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Technology use on the front line: how information technology enhances individual performance

  • Original Empirical Research
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Abstract

This study explores and tests a new model that links different types of technology usage to individual-level outcomes. The primary objective of this study is to examine the effects of efficient use (routinization) and effective use (infusion) along with the traditional measure of usage—namely, frequency of use—on two dimensions of individual-level outcomes: information technology-enabled administrative performance and information technology-enabled salesperson performance. To maintain consistency with the existing literature, the authors examine the effects of predeployment attitude toward or acceptance of technology and pre-deployment intended use of technology. The authors discuss managerial implications and provide directions for future research.

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Notes

  1. Under the umbrella of structural equation modeling are two main approaches: covariance-based (which is found in software such as LISREL, AMOS, and EQS) and PLS (which is found in software such as PLS-Graph).

  2. As a guideline, Chin (1998, p. 325) stated, “Standardized loadings should be greater than 0.707....But it should also be noted that this rule of thumb should not be as rigid at early stages of scale development. Loadings of 0.5 or 0.6 may still be acceptable if there are additional indicators in the block for comparison basis.”

  3. This is also consistent with Fornell and Larcker’s (1981) recommendation that the average variance extracted should be larger tha the square of the correlations (i.e., equivalent to a monotonic power transformation of numbers in the table).

  4. We also tested the mediating role of intention and frequency of use on prior attitudes and found that it was consistent with the traditional attitude–behavior literature.

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Author information

Authors and Affiliations

  1. Department of Marketing, Law and Social Responsibility, Loyola College in Maryland Sellinger School of Business, 4501 North Charles Street, Baltimore, MD, 21210, USA

    Suresh Sundaram

  2. Department of Information Systems and Decision Sciences, E. J. Ourso College of Business Administration, Louisiana State University, Baton Rouge, LA, 70803, USA

    Andrew Schwarz

  3. Department of Marketing and Entrepreneurship, Bauer College of Business, Sales Excellence Institute University of Houston, Houston, TX, 77204-6021, USA

    Eli Jones

  4. Department of Decision and Information Sciences, Bauer College of Business, University of Houston, Houston, TX, 77204-6021, USA

    Wynne W. Chin

Authors
  1. Suresh Sundaram
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  2. Andrew Schwarz
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  3. Eli Jones
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  4. Wynne W. Chin
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Corresponding author

Correspondence to Eli Jones.

Additional information

Authors are listed in reverse alphabetical order and contributed equally. The authors thank the editor; four anonymous JAMS reviewers; and Carl Herman, a former Siebel executive and current Director of Partner Relations for the Sales Excellence Institute, for their helpful comments on a previous draft of this manuscript.

Appendices

Appendix

Projected extent of use

Table 3 Definition: the a priori frequency with which the salesperson projects that he or she will use the technology (Azjen, 1985; Davis, 1989; Taylor & Todd, 1995)
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Pre-deployment attitude

Table 4 Definition: the a priori overall attitude toward the usage of the new system (Davis, 1989; Davis et al., 1989)
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Frequency of use

Table 5 Definition: the frequency with which the salesperson uses the technology (Taylor & Todd, 1995)
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Routinization

Table 6 Definition: the extent to which the use of the technology has been integrated into the salesperson’s normal work routine (Saga & Zmud, 1994)
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Infusion

Table 7 Definition: the extent to which a salesperson fully uses the tecnology to enhance his or her productivity (Jones et al., 2002)
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IT-enabled administrative performance—indicators modeled as formative variables

Table 8 Definition: the extent to which the technology affects the quality of the salesperson’s call planning and time and expense management (created for this study)
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IT-enabled salesperson performance—indicators modeled as formative variables

Table 9 Definition: the extent to which the technology affects the quality of the salesperson’s ability to produce key sales results (created for this study)
Full size table

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Sundaram, S., Schwarz, A., Jones, E. et al. Technology use on the front line: how information technology enhances individual performance. J. of the Acad. Mark. Sci. 35, 101–112 (2007). https://doi.org/10.1007/s11747-006-0010-4

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  • DOI: https://doi.org/10.1007/s11747-006-0010-4

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