Accelerators studies
| Authors | Dependent variable/ research focus | Method | Data | Summary and findings |
|---|---|---|---|---|
| (1) Conceptual descriptions of the accelerator model | ||||
| Cohen and Hochberg (2014) | Accelerator model definition | Conceptual | Differences between accelerators, incubators, angel investors and coworking environments. Success factors | |
| Dempwolf et al. (2014) | Accelerator performance assessment | Conceptual | Taxonomy of innovation accelerator: (1) incubators and venture development organisations, (2) proof-of-concept centres and (3) accelerators | |
| Hochberg (2016) | Accelerator model definition | Conceptual | Evidence on the effects of the accelerator models on the regional entrepreneurial environment | |
| (2) Qualitative analyses assessing accelerator performance | ||||
| Kim and Wagman (2014) | (1) Accelerator portfolio size choice; (2) Profit-maximising portfolio size; (3) Entrepreneurial effort effects; (4) Accelerator disclosures; (5) Accelerator portfolio quality; (6) Accelerator exit time | Qualitative | Game theory model of the accelerator as certification of start-up quality. Accelerator may possess incentives to exit its portfolio firms early | |
| Radojevich- Kelley and Hoffman (2012) | Accelerator model and start-ups: (1) Motivations; (2) Success rates; (3) Selection criteria; (4) Challenges; (5) Added value | Qualitative | 5 US accelerators | Exploratory case study examining how accelerator programs connect start-ups with potential investors |
| Cohen (2013) | Accelerators organisational learning | Qualitative | 70 interviews from 9 US accelerators | Embedded multiple-case study to assess how the new venture process is accelerated |
| Pauwels et al. (2016) | Design elements : (1) Program; Strategy; (2) Selection; (3) Funding; Alumni | Qualitative | 13 European accelerators | Accelerator model's key design parameters |
| Cohen, Bingham and Hallen (2018) | Accelerators' choices: (1) Consultation intensity ; (2) Disclosure level; (3) Extent of customisation | Qualitative | 8 US accelerators and 37 accelerated start-ups | Inductive multiple-case study on how accelerator programs influence new ventures' ability to survive and grow |
| Stayton and Mangematin (2019) | Venture characteristics: (1) Survival; (2) Resource network; (3) Accelerator's resources | Qualitative | 4 clean tech start-ups | Explores the mechanisms by which accelerator programs assist nascent technology ventures to minimise start-up time |
| (3) Empirical studies of accelerators, establishing a new performance framework or studying the positive effect on the outcomes of accelerated start-ups | ||||
| Smith and Hannigan (2015) | 1) Time of exit; 2) Subsequent funding outcomes | Quantitative | 619 US start-ups | Study based on 2 top accelerators (Y Combinator and Tech Stars) for the period 2005–2011. Participation in a top accelerator program increases the speed of exit by acquisition and by quitting |
| Cohen, Fehder, Hochberg and Murray (2019) | (1) Founder background; (2) Sponsor type; (3) Accelerated start-up raised funding post-program > $500 K; (4) Total $ funding raised; (5) Maximum valuation attained | Quantitative and qualitative | 146 US accelerators and 100 interviews | Descriptive correlations between design elements and performance of the start-ups that attend the Accelerator programs |
| Fehder and Hochberg (2019) | (1) Accelerator year foundation; (2) MSA location | Quantitative | 59 US accelerators | Impact of an accelerator's arrival on the volume of seed and early-stage VC deals completed in the region |
| Hallen, Bingham and Cohen (2019) | (1) Accelerated start-up outcomes; (2) Time to fundraising; (3) Start-up learning process; (4) Consultation in focal Accelerators; (5) Inter-organisational learning mechanisms | Quantitative and qualitative | 8 US accelerators and 70 interviews | Comparison of treated and untreated start-ups on a variety of outcomes |
| This study | (1) Accelerator investment rounds in accelerated start-ups; (2) Location effect | Quantitative | 116 worldwide accelerators | Model exploring accelerator performance on three axes: (1) size, (2) location and age and (3) profitability variables. Higher size and performance in the United States and in the eldest accelerators |
| (4) Empirical studies of accelerators' negative or inconclusive effect on the outcomes of accelerated start-ups | ||||
| Smith, Hannigan and Gasiorowski (2013) | (1) Accelerated start-ups survival; (2) Funding; (3) Founder background | Quantitative | 740 accelerated start-ups | Analysis of differences in the founder backgrounds in two top accelerators (Y Combinator and TechStars) compared to other start-ups |
| Gonzalez-Uribe and Leatherbee (2017) | (1) Effect of basic accelerator services on new venture performance; (2) Effect of schooling and basic services | Quantitative | 3,258 accelerator applicants and 276 pitch-day competitors | Study based on an individual accelerator program (Start-up Chile). Start-ups selected for access to entrepreneurship schooling tend to achieve more intermediate milestones |
