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Journal articles on the topic 'Demand characteristics'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Shkvarchuk, Lyudmyla, and Rostyslav Slav’yuk. "Households’ credit demand: Main trends and characteristics for Ukraine." Banks and Bank Systems 16, no. 3 (August 2, 2021): 13–22. http://dx.doi.org/10.21511/bbs.16(3).2021.02.

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Household demand for credits is quite volatile, which requires constant evaluation of it changes. The purpose of the paper is to identify quantitative signals, the use of which increases the predictability of the credit market development. The study utilizes technical analysis methods for an econometric estimation of trends in household demand for credits in Ukraine for the 2002–2019 period. Based on the analysis of historical market lows, it was argued that with all the negative effects of destabilizing factors, the household demand for loans will not fall below the market support point of UAH 50 million. The financial behavior of Ukrainian households when choosing the type of loan is stable and does not change with fluctuations in GDP. Short-term loans are quite dynamic and largely depend on macroeconomic conditions, provoking market movements. If the relevant direction is supported by medium-term loans, the general market trend will correspond to the GDP trend. The demand for long-term loans is quite inertial, its change does not affect the overall market trend. The constant and variable elements of household demand for credit are highlighted.

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2

Berry, Steven, and Ariel Pakes. "THE PURE CHARACTERISTICS DEMAND MODEL*." International Economic Review 48, no. 4 (December 11, 2007): 1193–225. http://dx.doi.org/10.1111/j.1468-2354.2007.00459.x.

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3

Govern, John M., and Lisa A. Marsch. "Inducing Positive Mood without Demand Characteristics." Psychological Reports 81, no. 3 (December 1997): 1027–34. http://dx.doi.org/10.2466/pr0.1997.81.3.1027.

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The possibility that a state of private self-awareness induced by a small mirror can facilitate the effect of procedures which induce a positive mood was investigated. Participants were 171 female and 60 male undergraduates who were randomly assigned to one of six conditions in a 2 (Mirror vs No-mirror) × 3 (Control vs Velten manipulation vs Music manipulation) design. As predicted, participants who experienced the Velten and Music manipulations in the presence of the mirror reported elevated mood relative to control participants. The mood of participants who experienced the Velten and Music manipulations without the mirror did not differ from the mood of control participants. The potential benefits of using a small mirror as a substitute for explicit instructions about the expected effect of mood-induction procedures are discussed.

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4

L. E. Stetson, G. L. Stark, and K. L. Farrell-Poe. "Electric Demand Characteristics of Nebraska Farmsteads." Transactions of the ASAE 31, no. 1 (1988): 0247–56. http://dx.doi.org/10.13031/2013.30696.

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5

Morley, Clive L. "Tourism Demand: Characteristics, Segmentation and Aggregation." Tourism Economics 1, no. 4 (December 1995): 315–28. http://dx.doi.org/10.1177/135481669500100401.

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This paper aims to advance the understanding of the micro-economic foundations of tourism demand theory, particularly through bringing out some of the implications of the tour characteristics approach to utility analysis, and also through explicitly linking the micro-economic theory of the individual tourist to the aggregate level demand models as actually estimated. Important implications of the tour characteristics theory are that it yields theoretical rationales for the importance of market segmentation of tourism demand, and for tourism product differentiation. Consideration of the aggregation issues in the particular context of tourism demand shows aggregation to be justified and feasible when the market is segmented. Market segmentation is shown to be theoretically important for good tourism demand models, supporting its generally observed practical usefulness. Alternatively, aggregation can be justified through a random utility argument that assumes independence of individuals' tour decisions.

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6

Siegel, Paul S., Edward A. Konarski, and Scott L. Bernard. "Demand characteristics and the response suppression hypothesis." Bulletin of the Psychonomic Society 24, no. 5 (November 1986): 365–68. http://dx.doi.org/10.3758/bf03330154.

