Contents
Article 1: Operationalizing Lean Startup (R. Harms & M. Schwery, 2019)........................................2
Article 2: Digital paradox in servitization (D. Sjödin, 2020)..............................................................3
Article 3: Current state of business models (B.W. Wirtz, 2016)..........................................................4
Article 4: Artificial Intelligence (AI) by governments (S. Chatterjee, 2022)......................................5
Article 5: Impact acculturation purchase intentions (H. Kizgin, 2021)...............................................6
Article 6: Artificial Intelligence (AI) on information disclosure (B. Kronemann, 2022)....................7
Article 7: Effective marketing communication using social tie (Chung-Chi Shen, 2016)...................8
, Article 1: Operationalizing Lean Startup (R. Harms & M.
Schwery, 2019)
Abstract/introduction
This article is about lean startup that is often used within startup organizations. LS is often research in
a qualitative approach. In quantitative research that has been performed, LS is often looked at from
only an experimental approach. However, this research operationalized LS considering the full
spectrum of LSC using it’s five phases.
Research gap: quantitative approach using five phases of LS
Research question(s):
How to operationalize lean startup (quantitative) using lean startup capability (LSC)
Hypothesis 1: The higher the degree of LSC, the higher the performance. Hypothesis 2: The LSC-
performance link is moderated by market and technological uncertainty: The higher the market and
technological uncertainty, the stronger the LCS-performance relationship. Hypothesis 3: The LSC-
performance link is moderated by the degree of innovativeness: The more radical innovation is, the
weaker is the LSC-performance relationship. Hypothesis 4: The type of business moderates the LSC-
performance link: It is stronger for B2C than for combined B2B and B2C business, which is again
stronger than for B2B businesses.
Methodology
Sample = 2.100 software startups in Berlin that are younger than 6jrs. Also personal connections were
used of about 450 entrepreneurs with 100 useable responses. They open-coded the responses, leading
to 5 major activities that entrepreneurs perform when using LS. They were; iteration, experimentation,
characteristics (customer orientation), validation, learning and prototyping. Because prototyping was
found to not be empirically distinct and covered in other research, it was left out. Hypothesis testing
was found to be more important.
Results/discussion
Moderately strong relationship found between LSC and performance (H1), more mature ventures
performed better. Of the moderators, the mixed category showed a stronger relation between LSC and
performance (H4). Columns represent the four presented hypotheses.
Limitations/future research
There are contrary findings in other research and depend strongly on the research context. Most
importantly, the research did not test the iterative and circular nature of customer-oriented
experimental learning.
Article 1: Operationalizing Lean Startup (R. Harms & M. Schwery, 2019)........................................2
Article 2: Digital paradox in servitization (D. Sjödin, 2020)..............................................................3
Article 3: Current state of business models (B.W. Wirtz, 2016)..........................................................4
Article 4: Artificial Intelligence (AI) by governments (S. Chatterjee, 2022)......................................5
Article 5: Impact acculturation purchase intentions (H. Kizgin, 2021)...............................................6
Article 6: Artificial Intelligence (AI) on information disclosure (B. Kronemann, 2022)....................7
Article 7: Effective marketing communication using social tie (Chung-Chi Shen, 2016)...................8
, Article 1: Operationalizing Lean Startup (R. Harms & M.
Schwery, 2019)
Abstract/introduction
This article is about lean startup that is often used within startup organizations. LS is often research in
a qualitative approach. In quantitative research that has been performed, LS is often looked at from
only an experimental approach. However, this research operationalized LS considering the full
spectrum of LSC using it’s five phases.
Research gap: quantitative approach using five phases of LS
Research question(s):
How to operationalize lean startup (quantitative) using lean startup capability (LSC)
Hypothesis 1: The higher the degree of LSC, the higher the performance. Hypothesis 2: The LSC-
performance link is moderated by market and technological uncertainty: The higher the market and
technological uncertainty, the stronger the LCS-performance relationship. Hypothesis 3: The LSC-
performance link is moderated by the degree of innovativeness: The more radical innovation is, the
weaker is the LSC-performance relationship. Hypothesis 4: The type of business moderates the LSC-
performance link: It is stronger for B2C than for combined B2B and B2C business, which is again
stronger than for B2B businesses.
Methodology
Sample = 2.100 software startups in Berlin that are younger than 6jrs. Also personal connections were
used of about 450 entrepreneurs with 100 useable responses. They open-coded the responses, leading
to 5 major activities that entrepreneurs perform when using LS. They were; iteration, experimentation,
characteristics (customer orientation), validation, learning and prototyping. Because prototyping was
found to not be empirically distinct and covered in other research, it was left out. Hypothesis testing
was found to be more important.
Results/discussion
Moderately strong relationship found between LSC and performance (H1), more mature ventures
performed better. Of the moderators, the mixed category showed a stronger relation between LSC and
performance (H4). Columns represent the four presented hypotheses.
Limitations/future research
There are contrary findings in other research and depend strongly on the research context. Most
importantly, the research did not test the iterative and circular nature of customer-oriented
experimental learning.
