Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Real-Time Stabilization of a Single Inverted Pendulum Using a Power Series Based Controller Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Exploring Critical Success Factors of Mobile Recommendation Systems: The End User Perspective

verfasst von : Yan Sun, Woon Kian Chong, Ka Lok Man, Seungmin Rho, Dejun Xie

Erschienen in: Transactions on Engineering Technologies

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This study is intended to critically explore key factors for the user experience of mobile recommendations, evaluate the findings, and use the generated critical success factors (CSFs) to propose a framework to assist in the Chinese mobile marketplace. The proposed framework provides a guideline for academics and practitioners and highlights the significant role of each factor in developing and sustaining effective mobile recommendation systems practice. The findings can help managers to derive a better understanding and measurement of mobile marketing activities that appropriately balance between traditional and mobile marketing practices. At the same time, the CSFs can be integrated into the companies to determine the level of marketing performance in mobile marketplace.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Delay Constraint CAC with Hierarchical DBA over GPON-LTE Converged Networks
Nächstes Kapitel Multimedia Management Services Based on User Participation with Collaborative Tagging
Literatur
1.
Pathak B, Garfinkel R, Gopal R, Venkatesan R, Yin F (2010) Empirical analysis of the impact of recommender systems on sales. J Manage Inf Syst 27(2):159–188CrossRef
2.
Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 40:10059–10072CrossRef
3.
Bobadilla J, Ortega F, Hernando A, Gutierrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109
4.
Konstan J, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User-Adap Inter 22(1/2):101–123CrossRef
5.
Gallego D, Woerndl W, Huecas G (2013) Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones. J Syst Archit 59(9):748
6.
Shani G, Gunawardana A (2013) Tutorial on application-oriented evaluation of recommendation system. AI Commun 26(2):225–236MathSciNet
7.
8.
Aldrich SE (2014) Recommender systems in commercial use. AI Mag 32(3):28–34
9.
Hostler RE, Yoon VY, Guimaraes T (2012) Recommendation agent impact on consumer online shopping: the movie magic case study. Expert Syst Appl 39(3):2989 (2012)
10.
Pu P, Li C, Kumar P (2008) Evaluating product search and recommender systems for E-commerce environments. Electron Commer Res 8(1/2):1–27CrossRefMATH
11.
Dabrowski M, Acton T (2013) The performance of recommender systems in online shopping: a user-centric study. Expert Syst Appl 40(14):5551
12.
Polatidis N, Georgiadis C (2013) Mobile recommender systems: an overview of technologies and challenges. In: Informatics and applications (ICIA), second international conference on IEEE, pp 282–287
13.
Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333CrossRef
14.
Joon YC, Hee SS, Soung HK (2007) MCORE: a context-sensitive recommendation system for the mobile Web. Expert Syst 24(1):32–46
15.
Xia X, Wang X, Li J, Zhou X (2014) Multi-objective mobile app recommendation: a system-level collaboration approach. Comput Electr Eng 40(1):203
16.
Jannach D (2011) Recommender systems: an introduction. Cambridge University Press, New York
17.
Owusu TD, Hoffman C (2014) The personalization and prediction innovation of mobile recommender systems. Issues Inf Syst 15(2):168–174
18.
Gao H, Wang S, Yang B, Yang H (2014) User preference-oriented collaborative recommendation algorithm in E-commerce. J Softw 9(7):1886
19.
Shen X, Sun Y, Wang N (2013) Recommendations from friends anytime and anywhere: toward a model of contextual offer and consumption values. CyberPsychol Behav Soc Network 16(5):349–356CrossRef
20.
