Skip to main content
Log in

When the technology abandonment intentions remitted: the case of herd behavior

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

The herd behavior of technology users, i.e., adopters observe the decisions (but not the reasoning) of prior adopters and imitate their system usage behaviors, is prominentin the information technology (IT) industry. However, such en mass adoptions areprone to be fragile. Adopters may reverse their decisions, after an initial adoption, andabandon system use when updated contradictory information is presented. This phenomenon of herd-like abandonment, at both individual and organizational levels, is significant and requires more investigation, since it is connected to the durability of particular products and technologies in the marketplace. In addition, IS behaviors of users at the later phases of IS life cycle, i.e., termination phase, are the primary source of benefit for organizations. This study develops and empirically validates a theoretical model of IS abandonment in a herding context. We test our model via a longitudinal research design, which surveys adopters at two different points in time. The study examines the determinants of adopters’ abandonment intentions, which occure specially after an initial en mass adoption (i.e., a herding setting). Results suggest that post-adoptive task-technology fit, perceptions of niche, and observation of a criticalmass of abandoners are all salient factors affecting IS abandonment intentions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Please note that we did not include the “discounting own information” construct in our herding model. This construct acts as a moderating factor and does directly associate with our outcome variable [73]. Hence, we only focused on “imitating others,” statistically controlling the potential effect of “discounting own information.”.

References

  1. Abrahamson E, Rosenkopf L (1993) Institutional and competitive bandwagons: using mathematical modeling as a tool to explore innovation diffusion. Acad Manag Rev 18(3):487–517

    Article  Google Scholar 

  2. Aiken LS, West SG (1991) Multiple regression: Testing and interpreting interactions. Sage, London, England

    Google Scholar 

  3. Aldunate R, Nussbaum M (2013) Teacher adoption of technology. Comput Hum Behav 29(3):519–524

    Article  Google Scholar 

  4. Alonso-Dos-Santos M, Jiménez MA, Carvajal-Trujillo E (2019) Facebook commerce usage intention: a symmetric and asymmetric approach. Inf Technol Manag 21(3):145–156

    Article  Google Scholar 

  5. Bagozzi R (2011) Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. MIS Q 35(2):261–292

    Article  Google Scholar 

  6. Bagozzi R, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16(1):74–94

    Article  Google Scholar 

  7. Banerjee A (1992) A simple model of herd behavior. Q J Econ 107(3):797–817

    Article  Google Scholar 

  8. Barasch A, Berger J (2014) Broadcasting and narrowcasting: how audience size affects what people share. J Mark Res 51(3):286–299

    Article  Google Scholar 

  9. Beaudry A, Pinsonneault A (2005) Understanding user responses to information technology: a coping model of user adaptation. MIS Q 29(3):493–524

    Article  Google Scholar 

  10. Bere A (2018) Applying an extended task-technology fit for establishing determinants of mobile learning: an instant messaging initiative. J Inf Syst Educ 29(4):239–252

    Google Scholar 

  11. Berger J, Heath C (2008) Who drives divergence? identity signaling, outgroup dissimilarity, and the abandonment of cultural tastes. J Pers Soc Psychol 95(3):593–607

    Article  Google Scholar 

  12. Bernhardt D, Campello M, Kutsoati E (2006) Who herds? J Financ Econ 80(3):657–675

    Article  Google Scholar 

  13. Bhattacherjee A, Lin CP (2015) A unified model of IT continuance: three complementary perspectives and crossover effects. Eur J Inf Syst 24(4):364–373

    Article  Google Scholar 

  14. Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100(5):992–1026

    Article  Google Scholar 

  15. Bhattacherjee A, Limayem M, Cheung CM (2012) User switching of information technology: a theoretical synthesis and empirical test. Inf Manag 49(7–8):327–333

    Article  Google Scholar 

  16. Bikhchandani S, Sharma S (2000) Herd behavior in financial markets. IMF Staff Pap 47(3):279–310

    Google Scholar 

  17. Bohl M, Branger N, Trede M (2017) The case for herding is stronger than you think. J Bank Financ 85(1):30–40

    Article  Google Scholar 

  18. Buhrmester MD, Talaifar S, Gosling SD (2018) An evaluation of amazon’s mechanical turk, its rapid rise, and its effective use. Perspect Psychol Sci 13(2):149–154

