Dissertation Proposal

1
Dissertation Proposal

• Title A study of performance and effort
  expectancy factors among generational and gender
  groups to predict enterprise social software
  technology acceptance
• Presented by Sunil Patel

2
Background / Need for the Study / Purpose of the
Study

• Background Social software usage in
  non-business contexts has risen significantly in
  the last decade
• Web 2.0 software technology gives rise to
  Enterprise Social Software (ESS)
• Companies across industries are increasingly
  investigating ESS for usage in the context of
  business to support business objectives such as
  enhancing employee productivity
• Need for the study Technology adoption
  (acceptance) is a critical success factor to
  successful IT delivery
• Ample research literature exists on general
  technology acceptance, but little exists on IT
  managers perceptions of ESS technology
  acceptance
• Age and gender have shown differing patterns on
  technology acceptance
• Purpose of the study Examine IT managers
  perceptions of ESS usefulness (PU), ease of use
  (PEOU), and behavioral intention (BI) to use ESS
  to determine if differences exist between the
  managers, generations or gender types or if
  relationships exist with age, gender

Pg. 1-11
3
Research Questions
Link Detailed Hypotheses

• IT Acceptance Factors Is there a relationship
  between variables of IT managers' behavioral
  intention to use ESS, perceived usefulness, and
  perceived ease of use? Is there a moderating
  variable?
• Age Is there a relationship or difference
  between IT managers' age and generational groups
  and the variables of perceived usefulness,
  perceived ease of use, and behavioral intention
  to use ESS?
• Gender Is there a relationship or difference
  between IT managers' gender and the variables of
  perceived usefulness, perceived ease of use, and
  behavioral intention to use ESS?
• All variables Is there a relationship or
  difference between IT managers' behavioral
  intention to use ESS and the variables of age,
  generation, gender, perceived usefulness, and
  perceived ease of use?

Pg. 11-12
4
Literature Review
Link Theoretical Model
Link Variables / Analyses

• IT Acceptance Factors Perceived usefulness
  (PU), Perceived Ease of Use (PEOU), and
  Behavioral Intention (BI) to use ESS
• Studies indicate individuals are more apt to use
  technology to the extent it will (a) increase
  performance through usefulness and (b) decrease
  effort required through ease of use
• Technology acceptance factors in the context of
  IT / social software
• Social software Lane Coleman, 2011 Wattal,
  Racherla Mandviwalla, 2009
• General IT and voluntariness Brown, Massey,
  Montoya-Weiss Burkman, 2002
• IT and productivity enhancement Lehr
  Lichtenberg, 1999
• Age and Generational Groups / Technology
  Acceptance
• Aging workforce as a business dynamic
• Studies indicate differing IT acceptance patterns
  among generational groups
• Generational cohort-groups theorized to have
  differing patterns of identifying traits (Strauss
  Howe, 1994)
• Online communities and ubiquitous technologies
  (Chung et al., 2010)
• Other studies supporting age as moderating factor
  in IT acceptance decisions (Morris Venkatesh,
  2000 Morris, Venkatesh Ackerman, 2005)

Pg. 16-22
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Literature Review, cont.
Link Theoretical Model
Link Variables / Analyses

• Gender Types / Technology Acceptance
• One of the first studies on the influence of
  gender on IT acceptance factors performed just
  over 14 years ago
• Research supports gender differences with general
  technology acceptance although little empirical
  data exists in context of enterprise social
  software
• Gender differences on acceptance of e-mail
  technology (Gefen Straub, 1997)
• Differing salience to technology usage and ease
  of use between gender types (Minton Schneider,
  1980 Morris, Venkatesh Ackerman, 2005
  Venkatesh Morris, 2000 Wattal, Racherla
  Mandviwalla, 2009)

Pg. 22-23
6
Methodology
Link Theoretical Model
Link Variables / Analyses

• Correlation-research design
• IT managers in the U.S. are in scope for this
  study
• Population consists of over 288,000 IT Managers
  (U.S. BLS, 2010)
• Sample size of 384 necessary based on alpha set
  to .05 and power set to .80
• Instrumentation
• Perceived Usefulness Ease of Use scale (Adapted
  from Venkatesh Davis, 1996)
• Item grouping and analysis did not indicate
  artificial inflation or deflation of reliability
  / validity (Davis, Bagozzi Warshaw, 1989 Davis
  Venkatesh, 1996)
• Validity and reliability are consistent through
  numerous replication studies
• Adams, Nelson Todd 1992 Davis, Bagozzi
  Warshaw, 1989 Hendrickson, Massey Cronan 1993
  Igbaria Iivari, 1995 Segars Grover 1993
  Subramanian, 1994 Szajna, 1994
• Reliability Cronbachs alpha remained at over
  .90 in above listed studies
• Validity High discriminant / factorial validity
  as measured by correlation coefficient (r)

