The product life cycle revisited: an integrative review and research agenda
Bibliographic record
Abstract
Purpose This paper aims to respond to calls in academia for an update of the product lifecycle (PLC). Through a systematic literature review, the authors provide an updated agenda, which aims to advance the PLC concept in research, teaching and practice. Design/methodology/approach The authors started by surveying 101 marketing academics globally to ascertain whether a PLC update was viewed necessary and beneficial in the marketing community and thereafter conducted citation analysis of marketing research papers and textbooks to ascertain PLC usage. The subsequent literature review methodology was split into two sections. First, 97 empirical articles were reviewed based on an evaluative framework. Second, research pertaining to the PLC determinants were assessed and discussed. Findings From the results of this review and primary data from marketing academics, the authors find that the method of predicting the PLC based on past sales has been largely unsuccessful and perceived as somewhat outdated. However, a new stream of PLC literature is emerging, which takes a consumer-centric perspective to the PLC and has seen more success at modeling lifecycles in various industries. Research limitations/implications First, the study outlines the most contemporary and successful methodological approaches to modeling the PLC. Namely, the use of artificial intelligence, big data, demand modeling and consumer psychological mechanisms. Second, it provides several future research avenues using modern market trends such as sustainability, globalization, digitization and Covid-19 to push the PLC into the 21st century. Originality/value The PLC has shown to be resolutely popular in management application and education. However, without a continued effort in academic PLC research to update the knowledge around the concept, its use as a productive management tool will likely become outdated. This study provides a necessary and comprehensive literature update resulting in actionable future research and teaching agendas intended to advance the PLC concept into the modern market context.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".