    Article  Google Scholar 

  19. Cao X, Khan AN, Ali A, Khan NA (2019) Consequences of cyberbullying and social overload while using SNSS: a study of users’ discontinuous usage behavior in SNSs. Inf Syst Front 21(3):1–14

    Google Scholar 

  20. Chen JV, Tran A, Nguyen T (2019) Understanding the discontinuance behavior of mobile shoppers as a consequence of technostress: an application of the stress-coping theory. Comput Hum Behav 95:83–93

    Article  Google Scholar 

  21. Chin W. (1998) The partial least squares approach to structural equation modeling in Marcoulides. GA (Ed) Modern Methods for Business Research Lawrence Erlbaum Associates, Mahwah, NJ, pp 295-336

  22. Cialdini R (1993) Influence: The psychology of persuasion. William Morrow, New York

    Google Scholar 

  23. Cingl L (2013) Does herd behaviour arise easier under time pressure? experimental approach. Prague Econ Pap 2013(4):558–582

    Article  Google Scholar 

  24. Connelly E, Zweig D, Webster J, Trougakos J (2012) Knowledge hiding in organizations. J Organ Behav 33(1):64–88

    Article  Google Scholar 

  25. D’Ambra J, Wilson CS, Akter S (2013) Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics. J Am Soc Inform Sci Technol 64(1):48–64

    Article  Google Scholar 

  26. Darban M, Polites GL (2020) Why Is It hard to fight herding? The roles of user and technology attributes. In: ACM SIGMIS Database: the DATABASE for Advances in Information Systems, vol 51, no 4, pp 93–122

  27. Davis F (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  28. Dellarocas C, Gao G, Narayan R (2010) Are consumers more likely to contribute online reviews for hit or niche products? J Manag Inf Syst 27(2):127–158

    Article  Google Scholar 

  29. Dimoka A (2010) What does the brain tell us about trust and distrust? evidence from a functional neuroimaging study. MIS Q 34(2):373–396

    Article  Google Scholar 

  30. Ding AW, Li S (2019) Herding in the consumption and purchase of digital goods and moderators of the herding bias. J Acad Mark Sci 47(3):460–478

    Article  Google Scholar 

  31. Dishaw MT, Strong DM (1999) Extending the technology acceptance model with task–technology fit constructs. Inf manag 36(1):9–21

    Article  Google Scholar 

  32. Doargajudhur MS, Dell P (2019) Impact of BYOD on organizational commitment: an empirical investigation. Inf Technol People 32(2):246–268

    Article  Google Scholar 

  33. Duan W, Gu B, Whinston A (2009) Informational cascades and software adoption on the internet: an empirical investigation. Manag Inf Syst Q 33(1):23–48

    Article  Google Scholar 

  34. Ello Manifesto (2014) https://ello.co/wtf/about/ello-manifesto/. Accessed 03 July 2019

  35. Festinger L (1962) A theory of cognitive dissonance (Vol. 2). Stanford university press

  36. Fiol C, O’Connor E (2003) Waking up! mindfulness in the face of bandwagons. Acad Manag Rev 28(1):54–70

    Article  Google Scholar 

  37. Furneaux B, Wade M (2010) The end of the information system life: a model of is discontinuance. Data Base Adv Inf Syst 41(2):45–69

    Article  Google Scholar 

  38. Furneaux B, Wade M (2011) An exploration of organizational level information systems discontinuance intentions. MIS Q 35(3):573–598

    Article  Google Scholar 

  39. Gerhart N, Peak DA, Prybutok VR (2015) Searching for new answers: the application of task-technology fit to E-Textbook usage. Decis Sci J Innov Educ 13(1):91–111

    Article  Google Scholar 

  40. Gierl H, Huettl V (2010) Are scarce products always more attractive? the interaction of different types of scarcity signals with products’ suitability for conspicuous consumption. Int J Res Mark 27(3):225–235

    Article  Google Scholar 

  41. Goldstone R, Janssen M (2005) Computational models of collective behavior. Trends Cogn Sci 9(9):424–430

    Article  Google Scholar 

  42. Goodhue D, Thompson R (1995) Task-technology fit and individual performance. MIS Q 19(2):213–236

    Article  Google Scholar 

  43. Hair J, Black W, Babin B, Anderson R, Tatham R (2009) Multivariate Data Analysis. Prentice hall, Upper Saddle River, NJ

