Evidence map›Paper›PMID 32617418›Full record

ArticleInnovation in aging2020

Measuring Senior Technology Acceptance: Development of a Brief, 14-Item Scale.

Ke Chen, Vivian Wei Qun Lou

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Article in Innovation in aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

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3 · Its place in the literature

Who cites it

45 citing papers in PubMed.

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  20. Vitality at home: a phenomenological study of tele-exercise in women aged 80 and older.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2024
    Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Ke ChenSau Po Centre on Ageing, The University of Hong Kong, China.ORCID 0000-0001-5310-5090
Vivian Wei Qun LouSau Po Centre on Ageing, The University of Hong Kong, China.ORCID 0000-0001-7000-1306

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesTechnology has the potential to provide assistance and enrichment to older people; however, the desired outcomes are dependent on users' acceptance and usage. The senior technology acceptance model (STAM) was developed as a multidimensional measure assessing older people's acceptance of general technology. It contained 11 constructs measured by 38 items and had shown satisfactory psychometric properties. However, the length of the questionnaire increased respondent burden and limited its utilization. The study aimed to develop a brief, reliable, and valid version of scale to measure older people's technology acceptance by shortening the full, 38-item STAM questionnaire. RESEARCH DESIGN AND

methodsThe research method included (1) a sequential item-reduction strategy maximizing internal consistency, (2) convergent and discriminant validity analysis based on confirmative factor analysis, and (3) an expert review of resultant items. Data previously collected for developing the original STAM questionnaire were used to create the brief version. The data were collected from 1,012 community-dwelling individuals aged 55 and older in Hong Kong. Internal consistency and construct validity of the shortened questionnaire were examined. Two experts were invited for reviewing content validity.

resultsThe final 14-item, brief version of the STAM questionnaire consisted of a 4-factor structure, representing classical technology acceptance constructs and age-related health characteristics. Theoretical relationships in the brief version showed similar patterns to the original STAM. The 14-item STAM demonstrated robustness in psychometrics by preserving the reliability and validity of the original STAM questionnaire. DISCUSSION AND IMPLICATIONS: The availability of a reliable and valid assessment tool of the short STAM can help researchers and practitioners to measure older adults' acceptance of technology and its effective usage. The short STAM could save administration time, reduce the burden on respondents, and be included in large-scale surveys.

Indexed as

Confirmative factor analysisPsychometricsSequential item reductionValidation

Identifiers

PMID32617418
PMCPMC7320876

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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.