The value of online surveys: a look back and a look ahead

Evans, Joel R.; Mathur, Anil

doi:10.1108/intr-03-2018-0089

Cited by 318 publications

(231 citation statements)

References 167 publications

(175 reference statements)

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“…The study followed general recommendations for online survey research methodology (Evans & Mathur, 2018). Procedural remedies suggested by Podsakoff, MacKenzie, Lee, and Podsakoff (2003) were considered in the questionnaire design.…”

Section: Methodsmentioning

confidence: 99%

Understanding the intention to buy secondhand clothing on sharing economy platforms: The influence of sustainability, distance from the consumption system, and economic motivations

Styvén

Mariani

2020

Psychology and Marketing

117

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Sharing economy (SE) platforms are increasingly providing an effective means of connecting providers and users of secondhand goods. While media seem to emphasize that the trend of selling/buying secondhand has been growing due to the consolidation and development of peer-to-peer (P2P) sharing platforms, a comprehensive identification of the antecedents of buying secondhand on SE platforms is virtually missing. This study addresses this gap by (a) identifying different sets of motivations and attitudes leading consumers to adopt SE platforms for secondhand buying and (b) testing a model on a sample of UK consumers in the context of P2P sharing platforms for secondhand clothing. This study reveals that there are three major antecedents of consumers' attitude towards buying secondhand clothes on P2P-SE platforms: perceived sustainability, economic motivations, and taking a distance from the consumption system. Perceived sustainability and economic motivations influence positively attitude toward buying secondhand, as well as motivations in the form of distance from the consumption system. Attitude toward buying secondhand is positively influenced also by distance from the system and, in turn, has a strong positive influence on behavioral intention to buy secondhand clothes. Moreover, past experience of buying secondhand online has a positive moderating effect on the relationship between perceived sustainability and distance from the consumption system.

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Section: Methodsmentioning

confidence: 99%

Understanding the intention to buy secondhand clothing on sharing economy platforms: The influence of sustainability, distance from the consumption system, and economic motivations

Styvén

Mariani

2020

Psychology and Marketing

117

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“…It also provided a way to obtain information quickly from evacuees so that the details of their experiences would be fresh in their minds. An online survey also provided a way to obtain input from many evacuees [25].…”

Section: Methodsmentioning

confidence: 99%

Preparedness and Experiences of Evacuees from the 2016 Fort McMurray Horse River Wildfire

McGee

2019

Fire

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The 2016 Fort McMurray Horse River Wildfire that caused the evacuation of more than 88,000 residents from the Regional Municipality of Wood Buffalo (RMWB), Alberta is the largest wildfire evacuation in Canadian history. This paper presents results of an online survey of 447 evacuees in June when some residents had returned to RMWB, and others were still living elsewhere. Results of this online survey show that many survey respondents were not aware of the high wildfire risk leading up to May 3rd and social cues indicated they should carry on as usual. Many respondents received little if any warning time to enable them to prepare. Most respondents had a vehicle, but traffic impediments caused some to run out of gas, food and water on the drive away from Fort McMurray. Most respondents stayed in more than one location, with most staying with friends and family for at least part of the evacuation. Some respondents faced challenges including financial difficulties, finding suitable accommodation, and dealing with insurance, and some received insufficient information. Help was offered to evacuees by a vast array of sources including organizations, businesses, communities, and residents. The results of this study show that most survey respondents were not prepared before they had to leave, which increased reliance on help provided by others.

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“…. Figures 12,13, and 14 present car and bus travel time distribution with different purposes. Unlike travel distance distribution, travel time distribution has a great difference between car and bus.…”

Section: Travel Time Analysis With Bus and Carmentioning

confidence: 99%

“…The past 20 years has seen a tremendous increase in internet use and computer-mediated communication. Researches of online populations have led to an increase in the use of online surveys [10][11][12][13]. On one hand, it has some advantages including access to individuals in distant locations, the ability to reach difficult to contact participants, and the convenience of having automated data collection, which reduces researcher time and effort [14].…”

Section: Introductionmentioning

confidence: 99%

Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

Gao

Wang²,

Liu

et al. 2019

Journal of Advanced Transportation

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Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

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The value of online surveys: a look back and a look ahead

Cited by 318 publications

References 167 publications

Understanding the intention to buy secondhand clothing on sharing economy platforms: The influence of sustainability, distance from the consumption system, and economic motivations

Understanding the intention to buy secondhand clothing on sharing economy platforms: The influence of sustainability, distance from the consumption system, and economic motivations

Preparedness and Experiences of Evacuees from the 2016 Fort McMurray Horse River Wildfire

Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

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