Exploring the Effect of Social Networking Service on Homestay Intention in Vietnam by GM(1, N) and Multiple Regression Analysis
A significant area of Social Computing is the Social Network Service (SNS), known as Social Network Applications. In recent years, researchers have commonly used SNS as an instrument for linking and communicating. Homestay travel has been prevalent for a long time with the rise of social media. The...
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Veröffentlicht in: | Journal of Information Science and Engineering 2022-05, Vol.38 (3), p.531-546 |
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creator | 邱桂堅(KUEI-CHIEN CHIU) 賴志松(CHIH-SUNG LAI) 朱倖慧(HSING-HUI CHU) DUCHANH TRAN THI 陳榮靜(RUNG-CHING CHEN) |
description | A significant area of Social Computing is the Social Network Service (SNS), known as Social Network Applications. In recent years, researchers have commonly used SNS as an instrument for linking and communicating. Homestay travel has been prevalent for a long time with the rise of social media. The goal of this study is to examine the influence of Social Network Service functions on homestay travel intention in Vietnam. Fourteen Social Network Service functions were summarized from the literature review and used as the variables influencing the purpose of homestay travel to develop a five-point Likert scale questionnaire for convenience sampling to perform an online survey. For further study, two hundred and twenty valid respondents were included. The GM(1, N) analysis showed interest sharing, photo sharing, and video sharing as the first to third most principal factors in their highly seasoned weighting towards homestay travel intention. On the other side, helping decision, helping interaction, and helping planning as the last three lists of weighting scores. Besides, multiple regression analysis shows that offering recommendations, helping planning, and sharing interest simultaneously predict homestay travel intention while the others don't. That means consumers would heavily rely upon the functions of sharing interest of social network services to evaluate their traveling options. It is proposed that homestay traveling vendors should pay more attention to the marketing of previous travelers' experiential interests to provoke the awareness of customers. |
doi_str_mv | 10.6688/JISE.202205_38(3).0003 |
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In recent years, researchers have commonly used SNS as an instrument for linking and communicating. Homestay travel has been prevalent for a long time with the rise of social media. The goal of this study is to examine the influence of Social Network Service functions on homestay travel intention in Vietnam. Fourteen Social Network Service functions were summarized from the literature review and used as the variables influencing the purpose of homestay travel to develop a five-point Likert scale questionnaire for convenience sampling to perform an online survey. For further study, two hundred and twenty valid respondents were included. The GM(1, N) analysis showed interest sharing, photo sharing, and video sharing as the first to third most principal factors in their highly seasoned weighting towards homestay travel intention. On the other side, helping decision, helping interaction, and helping planning as the last three lists of weighting scores. Besides, multiple regression analysis shows that offering recommendations, helping planning, and sharing interest simultaneously predict homestay travel intention while the others don't. That means consumers would heavily rely upon the functions of sharing interest of social network services to evaluate their traveling options. It is proposed that homestay traveling vendors should pay more attention to the marketing of previous travelers' experiential interests to provoke the awareness of customers.</description><identifier>ISSN: 1016-2364</identifier><identifier>DOI: 10.6688/JISE.202205_38(3).0003</identifier><language>eng</language><publisher>Taipei: 社團法人中華民國計算語言學學會</publisher><subject>Communication ; Literature reviews ; Multiple regression analysis ; Regression analysis ; Social networks ; Social research ; Travel ; Weighting</subject><ispartof>Journal of Information Science and Engineering, 2022-05, Vol.38 (3), p.531-546</ispartof><rights>Copyright Institute of Information Science, Academia Sinica May 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27922,27923</link.rule.ids></links><search><creatorcontrib>邱桂堅(KUEI-CHIEN CHIU)</creatorcontrib><creatorcontrib>賴志松(CHIH-SUNG LAI)</creatorcontrib><creatorcontrib>朱倖慧(HSING-HUI CHU)</creatorcontrib><creatorcontrib>DUCHANH TRAN THI</creatorcontrib><creatorcontrib>陳榮靜(RUNG-CHING CHEN)</creatorcontrib><title>Exploring the Effect of Social Networking Service on Homestay Intention in Vietnam by GM(1, N) and Multiple Regression Analysis</title><title>Journal of Information Science and Engineering</title><description>A significant area of Social Computing is the Social Network Service (SNS), known as Social Network Applications. In recent years, researchers have commonly used SNS as an instrument for linking and communicating. Homestay travel has been prevalent for a long time with the rise of social media. The goal of this study is to examine the influence of Social Network Service functions on homestay travel intention in Vietnam. Fourteen Social Network Service functions were summarized from the literature review and used as the variables influencing the purpose of homestay travel to develop a five-point Likert scale questionnaire for convenience sampling to perform an online survey. For further study, two hundred and twenty valid respondents were included. The GM(1, N) analysis showed interest sharing, photo sharing, and video sharing as the first to third most principal factors in their highly seasoned weighting towards homestay travel intention. On the other side, helping decision, helping interaction, and helping planning as the last three lists of weighting scores. Besides, multiple regression analysis shows that offering recommendations, helping planning, and sharing interest simultaneously predict homestay travel intention while the others don't. That means consumers would heavily rely upon the functions of sharing interest of social network services to evaluate their traveling options. It is proposed that homestay traveling vendors should pay more attention to the marketing of previous travelers' experiential interests to provoke the awareness of customers.