Study on free trial decision-making of IT products and services from an IT company's perspective
Bibliographic record
Abstract
Purpose Free trial is an effective strategy to gaining users’ data so as to strengthen and optimize product design. The purpose of this paper is to understand the IT companies' dynamic decision-making behavior in the free trial of IT products and services context based on a three-stage theoretical framework and users' decision-making behavior in the respective stage. Design/methodology/approach A three-stage methodology is proposed to clarify relevant decision problems and actions in each stage from IT companies' and users' perspectives, respectively. It then investigates relating variables on IT companies' decision-making based on extant research and users' decision-making. Findings In this study, the authors argue that the IT companies have to make the offering, implementation and retention decision in different stage during the whole free trial process. Each decision is determined by several variables from their own and users, namely the offering decision is determined by product characteristics, network effects, product life cycle and WOM (word of mouth); the implementation decision is determined by the quality of products and services, trial type, incentive measures on user's usage and communication strategy; and the retention decision is determined by the product and price strategy. Practical implications The results are practical and can be used by IT companies as a decision basis or reference to make reliable decisions so that IT companies can take target measures to ensure the effectiveness of their free trial strategy so as to meet their users' needs based on products designed by data driven. Thus, the ultimate goal of supply chain management is achieved. Originality/value In this study, the decision-making process in the free trial of IT products and services context is investigated as a whole for the first time. From the IT companies' perspective, the process includes offering, implementation and retention decision stages, which are continuous and inseparable. The variables that determine IT companies' decision-making are identified based on users' decision and action. Hence, it represents a brand-new whole process perception to clearly understand the dynamic of the IT companies' decision-making. Considering users' decision and action, the final decisions of the IT companies will be more practical in respect of motivating, retaining and upgrading users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".