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7

Bezmen, Trisha, and Craig A. Depken, II. "School characteristics and the demand for college." Economics of Education Review 17, no. 2 (April 1998): 205–10. http://dx.doi.org/10.1016/s0272-7757(97)00025-3.

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8

Tian, Jing-Jing, Dong-Fan Xie, and Fu-Jun Ding. "Mining Spatiotemporal Characteristics of Car-sharing Demand." Journal of Physics: Conference Series 1187, no. 5 (April 2019): 052047. http://dx.doi.org/10.1088/1742-6596/1187/5/052047.

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9

Davies, John B., and David W. Best. "Demand characteristics and research into drug use." Psychology & Health 11, no. 2 (February 1996): 291–99. http://dx.doi.org/10.1080/08870449608400258.

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10

Baltas, George, and Paraskevas C. Argouslidis. "Consumer characteristics and demand for store brands." International Journal of Retail & Distribution Management 35, no. 5 (May 2007): 328–41. http://dx.doi.org/10.1108/09590550710743708.

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11

Ohe, Yasuo. "Accessing demand characteristics of agritourism in Italy." Tourism and hospitality management 18, no. 2 (2012): 281–96. http://dx.doi.org/10.20867/thm.18.2.8.

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The purpose – The aim of this paper is to investigate the demand characteristics of agritourism in Italy, which has not been fully investigated despite the relatively high number of the supply-side studies on the Italian agritourism. Design – First this paper conceptually characterized the features of agritourism as the old and modern types and outlined the trend of supply and demand in agritourism in Italy in comparison with Japan. Second, this paper statistically examined the characteristics in the demand side for agritourism in Italy in comparison with tourism demand in general, such as that for hotels. Methodology and approach – Data were obtained from ‘Annuario Statistico Italiano’ edited and issued by ISTAT (Istituto Nazionale di Statistica). Data from 1997 were compared with those from 2006. We examined the regional characteristics and trends in the composition of domestic and inbound tourists in relation to agritourism. Findings – (1) Agritourism experienced rapid growth in the number of beds available and of those tourists who stayed overnight during the last decade while the operation rate of agritourism is much lower than that of tourism in general. (2) The market for agritourism domestic demand accounted for more than half of the total agritourism demand. The remaining demand was filled by inbound tourists from European countries. These inbound tourists are driving the growth of agritourism in this country. Even if we consider the particular reasons for low barriers to travel in Europe, these findings clearly indicate that it is essential for the development of agritourism to count not only on domestic but also inbound tourists to raise the operation rate. Originality of the research – The originality comes from the investigation of agritourism in Italy by focusing on the demand factors in comparison with tourism in general.

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Jędrych, Anna, and Małgorzata Krzywnicka. "Stimulation Demand Characteristics of the Students of the Polish Air Force Academy in Dęblin." Polish Journal of Aviation Medicine and Psychology 19, no. 4 (October 2, 2013): 31–34. http://dx.doi.org/10.13174/pjamp.19.04.2013.5.

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Rajbhandhari, Utsav Shree, Laxman Poudel, and Nawraj Bhattarai. "Demand Characteristics of Electricity in Residential Sector of Kathmandu Valley." Journal of the Institute of Engineering 15, no. 3 (October 16, 2020): 275–84. http://dx.doi.org/10.3126/jie.v15i3.32196.

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Planning for electricity demand is a vital as the characteristics of different electricity generation systems vary temporally – both hourly in a day as well as seasonally in a year. Thus, this study focuses on evaluating the demand characteristic of electricity in residential sector within urban boundaries of three districts in Kathmandu Valley. It has addressed variations in demand in form of load curve based on hourly peak demand during a day. The demand characteristics have been affected in recent years are primarily influenced by two factors – the trade debacle in 2015 and the end of load-shedding. A typical family with owned household would have highest demand with loads spread over various time of the day. While the one in rented family would have least demand level with most characteristic peaks. But in overall, there are peculiar morning and evening peaks, in addition to small early morning peak. The current technology interventions and electricity consumption pattern with reference in earlier years depicts the change in energy technology preference. The reduction in daily demand pattern as well as total electricity demand are majorly due to replacement of older technologies with more efficient appliances as well as reduced use of inverters for battery charging. Thus, it can be said that a stringent condition can enforce people to change to efficient technologies as well as proper supply can reduce unnecessary demand in battery charging. On other hand, similar trend, and hence the increase in electricity demand, can be anticipated in other flourishing urban areas of the country. Additionally, it is beneficial to have the demand characteristics of each sector separately - which can be useful to design the decentralized systems for specific sector.

