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Dissertation Defense

Final Version of Dissertation Defense

Peter Boumgarden

on 7 September 2016

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Transcript of Dissertation Defense

Now that's promising!
A social influence model of innovation evaluation Evaluative Heuristic From where does innovation in organizations come? Importance & Difficulty Idea Generation
Idea Evaluation Internal Selection
External Selection Internal Selection
Idea Evaluation Individuals in Organizations Literature Reviews Structure of the Contemporary Organization Innovation Ambiguity
& Social Influence Idea Evaluation THEORY George, 2008
Amabile, 1996
Baer, 2008
Sawyer, 1996
Burt, 2004 Blain & Mumford, 2007
Dailey & Mumford, 2006
Lonergan, Scott & Mumford, 2004
Kozbelt, 2007
Simonton, 2003 Haveman, 1993
Hannan & Freeman, 1989
Carroll & Hannan, 2000
Tushman & Anderson, 1986 Bower, 1970
Burgelman, 1983; 1992; 2002
Noda & Bower, 1996
Henderson & Stern, 2002
"a firm may be internally diverse, with a portfolio of projects and technologies that arise through bottom-up initiatives" (Henderson & Stern, 2004: 40). "the application of judgement to the generated options to select the most significant options" (Basadur, Runco & Vega, 2000: 80). TO UNDERSTAND INNOVATION IN ORGANIZATIONS, WE HAVE TO KNOW HOW ALL INDIVIDUALS EVALUATE THE INITIATIVES CURRENTLY UNDER DEVELOPMENT "Leaders sort through a mass of ideas to find the ones that fit into a coherent whole- that support the story- which is a very difficult task. It's like an archaelogical dig where you don't know what you're looking for or whether you will even find something. The process is downright scary."

-Ed Catmull, CEO Pixar "The real challenge is developing a system to do the research to identify those things that are going to be high value in the first place, and to screen out those things that are low value and not adopt them as quickly as we have in the past... I don't know any country that has done it very well so far, because new innovation is just so complex and hard to predict."

Heath Care Economist, Glenn Melnick (2009) System Premise:
Rational-Expertise Model of Evaluation

Empirical Process:
Typically an assessment of an individual's ability to identify the objective novelty (reverse code: frequency) and usefulness (external raters) of idea

Context shapes hueristic of judgment

Effectiveness related to:
Skills such as divergent thinking (Runco, 1991; 1993; 1994)
Training (Kaufman, Baer, Cole & Sexton, 2008)
Processes such as delay of judgment (Basadur, Runco & Vega, 2000)

Psychological processes shape evaluation and judgment

The future value of innovation is a product of historically-shaped market dynamics (Popper, 1963) and uncertain customer demand

Empirical Process:
Assess the relationship between evaluation/ selection and outcome in actual market environment (e.g. academic creativity- Simonton, 1997; 1999; 2003)

Production of high-quality contributions most associated with total productivity, regardless of success
Time pattern of contributions is random and Poisson distrubted, a distribution when probability is low and the number of trails is high
Individuals do not show significant ability to improve in selection over time
Groups show a similar inability to select for their best ideas (Rietzschel, Nijstad & Stroebe, 2006).

Innovation's future value appears to be fundamentally ambiguous.
Given a lack of objective markers of value, its evaluation should be highly amenable to social influence (Festinger, 1950; Pfeffer, Salancik & Leblebici, 1976) Premise:
Organizations are a mix of formal and informal structural elements
The contemporary organization has been reorganized with greater subunit autonomy (Zenger & Hesterly, 1997) and the frequent use of project teams (Edmondson, 1999; Senge, 1990)

Formal Structure:
Project Teams
Organizational Subunits

Informal Structure:
Networks between individuals shape the passing of information, with corresponding influence on judgment and/or organizational attitudes (Friedkin, 1998) Typical Approaches
Characteristics of Product (e.g. Novelty/ Usefulness)
Input/ Output of Development
Quantitative Holistic Judgment (NPV)
Qualitative Holistic Judgment (Kuipers, Moskowitz & Kassinger, 1988)

Promise- a holistic evaluation of the future value to an investor (e.g. individual, group, organization) of an innovative initiative not yet developed.
Addressed informally by Scardamalia & Bereiter (1993)

Function of:
Match to Goals
Match to Capabilities:

And demonstration of:
Direct Return Potential
Generative Return Potential Formal Structure- Group Formal Structure- Subunit Informal Structure- Network Possible Effect of Involvement:
Involvement -> greater information -> less bias
Involvement -> attachment -> greater bias (Guler 2007)
Innovation's future value is ambiguous
Ambiguity should weaken the link between information and judgment accuracy
Supported by work on fundamental attribution error coupled with overconfidence (Runco, 1993; Lovallo & Kahneman, 2003), committment to a course of action (Staw, 1976; Teger, 1980), and sensemaking as committed interpretation (Weick, 2001)