    Google Scholar 

  44. Hagger MS, Chatzisarantis LS (2005) First-and higher-order models of attitudes, normative influence, and perceived behavioural control in the theory of planned behaviour. Br J Soc Psychol 44(4):513–535

    Article  Google Scholar 

  45. Hansen J, Saridakis G, Benson V (2018) Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Comput Hum Behav 80:197–206

    Article  Google Scholar 

  46. Hogarth RM, Einhorn HJ (1992) Order effects in belief updating: The belief adjustment model. Cogn Psychol 24(1):1–55

    Article  Google Scholar 

  47. Hou A, Shiau W (2019) Understanding Facebook to Instagram migration: a push-pull migration model perspective. Inf Technol People Vol. ahead of print No. ahead of print

  48. Hu L, Bentler P (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 6(1):1–55

    Article  Google Scholar 

  49. Hwang S, Salmon M (2004) Market stress and herding. J Empir Financ 11(4):585–616

    Article  Google Scholar 

  50. Jarupathirun S, Zahedi F (2007) Exploring the influence of perceptual factors in the success of web-based spatial DSS. Decis Support Syst 43(3):933–951

    Article  Google Scholar 

  51. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica. Journal of the Econometric Society 47(2):263–291

    Article  Google Scholar 

  52. Kim SS (2009) The integrative framework of technology use: an extension and test. MIS Q 33(3):513–537

    Article  Google Scholar 

  53. Kim SS, Malhotra NK (2005) A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena. Manage Sci 51(5):741–755

    Article  Google Scholar 

  54. Kim T, Suh YK, Lee G, Choi BG (2010) Modelling roles of task-technology fit and self-efficacy in hotel employees’ usage behaviours of hotel information systems. Int J Tour Res 12(6):709–725

    Article  Google Scholar 

  55. Larsen TJ, Sørebø AM, Sørebø Ø (2009) The role of task-technology fit as users’ motivation to continue information system use. Comput Hum Behav 25(3):778–784

    Article  Google Scholar 

  56. Lee Y, Hosanagar K, Tan Y (2015) Do I follow my friends or the crowd? information cascades in online movie ratings. Manage Sci 61(9):2241–2258

    Article  Google Scholar 

  57. Li X (2004) Informational cascades in IT adoption. Commun ACM 47(4):93–97

    Article  Google Scholar 

  58. Maier C, Laumer S, Weinert C, Weitzel T (2015) The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. Inf Syst J 25(3):275–308

    Article  Google Scholar 

  59. Maier C, Laumer S, Eckhardt A, Weitzel T (2015) Giving too much social support: social overload on social networking sites. Eur J Inf Syst 24(5):447–464

    Article  Google Scholar 

  60. Moore G, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222

    Article  Google Scholar 

  61. Negahban A, Chung C (2014) Discovering determinants of users perception of mobile device functionality fit. Comput Hum Behav 35:75–84

    Article  Google Scholar 

  62. Park C (2019) Exploring a new determinant of task technology fit: Content characteristics. J IntTechnol Inf Manag 27(3):100–118

    Google Scholar 

  63. Pollard C (2003) Exploring continued and discontinued use of IT: a case study of OptionFinder, a group support system. Group Decis Negot 12(3):171–193

    Article  Google Scholar 

  64. Raafat R, Chater N, Frith C (2009) Herding in humans. Trends Cogn Sci 13(10):420–428

    Article  Google Scholar 

  65. Rao H, Greve H, Davis G (2001) Fool’s gold: social proof in the initiation and abandonment of coverage by wall street analysts. Adm Sci Q 46(3):502–526

    Article  Google Scholar 

  66. Rhodes R, Courneya K (2003) Investigating multiple components of attitude, subjective norm, and perceived control: an examination of the theory of planned behaviour in the exercise domain. Br J Soc Psychol 42(1):129–146

    Article  Google Scholar 

  67. Sharma R, Yetton P, Crawford J (2009) Estimating the effect of common method variance: the method—method pair technique with an Illustration from TAM research. MIS Q 33(3):473–490

    Article  Google Scholar 

  68. Schaefers T (2014) Standing out from the crowd: niche product choice as a form of conspicuous consumption. Eur J Mark 48(9/10):1805–1827

