.1080/17482798.2021.1882516.
- G. Falloon, “Young students using iPads: App design and content influences on their learning pathways,” Comput. Educ., vol. 68, pp. 505–521, 2013, doi: 10.1016/j.compedu.2013.06.006.
- A. A. Economides and A. Grousopoulou, “Mobiles in education: students’ usage, preferences and desires,” Int. J. Mob. Learn. Organ., vol. 4, no. 3, pp. 235–252, 2010, doi: 10.1504/IJMLO.2010.033553.
- B. Eschenbrenner and F. Fui-Hoon Nah, “Mobile technology in education: uses and benefits,” Int. J. Mob. Learn. Organ., vol. 1, no. 2, pp. 159–183, 2007, doi: 10.1504/IJMLO.2007.012676.
- C. C. Tossell, P. Kortum, C. Shepard, A. Rahmati, and L. Zhong, “You can lead a horse to water but you cannot make him learn: Smartphone use in higher education,” Br. J. Educ. Technol., vol. 46, no. 4, pp. 713–724, 2015, doi: 10.1111/bjet.12176.
- B. A. Kumar and S. S. Chand, “Mobile learning adoption: A systematic review,” Educ. Inf. Technol., vol. 24, no. 1, pp. 471–487, 2019, doi: 10.1007/s10639-018-9783-6.
- D. Mukherjee, A. Ahmadi, M. V. Pour, and J. Reardon, “An Empirical Study on User Reviews Targeting Mobile Apps’ Security & Privacy,” pp. 1–12, 2020, [Online]. Available: http://arxiv.org/abs/2010.06371
- W. Wang, Y. Feng, and W. Dai, “Topic analysis of online reviews for two competitive products using latent Dirichlet allocation,” Electron. Commer. Res. Appl., vol. 29, no. January, pp. 142–156, 2018, doi: 10.1016/j.elerap.2018.04.003.
- C. Liu and A. P. Correia, “A case study of learners’ engagement in mobile learning applications,” Online Learn. J., vol. 25, no. 4, pp. 25–48, 2021, doi: 10.24059/olj.v25i4.2827.
- C. D. Curiac and M. Micea, “Identifying Hot Information Security Topics Using LDA and Multivariate Mann-Kendall Test,” IEEE Access, vol. 11, no. February, pp. 18374–18384, 2023, doi: 10.1109/ACCESS.2023.3247588.
- M. Bahja and M. Lycett, “Identifying patient experience from online resources via sentiment analysis and topic modelling,” Proc. - 3rd IEEE/ACM Int. Conf. Big Data Comput. Appl. Technol. BDCAT 2016, pp. 94–99, 2016, doi: 10.1145/3006299.3006335.
- A. Ahmed et al., “Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach,” JMIR Form. Res., vol. 6, no. 3, pp. 1–12, 2022, doi: 10.2196/27654.
- O. Haggag, J. Grundy, M. Abdelrazek, and S. Haggag, “A large scale analysis of mHealth app user reviews,” Empir. Softw. Eng., vol. 27, no. 7, 2022, doi: 10.1007/s10664-02210222-6.
- O. Oyebode, F. Alqahtani, and R. Orji, “Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews,” IEEE Access, vol. 8, pp. 111141–111158, 2020, doi: 10.1109/ACCESS.2020.3002176.