Pg. 26-31
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Methodology, cont.
Link Theoretical Model
Link Variables / Analyses
Link Detailed Hypotheses

• Data Collection Procedures
• Online panel research survey firm to collect data
  (e.g. ResearchNow, Qualtrics)
• Recruitment email sent to panel participants
  meeting the criteria specified for studys
  population (i.e. IT managers in U.S.)
• Survey open 45 days or until minimum number of
  valid responses received
• Data Analysis
• Independent and Dependent Variables List
  (Reference Table 4, p. 34)
• Run data for descriptive, inferential, and
  multivariate analyses
• Tests of statistical significance (significant at
  p lt .05)
• Pearsons r (Ho1a, Ho1b, Ho2a, Ho3a, Ho4a)
• Wilks Lambda for MANOVAs (Ho2b, Ho3b, Ho4b)

Pg. 32-42
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Status and Next Steps

• Human Subjects Approval Status
• IRB Approval granted on April 19, 2012 (No.
  12-192)
• Next Steps
• Proceed with online panel research survey firm to
  publish informed consent notice and instrument
  items
• Collect data, complete Chapters 4 and 5
• Review, schedule dissertation defense (July)
• Seek publication
• Option 1 Performance Improvement Quarterly
  (PIQ)
• Option 2 Human Resource Development Quarterly
  (HRDQ)

9
Backup
10
Theoretical Framework
Link Literature Review
Link Methodology
Link Variables / Analyses
Pg. 33
11
Hypotheses Analysis and Variable Types
Link Literature Review
Link Methodology
Link Theoretical Model
Pg. 33
12
Hypotheses Analysis and Variable Types, cont.
Link Literature Review
Link Methodology
Link Theoretical Model
Pg. 34
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Hypotheses Analysis and Variable Types, cont.
Link Literature Review
Link Methodology
Link Theoretical Model
Pg. 35
14
Research Questions and Hypotheses
Link Research Questions
Link Methodology
Link Theoretical Model

• Is there a relationship between variables of IT
  managers' behavioral intention to use ESS
  technology, perceived usefulness, and perceived
  ease of use?
• Ho1a There is no statistically significant
  relationship between IT managers' perceived
  behavioral intention to use ESS technology and
  variables of perceived usefulness and perceived
  ease of use.
• Ho1b IT managers' perceived ease of use is not
  positively related to perceived usefulness.
• Is there a relationship or difference between IT
  managers' age and generational groups and the
  variables of perceived usefulness, perceived ease
  of use, and behavioral intention to use ESS
  technology?
• Ho2a There is no statistically significant
  relationship between IT managers' behavioral
  intention to use ESS technology and the variables
  of perceived usefulness, perceived ease of use,
  and age.
• Ho2b There is no statistically significant
  difference between IT managers' generational
  groups and the variables of perceived ease of
  use, perceived usefulness, and behavioral
  intention to use ESS technology.

Pg. 11-12
15
Research Questions and Hypotheses, cont.
Link Literature Review
Link Methodology
Link Theoretical Model

• Is there a relationship or difference between IT
  managers' gender and the variables of perceived
  usefulness, perceived ease of use, and behavioral
  intention to use ESS technology?
• Ho3a There is no statistically significant
  relationship between IT managers' behavioral
  intention to use ESS technology and the variables
  of perceived usefulness, perceived ease of use,
  and gender.
• Ho3b There is no statistically significant
  difference between IT managers' gender and the
  variables of perceived ease of use, perceived
  usefulness, and behavioral intention to use ESS
  technology.
• Is there a relationship or difference between IT
  managers' behavioral intention to use ESS
  technology and the variables of age, gender,
  perceived usefulness, and perceived ease of use?
• Ho4a There is no statistically significant
  relationship between IT managers' behavioral
  intention to use ESS technology and the variables
  of perceived usefulness, perceived ease of use,
  age, and gender.
• Ho4b There is no statistically significant
  difference between IT managers' generational
  groups and gender types and the variables of
  perceived usefulness, perceived ease of use, and
  behavioral intention to use ESS technology.

Pg. 12