Background - Psychological climate has been shown to be significantly linked to performance in
large service businesses; however, no corresponding research has been conducted in small
businesses.
Purpose – The purpose of the current study is to identify the dimensions relevant to psychological
climate in small service businesses and to develop the first psychological climate instrument tailored
to small businesses.
Design/methodology/approach – Questionnaires incorporating items from previous research
in large organisations and new items generated from focus groups were distributed to 316 employees
of 52 small businesses (employing up to the equivalent of 20 full-time employees). Principal
components analysis was used to identify relevant climate dimensions and to guide development of
climate scales. Scale validity and reliability was assessed.
Findings – Seven interpretable psychological climate dimensions were identified and 7 climate
subscales developed displaying acceptable to excellent reliability: Owner facilitation & support (α =
.95); Job training & standards (α = .90); Regulations organisation & pressure (α = .89); Scheduling
(α = .85); Workgroup cooperation, friendliness & esprit (α = .89); Friction & conflict (α = .77); and
Standards & objectives (α = .74). A full-scale measure, Global climate (α = .97), was also developed.
Mean scores on each subscale and Global climate varied significantly between establishments.
Conclusions – The findings of this study support the notion that differences in the nature of
psychological climate between small and large businesses warrant instruments specific to the size of
the organisation. The Psychological Climate Scale for Small Business is the first reliable and valid
instrument tailored to small businesses, and provides a measure for differentiating between
individuals and between establishments in small service organisations. The study was limited to
service organisations, comprising cafés and restaurants, and was confined to a single country.
Expanding beyond these limitations would increase generalisability.