You’ve got email! Does it really matter to process emails now or later?

Abstract

Email consumes as much as a quarter of knowledge workers’ time in organizations today. Almost a necessity for communication, email does interrupt a worker’s other main tasks and ultimately leads to information overload. Though issues such as spam, email filtering and archiving have received much attention from industry and academia, the critical problem of the timing of email processing has not been studied much. It is common for many knowledge workers to check and respond to their email almost continuously. Though some emails may require very quick responses, checking emails almost continuously may lead to interruptions in regular knowledge work. Managing email processing can make a significant difference in an organization’s productivity. Previous research on this topic suggests that perhaps the best way to minimize the effect of interruptions is to process email frequently for example, every 45 min. In this study, we focus on studying email response timing approaches to optimize the communication times and yet reduce the interruptive effects. We investigate previous recommendations by performing a two-phase study involving rigorous simulation experiments. Models were developed for identifying efficient and effective email processing policies by comparing various ways to reduce interruptions for different types of knowledge workers. In contrast to earlier research findings, results indicate that significant productivity improvements could be achieved through the use of some email processing policies while helping attain a balance between email response time and task completion time. Findings also suggest that the best policy may be to respond to email two to four times a day instead of every 45 min or continuously, as is common with many knowledge workers. We conclude by presenting many research opportunities for analytical and organizational IS researchers.

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References

  1. American Management Association. 2004. Workplace email and instant messaging survey. AMA Research. URL: http://www.epolicyinstitute.com/survey/survey04.pdf

  2. Axelrod, R. (2003). Advancing the art of simulation in the social sciences. Japanese Journal for Management Information System, 12(3), 1–19. Special Issue: Agent-Based Modeling.

    Google Scholar 

  3. Berghal, H. (1997). Email—the good, the bad and the ugly. Communications of the ACM, 40(4), 11–15.

    Article  Google Scholar 

  4. Chwif, L., Barretto, M., & Paul, R. (2000). On simulation model complexity. In J. A. Jones, R. R. Barton, K. Kang & P. A. Fishwick (eds.), Proceedings of 32nd Winter Simulation Conference, Orlando, Florida (pp. 449–454).

  5. Corragio, L. (1990). Deleterious effects of intermittent interruptions on the task performance of knowledge workers: A laboratory investigation. Unpublished Ph. D. thesis, U. of Arizona.

  6. Cutrell, E., Czerwinski, M., & Horvitz, E. (2000). Effects of instant messaging interruptions on computing tasks. In Extended Abstracts of CHI ’2000, Human Factors in Computing Systems, (The Hague, April 1–6, 2000), ACM press, 99–100.

  7. Czerwinski, M., Cutrell, E., & Horvitz, E. (2000). Instant messaging and interruption: Influence of task type on performance. In Paris, C., Ozkan, N., Howard, S. and Lu, S. (eds.), OZCHI 2000 Conference Proceedings, Sydney, Australia, Dec. 4–8, pp. 356–361.

  8. Davenport, T. H., & Beck, J. C. (2000). Getting the attention you need. Harvard Business Review, 78(5), 119–126.

    Google Scholar 

  9. Denning, P. (1982). Electronic junk. Communications of the ACM, 25(3), 163–165.

    Article  Google Scholar 

  10. Di Paolo, E. A., Noble, J., & Bullock, S. (2000). Simulation models as opaque thought experiments. In: M. A. Bedau, J. S. McCaskill, N. H. Packard & S. Rasmussen (eds.), Artificial Life VII: Proceedings of 7th International Conference on Artificial Life (pp. 497–506). Cambridge, MA: MIT Press.

  11. Ducheneaut, N., & Bellotti, V. (2001). E-mail as habitat. Interactions, 8(5), 30–38.

    Article  Google Scholar 

  12. Duchenaut, N., & Watts, L. (2005). In search of coherence: a review of email research. HCI Journal, 20(1, 2) (forthcoming).

  13. Hall, E. T., & Hall, M. R. (1990). Understanding cultural differences: Keys to success in West Germany, France and the United States. Yarmouth: Intercultural.

