Evidence map›Paper›PMID 38545142›Full record

ArticleHeliyon2024

Extending the technology acceptance model and empirically testing the conceptualised consumer goods acceptance model.

Katrin Förster

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Katrin FörsterDepartment of Mobility, Trade & Logistics, Zeppelin University, Friedrichshafen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The technology acceptance model (TAM) has not been applied beyond the technological context, despite its widespread applications. This study extends the TAM to consumer goods and validates the conceptualised consumer goods acceptance model (CGAM) using tea-to-go as a proxy for consumer goods. An exploratory research approach based on a quota sample is used to adjust and test the CGAM, showing that the adjusted CGAM and its measures are reliable and valid, thus supporting the hypothesis that extending the TAM to a non-technological context is possible. The results are discussed in light of the data collection, which is based on a cross-sectional setting, assuming consumers' knowledge of the good and forcing answers based on assumptions. The results show that despite major changes to the exogenous variables, the difference in variance levels between perceived usefulness (PU) and perceived ease of use (PEOU) is in accordance with TAM 3. Thus, PU is the strongest predictor of behavioural intention (BI), supporting the choice of constructs influencing PU. This also indicates that usefulness outweighs ease of use. The findings demonstrate that each variable category has a favourable influence on PU and PEOU. Specifically, cognitive instrumental processes (CIP) and value-related aspects (VRA) are significant predictors of PU, whereas CIP is the most critical driver, highlighting that consumer actions are driven by factors correlated with lifestyle, trends, and price. VRA_1 had the strongest impact on PU, accentuating the high potential of functional ingredients, such as vitamins. Furthermore, the results show that the anchor variables have a stronger impact on PEOU than the adjustment variables, emphasising the importance of enjoyment and indulgence in consumer goods, especially tea-to-go. These findings show that the empirically tested and adjusted CGAM provides a solid basis for measuring the acceptance of consumer goods and identifying key drivers.

Indexed as

Consumer acceptanceConsumer behaviourConsumer goods acceptance model (CGAM)Quantitative methodsTechnology acceptance model

Identifiers

PMID38545142
PMCPMC10966591

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.