    Article  Google Scholar 

  69. Shen X, Zhang K, Zhao S (2016) Herd behavior in consumers’ adoption of online reviews. J Am Soc Inf Sci 67(11):2754–2765

    Google Scholar 

  70. Shi J, Lai KK, Hu P, Chen G (2018) Factors dominating individual information disseminating behavior on social networking sites. Inf Technol Manage 19(2):121–139

    Article  Google Scholar 

  71. Soliman W, Rinta-Kahila T (2020) Toward a refined conceptualization of IS discontinuance Reflection on the past and a way forward. Inf Manag 57(2):103167

    Article  Google Scholar 

  72. Solon O (2018) Teens are abandoning Facebook in dramatic numbers, study finds. https://www.theguardian.com/technology/2018/jun/01/facebook-teens-leaving-instagramsnapchat-study-user-numbers. Accessed 18 Mar 2020

  73. Sun H (2013) A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Q 37(4):1013–1041

    Article  Google Scholar 

  74. Tang Z, Chen L, Gillenson ML (2019) Understanding brand fan page followers’ discontinuance motivations: a mixed-method study. Inf Manag 56(1):94–108

    Article  Google Scholar 

  75. Turel O (2015) Quitting the use of a habituated hedonic information system: a theoretical model and empirical examination of Facebook users. Eur J Inf Syst 24(4):431–446

    Article  Google Scholar 

  76. Vaghefi I, Qahri-Saremi H, Turel O (2020) Dealing with social networking site addiction: a cognitive-affective model of discontinuance decisions. Internet Res 30(5):1066–2243

    Article  Google Scholar 

  77. Van Slyke C, Ilie V, Lou H, Stafford T (2007) Perceived critical mass and the adoption of a communication technology. Eur J Inf Syst 16(3):270–283

    Article  Google Scholar 

  78. Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inform Syst Res 11(5):342–365

    Article  Google Scholar 

  79. Venkatesh V, Morris MG, Davis G, Davis F (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478

    Article  Google Scholar 

  80. Walden E, Browne G (2009) Sequential adoption theory: a theory for understanding herding behavior in early adoption of novel technologies. J Assoc Inf Syst 10(1):31–62

    Google Scholar 

  81. Xu P, Liu D (2019) Product engagement and identity signaling: the role of likes in social commerce for fashion products. Inf Manag 56(2):143–154

    Article  Google Scholar 

  82. Xu Y, Yang Y, Cheng Z, Lim J (2014) Retaining and attracting users in social networking services: an empirical investigation of cyber migration. J Strateg Inf Syst 23(3):239–253

    Article  Google Scholar 

  83. Zhang K, Chen X (2017) Herding in a P2P lending market: rational inference OR irrational trust? Electron Commer Res Appl 23(2):45–53

    Article  Google Scholar 

  84. Zhang X, Jiang S, Ordóñez de Pablos P, Lytras MD, Sun Y (2017) How virtual reality affects perceived learning effectiveness: a task–technology fit perspective. Behav Inf Technol 36(5):548–556

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mehdi Darban.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

1.1 Herd Simulation

1.1.1 Time 1 (Adoption Stage)

To create the situation for herding, the information should depict “how many adopters there are and who specifically has adopted the innovation” [38, p. 56].

The participants receive a message that not only states that Ello has been used by a lot of people, but also specifies some famous adopters. The treatment messages were composed based on information from Ello’s website and Mashable (a technology and social media blog).

The following message appears:

  1. (a)

    Number

    1. (a)

      Ello is getting 40,000 sign-ups per hour. The beginning of a mass migration from Facebook to another Ello (Forbes).

  2. (b)

    Identity

    1. (a)

      Some major companies in such as Apple, AUDI, Acura, McDonald, Domino’s, Taco Bell, Dr. Pepper, Harley-Davidson.

    2. (b)

      Here are some celebrity Ello users: Rihanna, Harry Styles, Ariana Grande, Joseph Gordon-Levitt, Ashley Greene, Blake Lively, Jared Leto.

1.2 Treatment

1.2.1 Time 2 (Post-Adoption Stage)

In order to study the effect of the mass of abandoners on post-adoption decision of individuals, we manipulate the magnitude of prior abandonments. A message was sent to the treatment group about the number of prior adopters of Ello who chose to stop using it and uses other alternatives. The treatment message provided a smaller number of abandoners, compared to the number of adopters in the simulation message (i.e., Time 1).