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Potrashkova, Lyudmyla. "Some models of demand for benefits, offered by an enterprise." Economics of Development 17, no. 3 (November 29, 2018): 30–41. http://dx.doi.org/10.21511/ed.17(3).2018.03.

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The activities of an enterprise are based on constant exchanges of material and nonmaterial goods with its stakeholders. According to the stakeholder concept, there are common features in the processes of enterprise’s interaction with different groups of stakeholders. For example, common patterns can be found in the behavior of two primary stakeholders groups – buyers and employees. Each actor of these stakeholder groups makes a choice between enterprises on the bases of characteristics of goods (products or working conditions), which are provided by these enterprises, and their own preferences for these characteristics. And yet the preferences of different actors from one stakeholders group are generally different. For example, different buyers have different preferences about products characteristics, and employees have different preferences about working conditions. Thus, in the market of certain good (products or vacancies), on the one hand, there is a variety of variants of the good which differ in their characteristics and, on the other hand, there is a variety of subgroups of stakeholders (buyers or jobseekers), which differ in their preferences about the characteristics of the analyzed good. Therefore, the demand for the good offered by the analyzed enterprise depends on the ratio between the supply of the analyzed good variants and the demand for them from stakeholders subgroups with different preferences. This fact should be taken into account in the process of analyzing and forecasting demand for products and vacancies of enterprises, but existing demand models do not take it into account. The purpose of the study is to construct mathematical models that will allow to assess the demand for products and vacancies of an enterprise, depending on: а) characteristics of the analyzed products and working conditions; b) the preferences of the stakeholders for these characteristics values; c) the available supply volumes from competing enterprises. The paper proposes to assess the demand for an enterprise products by means of an optimization “transportation” model, in which the variables are the volumes of the distribution of variants of products, which differ in consumer characteristics, between groups of buyers, which differ in preferences for products characteristics. Similarly, the demand for vacancies of an enterprise is proposed to be evaluated using the system of optimization "transportation" models, in which the variables are the volumes of the distribution of vacancies in different enterprises, which differ in working conditions, between groups of jobseekers, which differ in the level of competence and preferences for the characteristics of working conditions. The constructed models are designed to meet the challenges of analysis and modeling enterprises activity, taking into account the existence of different variants of buyers’ and employees’ preferences. The novelty of the proposed models resulted from taking into account the set of variants of stakeholders' preferences for the characteristics of goods, as well as using the criterion that evaluates variants of goods distribution among stakeholders in the context of its matching for the preferences of different sub-groups of stakeholders.

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Lin, Adrian Xi, Andrew Fu Wah Ho, Kang Hao Cheong, Zengxiang Li, Wentong Cai, Marcel Lucas Chee, Yih Yng Ng, Xiaokui Xiao, and Marcus Eng Hock Ong. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction." International Journal of Environmental Research and Public Health 17, no. 11 (June 11, 2020): 4179. http://dx.doi.org/10.3390/ijerph17114179.

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The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

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16

Morvay, Karakas. "Satisfaction and characteristics of Airbnb demand in Budapest." Zbornik radova Departmana za geografiju, turizam i hotelijerstvo, no. 46-2 (2017): 76–88. http://dx.doi.org/10.5937/zbdght1702076m.

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17

Wu, Shi Mei, Yu Long Pei, and Guo Zhu Cheng. "Characteristics of Transport Demand in Dense Urban Group." Advanced Materials Research 838-841 (November 2013): 2103–6. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.2103.