Tintarev N, Masthoff J (2012) Evaluating the effectiveness of explanations for recommender systems. User Model User-Adap Inter 22(4/5):399–439CrossRef
21.
Pu P, Li C, Hu R (2012) Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User-Adap Inter 22(4/5):317–355CrossRef
22.
Liu Q, Ma H, Chen E, Xiong H (2013) A survey of context-aware mobile recommendations. Int J Inf Technol Decis Making 12(1):139–172CrossRef
23.
Knijnenburg B, Willemsen M, Gantner Z, Soncu H, Newell C (2012) Explaining the user experience of recommender systems. User Model User-Adap Inter 22(4/5):441–504CrossRef
24.
Polatidis N, Georgiadis C (2014) Factors influencing the quality of the user experience in ubiquitous recommender systems. In: Distributed, ambient, and pervasive interactions, pp 369–379
25.
Hor-Meyll LF, Correia LM, Ferreira JB (2014) Why should I accept ads on my mobile phone? Factors affecting the acceptance by Brazilian teenagers. Braz Bus Rev (English Edition) 11(4):130–150
26.
Oulasvirta A, Ratternbury T, Raita E (2012) Habits make smartphone use more pervasive. Pers Ubiquit Comput 16(1):105–114CrossRef
27.
Bryman A (2012) Social research methods. Oxford University Press, New York
28.
MarketLine Industry Profile (2014) Profile Global Mobile Phones April 2014. Mobile Phones Industry Profile: Global, pp 1–34
29.
MarketLine Industry Profile (2012) Mobile Phones in China September 2012, Mobile Phones Industry Profile: China, pp 1–35
30.
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil Mag 2(11):559–572CrossRefMATH
31.
Russell DW (2002) In search of underlying dimensions: the use (and abuse) of factor analysis in personality and social psychology bulletin. Pers Soc Psychol Bull 28(12):1629–1646CrossRef
32.
Wienclaw AR (2009) Correlation, research starters sociology. Res Starters, EBSCOhost, viewed 30 April 2015
33.
Hair JF, Black C, Babin J, Anderson E (1998) Multivariate data analysis. Prentice Hall, Upper Saddle River, N.J
34.
Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometricka 13:93–96
35.
Gliem J, Gliem R (2003) Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest research-to-practice conference in adult, continuing, and community education
36.
Kline P (2000) The handbook of psychological testing. Routledge, London
37.
George D, Mallery P (2003) SPSS for windows step by step: a simple guide and reference. Allyn & Bacon, Boston
38.
Bartlett MS (1950) Tests of significance in factor analysis. Br J Psychol 3:77–85
39.
Kaiser HF, Rice J (1974) Little jiffy, mark iv. Educ Psychol Measur 34(1):111–117CrossRef
40.
Brace N, Snelgar R, Kemp R (2012) SPSS for psychologists. Psychology Press, New York
41.
Kutner MH, Nachtsheim CJ, Neter J (2004) Applied linear regression models. McGraw-Hill Irwin, New York
42.
Bauer HH et al (2015) Driving consumer acceptance of mobile marketing: a theoretical framework and empirical study. J Electron Commer Res 6:181–192
43.
Sultan F, Rohm A, Gao T (2009) Factors affecting consumer acceptance of mobile marketing: a two-country study of youth markets. J Interact Mark 23:308–320CrossRef
44.
Tsang MM, Ho SC, Liang TP (2004) Consumer attitudes toward mobile advertising: an empirical study. Int J Electron Commer 8:65–78
45.
Zhang J, Mao E (2008) Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychol Mark 25(8):787–805CrossRef
46.
Sun Y, Chong K, Han YS, Rho S, Man KL (2015) Key factors affecting user experience of mobile recommendation systems. In: Lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists, 18–20 March, 2015, Hong Kong, pp 724–728
Metadaten
Titel
Exploring Critical Success Factors of Mobile Recommendation Systems: The End User Perspective
verfasst von
Yan Sun
Woon Kian Chong
Ka Lok Man
Seungmin Rho
Dejun Xie
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Transactions on Engineering Technologies

Print ISBN: 978-981-10-0550-3

Electronic ISBN: 978-981-10-0551-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-0551-0_4

Neuer Inhalt

    Bildnachweise