CRO Adoption in London Startups – Survey
Description
This dataset contains responses from 102 participants collected as part of a dissertation project examining the factors influencing organisational adoption of Conversion Rate Optimisation (CRO) practices. The study applies the Technology–Organisation–Environment (TOE) framework, exploring the impact of technological, organisational, and environmental determinants on adoption behaviour. The dataset includes demographic and firmographic information, perceptions of technological advantage, complexity, and compatibility, as well as organisational readiness (resources, knowledge, innovativeness) and environmental pressures (competition, firm size, revenue). Data Collection Methodology: Survey data were collected via an online questionnaire distributed to firms across industries. Respondents provided information on their firm’s size, industry, revenue, CRO usage or intent to adopt, and perceptions of various TOE sub-factors using Likert-type scales. Responses were anonymised prior to analysis. Data Type: Quantitative survey responses (numeric scales, categorical values, free-text open responses). File Format: CSV (.csv) — 24 columns, 102 rows. Variables (selected): • Participant Number — unique respondent ID. • Q1 (Industry experience) — years of business operation. • Q2 (Size) — firm size in employees (categorical, recoded to numeric midpoints). • Q3 (Revenue) — annual revenue band (categorical, recoded to numeric midpoints). • Q5 (Adoption status) — whether firm currently uses CRO. • Q7 (Intention to adopt) — future intention to adopt CRO. • Q10_1 (Advantage) — perceived relative advantage of CRO. • Q10_2 (Complexity) — perceived complexity of CRO. • Q10_3 (Compatibility) — perceived compatibility with existing systems. • Q11 & 12_1 (Innovativeness) — firm’s openness to innovation. • Q11 & 12_2 (Resources) — perceived adequacy of resources for CRO. • Q11 & 12_3 (Knowledge) — staff knowledge/skills for CRO. • Q11 & 12_4 (Competition) — perceived competitive pressure. • Derived variables: Log_Size, Log_Revenue, and binary Adopt (coded from Q5 and Q7). Potential Uses: The dataset can be used to: • Replicate logistic regression models predicting adoption under the TOE framework. • Explore relationships between firm characteristics and adoption intent. • Serve as a reference dataset for studies on digital marketing, innovation adoption, or CRO. Limitations: • Sample size (n = 102) may limit generalisability. • Responses are self-reported and subject to perception bias. • Certain items (Q6, Q9, Q13–Q15) contain open-text responses that may require qualitative coding for reuse. Ethics & Anonymisation: No personally identifiable information (PII) is included. Data were collected with informed consent, and open-text fields have been anonymised where necessary.