    Google Scholar 

  14. Hans-Joachim, M., Karsten, S., Florin, A., & Heinz, H. (2001). Computer simulation as a method of further developing a theory: simulating the elaboration likelihood model. Personality & Social Psychology Review, 5(3), 201–215.

    Article  Google Scholar 

  15. Her, C., & Hwang, S. (1989). Application of queuing theory to quantify information workload in supervisory control systems. International Journal of Industrial Ergonomics, 4, 51–60.

    Article  Google Scholar 

  16. Jackson, T., Dawson, R., & Wilson, D. (2001). The cost of email interruption. Journal of Systems and Information Technology, 5(1), 81–92.

    Article  Google Scholar 

  17. Jackson, T., Dawson, R., & Wilson, D. (2003). Understanding email interaction increases organizational productivity. Communications of the ACM, 46(8), 80–84.

    Article  Google Scholar 

  18. Jett, Q. R., & George, J. (2003). Work interrupted: a closer look at the role of interruptions in organizational life. Academy of Management, 28(3), 494–507.

    Google Scholar 

  19. Kahneman, D. (1973). Attention and effort. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  20. Kerr, B., & Wilcox, E. M. (2004). Designing remail: reinventing the email client through innovation and integration. CHI 2004, 24–29.

  21. Markus, M. L. (1994). Finding a happy medium: explaining the negative effects of electronic communication on social life at work. ACM Transactions on Information Systems, 12(2), 119–149.

    Article  Google Scholar 

  22. McFarlane, D. C. (2002). Comparison of four primary methods for coordinating the interruption of people in human-computer interaction. Human-Computer Interaction, 17(1), 63–139.

    Article  Google Scholar 

  23. Perlow, L. (1999). The time famine: towards a sociology of work time. Administrative Science Quarterly, 44(1), 57–81.

    Article  Google Scholar 

  24. Peschl, M. E., & Scheutz, M. (2001). Explicating the epistemological role of simulation in the development of theories of cognition. Proceedings of the seventh colloquium on Cognitive Science ICCS-01, 274–280.

  25. Sargent, R. G. (2003). Verification and validation of simulation models. In: S. Chick, P. J. Sánchez, D. Ferrin & D. J. Morrice (eds.), Proceedings of 2003 Winter Simulation Conference (pp. 37–48).

  26. Speier, C., Valacich, J., & Vessey, I. (1999). The influence of task interruption on individual decision-making: an information overload perspective. Decision Sciences, 30(2), 337–360.

    Article  Google Scholar 

  27. Speier, C., Vessey, I., & Valacich, J. (2003). The effects of interruptions, task complexity, and information presentation on computer-supported decision-making performance. Decision Sciences, 34(4), 623–812.

    Article  Google Scholar 

  28. Te’eni, D. (2001). Review: a cognitive-affective model of organizational communication for designing IT. MIS Quarterly, 25(2), 251–312.

    Article  Google Scholar 

  29. Trafton, J. G., Altmann, E. M., Brock, D. P., & Mintz, F. E. (2003). Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal. International Journal of Human-Computer Studies, 58, 583–603.

    Article  Google Scholar 

  30. Venolia, G., Dabbish, L., Cadiz, J. J., & Gupta, A. (2001). Supporting email workflow. Microsoft Research Tech Report MSR-TR-2001-88.

  31. Weber, R. (2004). A grim reaper: the curse of email. MIS Quarterly, 28(3), iii–xiii.

    Google Scholar 

  32. Welch, P. D. (1983). The statistical analysis of simulation results. In S. S. Lavenberg (Ed.), The computer performance modeling handbook. NY: Academic.

    Google Scholar 

  33. Whittaker, S., Bellotti, V., & Moody, P. (2005). Introduction to the special issue on revisiting and reinventing email. HCI Journal, 20(1–2) (forthcoming).

  34. Winsberg, E. (2003). Simulated experiments: methodology for a virtual world. Philosophy of Science, 70(1), 105–121.

    Article  Google Scholar 

  35. Zijlstra, F. R. H., Roe, R. A., Leonova, A. B., & Krediet, I. (1999). Temporal factors in mental work: effects of interrupted activities. Journal of Occupational and Organizational Psychology, 72(2), 163–185.

