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Record W4394816323 · doi:10.1108/shr-03-2024-0014

How appreciation preferences compare across employees who speak different languages

2024· article· en· W4394816323 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueStrategic HR Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPsychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Purpose Research has demonstrated that employees desire to be shown appreciation in various ways. The five languages of appreciation provide a model for exploring these differences. This study aims to explore whether individuals who speak different languages (and are from various cultures) differ in how they prefer to be shown appreciation. Design/methodology/approach The Motivating By Appreciation Inventory (MBAI) is an online tool that assesses each person’s preferences in how they desire to be shown appreciation at work. Initially developed in English, the MBAI has been translated into seven additional languages. Over 2,200 employees took the MBAI in their preferred spoken language: Mandarin (Chinese), Danish, French (Canadian), Portuguese (Brazilian), Spanish (Latin American), Thai and Turkish. The frequency of each group’s preferred appreciation languages was analyzed to determine similarities and differences across the languages spoken. Findings Given the non-normal distribution of the data, the Kruskal–Wallis test found that there was a significant difference in preferences for participants’ primary appreciation language across the seven groups of various spoken languages. One key theme was that words of affirmation were most frequently chosen by five of the seven language groups, whereas employees from Thailand and Turkey chose acts of service most frequently. Additionally, tangible gifts were the least frequently chosen appreciation language by all groups, and at rates below their US counterparts. In three of the languages, quality time was preferred significantly less compared with the other languages. Research limitations/implications Some of the groups’ findings (Portuguese, Thai) may be impacted by a confounding variable of the type of work setting (manufacturing) in which the employees worked – in comparison to office-based work settings. Practical implications One theme was, in comparison to other ways of receiving appreciation, tangible gifts are not highly valued by most employees across all language groups. Therefore, organizations using gifts as the primary way to communicate appreciation to employees may be wasting a lot of money. Similar to English-speaking employees, five of the seven language groups chose words as their preferred appreciation language. A wide range exists, however, across language groups with regards to the proportion who desire words, quality time or acts of service. Multicultural organizations should pay attention to employee preferences, lest they waste time and energy on undesired actions. Originality/value To the best of the author’s knowledge, this is the first study that has examined the preferences of how employees like to be shown appreciation across seven different language groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.640

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.419
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it