| Yu (2019) | (1) External financing and venture growth; (2) Acquisitions; (3) Closures | Quantitative and qualitative | 13 accelerators and 70 interviews | Start-ups admitted to accelerators are less likely to achieve key milestones |
| Authors | Dependent variable/ research focus | Method | Data | Summary and findings |
|---|---|---|---|---|
| Accelerator model definition | Conceptual | Differences between accelerators, incubators, angel investors and coworking environments. Success factors | ||
| Accelerator performance assessment | Conceptual | Taxonomy of innovation accelerator: (1) incubators and venture development organisations, (2) proof-of-concept centres and (3) accelerators | ||
| Accelerator model definition | Conceptual | Evidence on the effects of the accelerator models on the regional entrepreneurial environment | ||
| (1) Accelerator portfolio size choice; (2) Profit-maximising portfolio size; (3) Entrepreneurial effort effects; (4) Accelerator disclosures; (5) Accelerator portfolio quality; (6) Accelerator exit time | Qualitative | Game theory model of the accelerator as certification of start-up quality. Accelerator may possess incentives to exit its portfolio firms early | ||
| Accelerator model and start-ups: (1) Motivations; (2) Success rates; | Qualitative | 5 US accelerators | Exploratory case study examining how accelerator programs connect start-ups with potential investors | |
| Accelerators organisational learning | Qualitative | 70 interviews from 9 US accelerators | Embedded multiple-case study to assess how the new venture process is accelerated | |
| Design elements : (1) Program; Strategy; (2) Selection; (3) Funding; Alumni | Qualitative | 13 European accelerators | Accelerator model's key design parameters | |
| Accelerators' choices: (1) Consultation intensity ; (2) Disclosure level; (3) Extent of customisation | Qualitative | 8 US accelerators and 37 accelerated start-ups | Inductive multiple-case study on how accelerator programs influence new ventures' ability to survive and grow | |
| Venture characteristics: (1) Survival; (2) Resource network; (3) Accelerator's resources | Qualitative | 4 clean tech start-ups | Explores the mechanisms by which accelerator programs assist nascent technology ventures to minimise start-up time | |
| 1) Time of exit; 2) Subsequent funding outcomes | Quantitative | 619 US start-ups | Study based on 2 top accelerators (Y Combinator and Tech Stars) for the period 2005–2011. Participation in a top accelerator program increases the speed of exit by acquisition and by quitting | |
| (1) Founder background; (2) Sponsor type; (3) Accelerated start-up raised funding post-program > $500 K; (4) Total $ funding raised; (5) Maximum valuation attained | Quantitative and qualitative | 146 US accelerators and 100 interviews | Descriptive correlations between design elements and performance of the start-ups that attend the Accelerator programs | |
| (1) Accelerator year foundation; (2) MSA location | Quantitative | 59 US accelerators | Impact of an accelerator's arrival on the volume of seed and early-stage VC deals completed in the region | |
| (1) Accelerated start-up outcomes; (2) Time to fundraising; (3) Start-up learning process; (4) Consultation in focal Accelerators; (5) Inter-organisational learning mechanisms | Quantitative and qualitative | 8 US accelerators and 70 interviews | Comparison of treated and untreated start-ups on a variety of outcomes | |
| This study | (1) Accelerator investment rounds in accelerated start-ups; (2) Location effect | Quantitative | 116 worldwide accelerators | Model exploring accelerator performance on three axes: (1) size, (2) location and age and (3) profitability variables. Higher size and performance in the United States and in the eldest accelerators |
| (1) Accelerated start-ups survival; (2) Funding; (3) Founder background | Quantitative | 740 accelerated start-ups | Analysis of differences in the founder backgrounds in two top accelerators (Y Combinator and TechStars) compared to other start-ups | |
| (1) Effect of basic accelerator services on new venture performance; (2) Effect of schooling and basic services | Quantitative | 3,258 accelerator applicants and 276 pitch-day competitors | Study based on an individual accelerator program (Start-up Chile). Start-ups selected for access to entrepreneurship schooling tend to achieve more intermediate milestones | |
| (1) External financing and venture growth; (2) Acquisitions; (3) Closures | Quantitative and qualitative | 13 accelerators and 70 interviews | Start-ups admitted to accelerators are less likely to achieve key milestones | |
Source(s): Own compilation from the literature revision; Compiled by the authors from the literature review
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