Bias Amplification:
Sensemaking as committed interpretation should increase as a function of social relationships around individual (Weick, 2001)
Greater status -> greater committed interpretation -> more bias towards project when involved
Greater status -> more threat from outside projects -> more bias against project when not involved (Menon, Thompson & Choi, 2007)
Should not matter whether status is from diffuse or specific characteristics (Bunderson, 2003)

In-Group by Subunit:
Not-Invented-Here syndrome (Katz & Allen, 1982) based on an application of social identity theory (Sherif, 1966; Tajfel, 1977)
Individuals tend to have a general bias towards the in-group, though recent research has shown has this is not always the case (Menon, Thompson & Choi, 2006; Tsai, 2002)

Bias Suppression:
Manifestations of in-group bias are contingent on a strong identification with the in-group (Ashforth & Mael, 1989)
Individuals might meld diverse experiences into more holistic identifies (Ashforth, 2007; Sluss & Ashforth, 2008), thus decreasing identification with in-group.
Such exposure and melding could be the case the more one interacts with individuals from a diverse set of subunits across the organization
Social Influence through Networks?
Social influence suggests that attitudes are a function of exogenous factors plus the attitudes of one's peers, as individuals converge for a variety of reasons towards the opinion of those with whom they interact (Friedkin, 1998)
Some organizational attitudes have proven resistant to social influence (Ibarra & Andrews, 1993; Rice & Aydin, 1991)
Others argue similarity amongst network a function of selecting for similar others (McPherson, Smith-Lovin & Cook, 2001).
Dissonance Reduction by Social Influence:
The value of innovation is ambiguous, and relevant knowledge is distributed.
Both points suggest individuals will look to others in absence of objective cues of value, influenced over and above more exogenous cues (Friedkin, 1998).

Social Cohesion:
Socially cohesive environments make information more likely to be shared (Reagans & McEvily, 2003), more likely to be trusted (Ferrin, Dirks, & Shah, 2006), and should have greater influence on the direction of dissonance reduction.

Alter Awareness:
Individuals might also be motivated by accuracy concerns in addition to dissonance reduction (Wood, 2000), and thus also pay attention to the relevance an alter brings about the initiative in question.

Conceptualizing Innovation in Organizations
The Importance & Difficulty of the Process
Literature Review
Proposed Evaluative Heuristic- Promise
Theory Development
Data & Methods
Conclusion & Discussion Multi-National Fortune 500 Agribusiness Company
5 Business Subunits (North America, South America, Europe, Asia, Long-Term Innovation Group)
Subunit size 3-37 members
Project Team Division
Data Structure
Longitudinal Network (T1- Oct 2008, T2- Oct 2009)
81 scientists at Time 1 (76%), 72 Scientists at Time 2 (74%), 54 Scientists T1T2 (67%)
12 projects identified for evaluation (3-NA, 3-SA, 3-EU, 3-LongTerm)
Promise T2
Promise T1
Work Experience (years)
Gender (1- male, 2- female)
Organizational Position (1- scientist, 2- manager, 3- director)
Education (1 = bachelors... 5 = doctorate)
Measures for Hypothesis Testing:
Involve (0,1)
Status (Reputation as Innovator, Work Experience)
Initiative Home Subunit Location (0,1)
Alter Subunit Location Diversity
Alter Evaluations- T1 (Equal)
Alter Evaluations- T1 (Cohesion)
Alter Evaluations- T1 (Awareness)

NA NA NA SA SA SA EU EU EU LT LT LT Information Network Cohesion Network Initiative Awareness Initiative Evaluation
"Promise" 1 1 1 1 0 0 0 0 1 0 1 1 1 0 6 6 4 5 1 0 0 5 1 0 0 5 1 2 3 4 1 0 1 0 1 0 2 1 1 0 1 0 1 0 1 1 1 0 1 0 Formal Structure- Moderation Status as work experiernce positively moderates the relationship between involvement in an initiative and one's evaluation of its promise ( = .059, p < .01)

Simple Slope:
Non-significant @ 1 SD below
Positive & significant @ mean
Positive & significant @ 1 SD above Subunit Network Location Diversity positively moderates the relationship between a project being in one's home subunit and their evaluation of its promise ( = -1.257, p <.05)

Simple Slope:
Non-significant @ 1 SD below (p=.108)
Non-significant @ mean
Positive & moderately significant @ 1 SD above (p=.079) Informal Structure:
The Shape of Social Influence Promise & Nesting Formal Main Effect 0 0 0 0 0 2 1 1 1 1 Controls "Projects must display a value factor that aligns with us organizationally."

"It must be technically and operationally feasible... it has to pass through stages of confirmation."

"It has to have economic feasibility, payback potential."