Appendix 2

2.1 Questionnaire Items

Please complete the following:

  • Age: _____ Years

  • Gender: M F

  • Education level:

  • Prior Experience: adapted from Kim and Malhotra [53]

  • How long have you been using Ello? (Never heard about it, Heard but never used it before, less than 1 months, 1 to less than 3 months, 3 to less than 6 months, 6 months or more).

  • Imitating others (IMI): adapted from Sun [73]

  • IMI1. I will follow others in accepting Ello.

  • IMI2. It is a good idea to follow others in using Ello.

  • IMI3. I like the idea of adopting Ello, since others are also using it.

  • IMI4. It seems that Ello is the dominant social networking website; therefore, I would like to use it as well.

  • Per-Adoptive and Post Task-technology fit (TTF): will be measured at both t1 and t2 (adapted from Goodhue and Thompson [42] and Jarupathirun and Zahedi [50]).

  • TTF1. Ello’s functions are very adequate.

  • TTF2. Ello’s functions are very appropriate for social networking.

  • TTF3. Ello’s functions are very useful for social networking.

  • TTF4. Ello’s functions are very compatible with social networking.

  • TTF5. Ello’s functions are very helpful.

  • TTF6. Ello’s functions are very sufficient.

  • TTF7. Ello’s functions make social networking very easy.

  • TTF8. In general, Ello’s functions fit social networking.

2.2 Time-2 Survey Items

2.2.1 Ello Usage (Screening) Items

  • Ell1. How many followers do you have on Ello?

  • Ell2. How many Ello accounts do you follow?

  • Ell3. How many times you have posted on Ello?

Perceived Niche (NCH): Self-developed.

  • NCH1. Ello is designed for a specific cluster of SNS users.

  • NCH2. Ello is distinct from other SNSs.

  • NCH3. Other more popular SNSs are not similar to Ello.

  • NCH4. There are characteristics that are specific to Ello.

Intention to Abandonment (ABD): adapted from Turel [74]

  • ABD1. I intend to abandon my use of Ello.

  • ABD2. I plan to stop using Ello.

  • ABD3. I predict that I would stop using Ello in the future.

2.3 Control Variables

2.3.1 Subjective Norm: Two aspects

Aspect one: Descriptive Norm (DN) (adapted from Hagger and Chatzisarantis [44]).

  • DN1. Most of my friends are using Ello.

  • DN2. Most of my co-workers are using Ello.

  • DN3. Most people I know are using Ello.

  • DN4. Most people who are important to me use Ello.

Aspect two: Injunctive Norm (IN) (adapted from Rhodes and Courneya [66]).

  • IN1. Most people in my social circle want me to use Ello.

  • IN2. Most people in my social circle approve of my using Ello.

  • IN3. Most people who are important to me want me to use Ello.

  • IN4. Most people I know think I should use Ello.

Network Effects (NE): adapted from Sun [73]

  • NE1. The more people use Ello, the more valuable it is to users.

  • NE2. By adopting Ello, I would help increase its value to other users.

  • NE3. My adoption of Ello would make it more useful for people I know who already use it.

  • NE4. I hope that more people will adopt Ello because that will increase the value of Ello to me.

  • NE5. Ello will be more useful if more people adopt it.

Discounting Own Information (DOI): adapted from Sun [73]

  • DOI1. My acceptance of Ello would not reflect my own preferences for social networking apps.

  • DOI2. If I were to use Ello for social networking, I wouldn’t be making the decision based on my own research and information.

  • DOI3: If I did not know that a lot of people have already accepted Ello, I might choose another social networking app.

2.4 Time 2-Manipulation Check Items

  • MCH2-1. I am aware that a lot of people have stopped using Ello.

  • MCH2-2. I am aware that Ello has been abandoned by a lot of well-known prior users.

2.5 Bogus Items

  1. 1.

    I have been to every country in the world.

  2. 2.

    I have never brushed my teeth.

  3. 3.

    All my friends are aliens.

Appendix 3

See Table 4.

Table 4 Descriptive statistics and inter construct correlations

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Darban, M., Kim, M. & Koksal, A. When the technology abandonment intentions remitted: the case of herd behavior. Inf Technol Manag 22, 163–178 (2021). https://doi.org/10.1007/s10799-021-00329-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-021-00329-5

Keywords

Navigation