Construct · 6 names · 7 citations

Where does trialability collide?

Different names. Different fields. One construct. One map.

● jangle · six names, one construct Nearest construct by meaningresult demonstrabilitycompare the meanings
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Every construct on the map is compared with every other by the meaning of its definitions, and the closest one is surfaced here. The nearest construct to trialability is result demonstrability.

Hold this as a prompt, not a verdict. Two constructs sitting close in meaning is a reason to read them side by side and decide for yourself whether they are one idea under two names. It is not proof that they are. Compare the meanings in the Collider.

ITEMS WRITTEN FROM ROGERSSUBSCALE REUSED (1997)trialability6 NAMESDiffusion theoryROGERS · 1962 · 2003PCI instrumentMOORE & BENBASAT · PCIIT acceptanceAGARWAL & PRASAD · 1997FreemiumFREEMIUM · CONVERSIONProduct trialSAMPLING · TRIAL · BELIEFSMeta-analysisTORNATZKY & KLEIN · 1982

Spokes are the names and the models that coined them; dashed arcs are borrowings. Tap a field for its definition, unit, and measures.

2026

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Diffusion of innovations

the degree to which an innovation may be experimented with on a limited basis, one of five perceived attributes.

The degree to which an innovation may be experimented with on a limited basis, one of Rogers's five perceived attributes that together explain most of the variance in rate of adoption. In the 1962 first edition the attribute was called divisibility, the extent to which a farmer could plant a new seed on one field before committing the farm; Rogers renamed it trialability in later editions so that it covered innovations that cannot be divided but can still be tried. The referent is the innovation as adopters perceive it, and the outcome is the adoption rate of a population, not one person's decision. Everything else on this map descends from these two sentences.[1][2]

Unit
A perceived attribute of an innovation, aggregated to a social system's adoption rate
Recovery means
Not applicable, the criterion is rate of adoption
Measured by
Rated attributes in diffusion surveys; no fixed instrument

Perceived characteristics of innovating

a survey subscale measuring the degree to which an innovation may be experimented with before adoption.

The degree to which an innovation may be experimented with before adoption, Moore and Benbasat kept Rogers's wording and, in 1991, gave it items. They wrote candidate statements for each of Rogers's attributes, sorted them by judges, and tested them on personal-workstation users; trialability survived as its own subscale where observability did not. The items ask whether the respondent has had, and knows where to get, enough hands-on time with the technology. The referent shifts from the innovation to the individual's own opportunity to try it, and the outcome from a diffusion curve to one adopter's intention.[4]

Unit
An individual's perception of their own opportunity to try a technology
Recovery means
Not applicable
Measured by
Trialability subscale of the PCI, seven-point agreement
Scales indexed for this construct
  • PCI trialability (TR)Moore, G. C., & Benbasat, I., 1991 · opportunity to test the technology adequately before adopting5 items

Innovation characteristics in IT acceptance

users' perception of an opportunity to experiment with an innovation before committing to it, linked to initial use.

The extent to which users perceive an opportunity to experiment with an innovation before committing to it, Agarwal and Prasad's 1997 field study of a new information technology, using the Moore and Benbasat subscales with perceived voluntariness added. Their result is the reason this field has its own node: trialability and visibility predicted initial use but not intention to continue using, while relative advantage and result demonstrability did the opposite. The construct is the same; its job in the model is split by stage of adoption, which no one upstream had tested.[5][4]

Unit
A user's perception, related separately to current use and to future intention
Recovery means
Not applicable
Measured by
PCI trialability items, related to current use and continued-use intention

Software free trials and freemium

giving away a basic version of software so users can try it before paying for a premium tier.

The practice of giving away a basic version of a software product so that users can try it before paying for a premium tier, Kumar's 2014 treatment set out when freemium works and how to tune the free-to-paid boundary. The word trialability rarely appears; the construct does, as a design decision made by the seller rather than a perception held by the adopter. The criterion is conversion rate from free to paid, and the trial is not a limited experiment but an indefinite state that most users never leave. This is the seller's side of what Rogers described from the adopter's side.[7]

Unit
A firm's product and pricing design
Recovery means
Not applicable, the criterion is free-to-paid conversion
Measured by
Conversion, retention, and referral rates of free users

Consumer product trial

direct experience with a product before purchase, treated as a consumer behaviour and an information source.

Direct experience with a product before purchase, in consumer research a behaviour and an information source, not an attribute of the product. Kempf and Smith's 1998 structural model traced how a trial episode produces cognitive and affective responses, how those form beliefs and attitudes, and how prior advertising changes what the trial is taken to mean. Trial here is something the consumer does; the thing that varies is the quality of the experience and its diagnosticity, not whether trying was possible. Rogers's attribute is the precondition for this literature's independent variable.[6]

Unit
A trial episode and the consumer's response to it
Recovery means
Not applicable, the criterion is post-trial attitude and purchase
Measured by
Post-trial belief, attitude, and diagnosticity measures in experiments

Innovation-characteristics meta-analysis

the degree to which an innovation can be tried on a limited basis, often measured but inconsistently related to adoption.

The degree to which an innovation can be tried on a limited basis before adoption, Tornatzky and Klein's 1982 meta-analysis coded seventy-five studies for ten innovation characteristics and found trialability among those most often measured but inconsistently related to adoption. Their harder finding was methodological: most studies asked about characteristics after adoption, in cross-section, and with no shared operationalisation, so the pooled effect for trialability was as much an artefact of study design as a property of innovations. Only compatibility, relative advantage, and complexity came out consistently. The name is Rogers's; the verdict on its evidence is theirs.[3]

Unit
A coded characteristic across primary studies of adoption and implementation
Recovery means
Not applicable, the criterion is adoption or implementation in the primary study
Measured by
Whatever the primary study used; coded for direction and significance

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Nearby collisions

observability6 names · 7 citationsjangle · four names, one constructperformance expectancy6 names · 11 citationsjangle · six names, one constructeffort expectancy6 names · 10 citationsjangle · four names, one construct

References

  1. Rogers, E. M. (1962). Diffusion of innovations. Free Press.
  2. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
  3. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463
  4. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
  5. Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
  6. Kempf, D. S., & Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325–338. https://doi.org/10.2307/3152031
  7. Kumar, V. (2014). Making "freemium" work. Harvard Business Review, 92(5), 27–29.

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