"I prefer initiatives that allow us the possibility to learn about new technology." Promise Measures (Likert 1-7):
-This project shows significant promise as an innovative opportunity for (organization name)
-This project directly matches (organization name)'s goals.
-This project directly matches (organization name)'s capabilities.
-This project demonstrates great future revenue potential.
-This project points to other significant future possibilities. Awareness Measure:

To what extent were you aware of (Project Name)?

1- not aware
2- heard of project but unaware of details
3- some awareness of project details
4- significant awareness, but not involved
5- personally involved on project
H1: Individual involvement in an initiative is positively related to an individual's evaluation of that initiative's promise. H2: Individual status moderates the relationship between involvement and promise; specifically, the relationship between involvement and promise will be stronger (i.e., more positive) when status is high than when it is low. H3: Initiatives from one's formal organizational subunit will be seen as more promising than initiatives from outside of one's formal organizational subunit. H4:The subunit location diversity of one's alters moderates the relationship between the subunit location of an initiative and an individual's evaluation of its promise; specifically, the relationship between project home subunit location and promise will be weaker (i.e. less positive) when they communicate with individuals from a more rather than less diverse set of subunit locations. H5: An ego's evaluation of an initiative's promise is positively related to the aggregate promise evaluations of those individuals to whom he or she goes for information. H6a: Compared to an equal weighting model (H5), an ego's evaluation of an initiative's promise is more positively related to the aggregate promise evaluations of those individuals to whom he or she goes for information when alter opinions are weighted by their social cohesion with the ego. H6b: Compared to an equal weighting model (H5), an ego's evaluation of an initiative's promise is more positively related to the aggregate promise evaluations of those individuals to whom he or she goes for information when alter opinions are weighted by their project awareness. Promise:
Multi-level factor analysis (Van der Vegt & Janssen, 2003)
Correlated Loadings Across Groups (r=.66)
Correlated Loadings Across Time (r = .60)
By Projects (ICC < .01)
By Individuals (ICC = .352)
Nest only within individuals, given more variance across rather than within individual evaluators
Model Fit:
AIC= 1511 Promise Evaluations at Time 2 significantly related to:
Promise Time 1 ( = .459, p < .001)
Gender ( = -.442, p < .05)
Work Experience ( = -.018, p < .05)

Model Fit
AIC 1170
Fit sig: = 348 with 5 df, p <.001
Promise Evaluations at Time 2 significantly related to:

Involvement T1 ( = .663, p < .01)

Not significantly related to:
Initiative Home Subunit Location ( = .018, p > .10)

Model Fit
AIC 1141
Fit sig: = 31.22 with 2 df, p < .01
Supports Hypothesis 1 Fails to support Hypothesis 3 Supports Hypothesis 2 Supports Hypothesis 4 Model Fit:
AIC- 1120
Fit Sig.: = 34.15 with 2 df, p<.01 Equal-Weighted Alter Evaluations (Model 4)
Model Fit:AIC= 1076 , =39.47 1 df, p < .01
Predictor Fit. = .383, p < .01

Social Cohesion-Weighted Alter Evaluations (Model 5)- Compared to Model 4
Model Fit: AIC= 1059, = 16.3, 0 df, p < .01
Predictor Fit. = .354, p < .01

Initiative Awareness-Weighted Alter Evaluations (Model 6)- Compared to Model 4
Model Fit: AIC = 1075, = .64, 0 df, p < .01
Predictor Fit. = .389, p < .01 Supports Hypothesis 5 Fails to support Hypothesis 6a Supports Hypothesis 6b Findings Implications Limitations Promise functions in the evaluation of initiatives in an organization, and holds empirically across projects and across time.

Individuals are positively disposed towards projects on which they are involved, and this effect is stronger for those with status from work experience.

When controlling for involvement, individuals generally don't hold a bias towards projects in their own subunit, but those with less interaction outside their subunit do, and those with significant interaction outside actually favor such 'outside' initiatives.

Individuals are influenced by their peers, over and above formal structure, and more so when these alters are more aware of the initiative in question.
Test over time lends credence to claim that it's more than self-selection and/or exposure to similar stimuli at work. Need to better understand the role of network diversity not just in suppressing bias (Beckman & Haunschild, 2002), but also in creating biases of different forms
NIH or Grass is Greener

While the push for interconnected organizations has some clear benefits (Destruction of Silos), might it create other biases that are just, if not more, destructive?

Are experienced individuals the best people to make resource allocation decisions?

Are individuals trusting the right individuals when being influenced? Studying 'promise' and structural influences in one organization/ context alone.

Not directly addressing issues of judgment accuracy

Separating evaluation from selection But why all? Top Management--> Resource Allocation Decisions
Employees & Scientists --> Collaboration, Coordination, Personal Resource Allocation goals capability direct
outcomes generative outcomes Boston
2007 INVOLVE 0 1
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