</description><subject>Communication</subject><subject>Literature reviews</subject><subject>Multiple regression analysis</subject><subject>Regression analysis</subject><subject>Social networks</subject><subject>Social research</subject><subject>Travel</subject><subject>Weighting</subject><issn>1016-2364</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNpVUE1PAjEQ7UET8eMvmCZeIHGx23a75UjIChjARNTrpiyzWFxa3BaVk3_dbiAxHiYvM_Pm5c1D6DomXSGkvHsYz7MuJZSSJGeyzTpdQgg7Qa2YxCKiTPAzdO7cmhAqEs5b6Cf73la21maF_RvgrCyh8NiWeG4LrSo8A_9l6_dmP4f6UxeArcEjuwHn1R6PjQfjdRhpg181eKM2eLHHw2k7vsWzDlZmiae7yuttBfgJVjU419D7RlV7p90lOi1V5eDqiBfo5T57HoyiyeNwPOhPIkW59FFClkupCglKFSyGgjNJC8pKGlPJw-MkTknKVAopLBiUPcaohDQtFj3BemUp2QW6Oehua_uxC-bztd3VwYTLqUjDdZBoWKMDS-lae_3HaeJr0suP2R6A8JBu_L9JWCgu2C86OHOm</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>邱桂堅(KUEI-CHIEN CHIU)</creator><creator>賴志松(CHIH-SUNG LAI)</creator><creator>朱倖慧(HSING-HUI CHU)</creator><creator>DUCHANH TRAN THI</creator><creator>陳榮靜(RUNG-CHING CHEN)</creator><general>社團法人中華民國計算語言學學會</general><general>Institute of Information Science, Academia Sinica</general><scope>188</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20220501</creationdate><title>Exploring the Effect of Social Networking Service on Homestay Intention in Vietnam by GM(1, N) and Multiple Regression Analysis</title><author>邱桂堅(KUEI-CHIEN CHIU) ; 賴志松(CHIH-SUNG LAI) ; 朱倖慧(HSING-HUI CHU) ; DUCHANH TRAN THI ; 陳榮靜(RUNG-CHING CHEN)</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a248t-50dd8ac8eaac31ec4382c23f21284688017073a7e7eb3ef93328e77cb9639ff83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Communication</topic><topic>Literature reviews</topic><topic>Multiple regression analysis</topic><topic>Regression analysis</topic><topic>Social networks</topic><topic>Social research</topic><topic>Travel</topic><topic>Weighting</topic><toplevel>online_resources</toplevel><creatorcontrib>邱桂堅(KUEI-CHIEN CHIU)</creatorcontrib><creatorcontrib>賴志松(CHIH-SUNG LAI)</creatorcontrib><creatorcontrib>朱倖慧(HSING-HUI CHU)</creatorcontrib><creatorcontrib>DUCHANH TRAN THI</creatorcontrib><creatorcontrib>陳榮靜(RUNG-CHING CHEN)</creatorcontrib><collection>Airiti Library</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of Information Science and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>邱桂堅(KUEI-CHIEN CHIU)</au><au>賴志松(CHIH-SUNG LAI)</au><au>朱倖慧(HSING-HUI CHU)</au><au>DUCHANH TRAN THI</au><au>陳榮靜(RUNG-CHING CHEN)</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring the Effect of Social Networking Service on Homestay Intention in Vietnam by GM(1, N) and Multiple Regression Analysis</atitle><jtitle>Journal of Information Science and Engineering</jtitle><date>2022-05-01</date><risdate>2022</risdate><volume>38</volume><issue>3</issue><spage>531</spage><epage>546</epage><pages>531-546</pages><issn>1016-2364</issn><abstract>A significant area of Social Computing is the Social Network Service (SNS), known as Social Network Applications. In recent years, researchers have commonly used SNS as an instrument for linking and communicating. Homestay travel has been prevalent for a long time with the rise of social media. The goal of this study is to examine the influence of Social Network Service functions on homestay travel intention in Vietnam. Fourteen Social Network Service functions were summarized from the literature review and used as the variables influencing the purpose of homestay travel to develop a five-point Likert scale questionnaire for convenience sampling to perform an online survey. For further study, two hundred and twenty valid respondents were included. The GM(1, N) analysis showed interest sharing, photo sharing, and video sharing as the first to third most principal factors in their highly seasoned weighting towards homestay travel intention. On the other side, helping decision, helping interaction, and helping planning as the last three lists of weighting scores. Besides, multiple regression analysis shows that offering recommendations, helping planning, and sharing interest simultaneously predict homestay travel intention while the others don't. That means consumers would heavily rely upon the functions of sharing interest of social network services to evaluate their traveling options. It is proposed that homestay traveling vendors should pay more attention to the marketing of previous travelers' experiential interests to provoke the awareness of customers.</abstract><cop>Taipei</cop><pub>社團法人中華民國計算語言學學會</pub><doi>10.6688/JISE.202205_38(3).0003</doi><tpages>16</tpages></addata></record> |
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subjects | Communication Literature reviews Multiple regression analysis Regression analysis Social networks Social research Travel Weighting |
title | Exploring the Effect of Social Networking Service on Homestay Intention in Vietnam by GM(1, N) and Multiple Regression Analysis |
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