Demographic Profiling and Domestic Tourism Participation Behavior in Nairobi County, Kenya

Authors

DOI:

https://doi.org/10.18488/journal.31.2020.72.155.169

Abstract

This study sought to determine the extent to which demographic characteristics of residents influenced their domestic tourism participation behavior. This was achieved by establishing the relationship between seven demographic characteristics and participation behavior, followed by a comparison of the demographic characteristics of respondents participating and those not participating in domestic tourism. The purpose was to identify the segments with greatest potential for conversion from non-participation into participation in domestic tourists. The study targeted Nairobi residents aged above 18 years. Questionnaires were administered to 337 domestic tourists and 339 non-tourists. Chi square cross tabulation indicated that domestic tourism participation behavior was dependent on all the demographic characteristics of the respondents. Chi square goodness of fit test exhibited significant differences between tourists and non-tourists across all attributes of gender and level of education. For the other characteristics (namely age, occupation, income, marital status and family life cycle), the test revealed significant differences across some of the attributes while registering no significant difference across others. The segments with no significant difference were; Age (31-40), Occupation (students and retirees), Income (those earning Ksh.200,000-300,000 and above 300,000), marital status (the widowed and divorced), and family life cycle (those with young children and empty-nesters). The study, therefore, concluded that these were the segments with the greatest potential for conversion to domestic tourism participation. It further recommended the targeting of the segments identified above for domestic tourism in addition to the existing marketing efforts.

Keywords:

Demographic characteristics, Domestic tourists, Domestic non-tourists, Participation behavior, Profiling, Segment, Targeting

Abstract Video

Published

2020-06-29

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Section

Articles