    Article  Google Scholar 

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Correspondence to Ashish Gupta.

Appendices

Appendix I

Table A.1 Email types, processing time of email and primary task

Appendix II. The mathematical model

Table A.2 Parameters used in experiment phase I

Notation

i type of email message i = 1,2..I
{i = 1 for SPAM,
i = 2 for Irrelevant,
i = 3 for Read only,
i = 4 for Reply}
j urgency (priority) of message j = 1,2..J
{j = 1 for Urgent (Priority-1),
j = 2 for within Business Day (Priority-2),
j = 3 for within 1 week (Priority-3),
j = 4 for Irrelevant}
k category of processing need k = 1,2..K
{k = 1 for <1 min,
k = 2 for 1–10 min,
k = 3 for >10 min}
d day d = 1,2..D
s sequence number s = 1,2..S
t time period of day t = 1,2..T
{t = 1 for 8:00 a.m. until 10:00 a.m.,
t = 2 for 10:00 a.m. until 12:00 p.m.,
t = 3 for 12:00 p.m. until 2:00 p.m.,
t = 4 for 2:00 p.m. until 4:00 p.m.,
t = 5 for 4:00 p.m. until 6:00 p.m.,
t = 6 for 6:00 p.m. until 8:00 a.m.}
X email processing strategy employed
λ ijkt arrival rate of email messages of type i, urgency j, processing need k, occurring during time period t
P kds random variable that represents the processing time required for email of type k, occurring on day d, having sequence number s
p kds E(P kds )
\( {f_{kds}}^P\left( {\hbox{x}} \right) \) probability density function (pdf) of P kds
R ds random variable that represents the resumption lag occurring on day d, sequence number s
r ds E(R ds )
\( {f_{ds}}^R\left( {\hbox{x}} \right) \) pdf of R ds
L ds random variable that represents the interruption lag occurring on day d, sequence number s
l ds E(L ds )
\( {f_{ds}}^L\left( {\hbox{x}} \right) \) pdf of L ds
Q d random variable that represents the threshold of productive work (email processing and primary work) to be completed on day d
q d E(Q d )
\( {f_d}^Q\left( {\hbox{x}} \right) \) pdf of Q d
Wq js email’s wait in the queue (time spent waiting for the knowledge worker’s attention) for email of urgency j having sequence number s
Ws js email’s wait in the system (email resolution time) for email of urgency j having sequence number s
\( W{s_{js}} = W{q_{js}} + {P_{kds}} \)
\( \overline W {s_{js}} \) mean email resolution time for email of urgency j
\( \overline W {s_j} = {\sum_s}W{s_{js}}/S \)
Y d total email processing occurring on day d
\( {Y_d} = {\sum_k}{\sum_s}{P_{kds}} \)
Z d total amount of primary work completed on day d
G d total lag time occurring on day d
\( {G_{\rm{d}}} = {\sum_{\rm{s}}}{L_{ds}} + {\sum_{\rm{s}}}{R_{ds}} \)
H d total hours worked by the knowledge worker on day d
\( {H_d} = {Y_d} + {Z_d} + {G_d} \)
\( \overline H \) mean hours worked by the knowledge worker
\( {\sum_d}{H_d}/D \)
E d knowledge worker efficiency occurring on day d
\( {E_d} = \left( {{Y_d} + {Z_d}} \right)/{H_d} \)
\( \overline E \) mean knowledge worker efficiency
\( {\sum_d}{E_d}/D \)
Q d threshold of productive work (email processing and primary work) to be completed on day d
\( {Q_d} < = {Y_d} + {Z_d} \)

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Gupta, A., Sharda, R. & Greve, R.A. You’ve got email! Does it really matter to process emails now or later?. Inf Syst Front 13, 637–653 (2011). https://doi.org/10.1007/s10796-010-9242-4

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Keywords

  • Email management
  • Interruption
  • Performance
  • Simulation modeling