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Dense urban group is the inevitable product of relative developed stage of economic and social development. In order to ensure the sustainable development of dense urban group and provide a basis for formulating the transportation infrastructure of scientific development planning, this paper analyzed the traffic demand characteristics in dense urban group. The paper makes use of the statistic analysis method to analyze traffic demand characteristics, especially travel intensity, travel modaltrip time consume and trip distance. It can be concluded that trip number per person and traffic connection of dense urban group are far higher than other regions and the highway is main travel modal. However its average trip distance is low in the nationwide scope.

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18

Brew, Kerry, Taylar Clark, Jordan Feingold-Link, and Hilary Barth. "Do demand characteristics contribute to minimal ingroup preferences?" Journal of Experimental Child Psychology 204 (April 2021): 105043. http://dx.doi.org/10.1016/j.jecp.2020.105043.

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19

Akil, Yusri Syam. "Seasonal Peak Electricity Demand Characteristics: Japan Case Study." International Journal of Energy and Power Engineering 2, no. 3 (2013): 136. http://dx.doi.org/10.11648/j.ijepe.20130203.18.

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20

Kataoka, H. "Design concept and material demand characteristics of rail." Welding International 22, no. 6 (June 2008): 411–15. http://dx.doi.org/10.1080/09507110802285268.

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21

Timko, Christine, Katherine Yu, and Rudolf H. Moos. "Demand characteristics of residential substance abuse treatment programs." Journal of Substance Abuse 12, no. 4 (December 2000): 387–403. http://dx.doi.org/10.1016/s0899-3289(01)00056-6.

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22

Hong, Hyunchul, Wonjin Kim, Jeongyun Kim, and Bacek-Jo Kim. "Analysis of Demand Characteristics for Long-term Forecasts." Journal of Climate Research 8, no. 2 (June 30, 2013): 117–26. http://dx.doi.org/10.14383/cri.2013.8.2.117.

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23

Xia, Yixun, Alondra Rivera-Quintero, Eduardo Calderon, Fang Zhong, and Michael O'Mahony. "Paired Preference Tests with Reversed Hidden Demand Characteristics." Journal of Sensory Studies 29, no. 2 (March 11, 2014): 149–58. http://dx.doi.org/10.1111/joss.12090.

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24

Follain, James R., and Emmanuel Jimenez. "The Demand for Housing Characteristics in Developing Countries." Urban Studies 22, no. 5 (October 1985): 421–32. http://dx.doi.org/10.1080/00420988520080731.

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25

Ohsfeldt, Robert L. "Implicit markets and the demand for housing characteristics." Regional Science and Urban Economics 18, no. 3 (August 1988): 321–43. http://dx.doi.org/10.1016/0166-0462(88)90012-9.

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Nichols, Austin Lee, and Jon K. Maner. "The Good-Subject Effect: Investigating Participant Demand Characteristics." Journal of General Psychology 135, no. 2 (April 2008): 151–66. http://dx.doi.org/10.3200/genp.135.2.151-166.

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27

Grillon, Christian, and Edouard Zarifian. "Hoffmann reflex variations produced by task demand characteristics." Physiology & Behavior 34, no. 2 (February 1985): 213–16. http://dx.doi.org/10.1016/0031-9384(85)90108-8.

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28

Mahyuddin, Tamzil Ibrahim, and A. N. Fadyah. "Characteristics of brown rice demand in Makassar City." IOP Conference Series: Earth and Environmental Science 681, no. 1 (March 1, 2021): 012114. http://dx.doi.org/10.1088/1755-1315/681/1/012114.

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Ansah, Isaac Gershon Kodwo, Emmanuel Marfo, and Samuel Arkoh Donkoh. "Food demand characteristics in Ghana: An application of the quadratic almost ideal demand systems." Scientific African 8 (July 2020): e00293. http://dx.doi.org/10.1016/j.sciaf.2020.e00293.

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30

Roy, Debarpita. "Housing demand in Indian metros: a hedonic approach." International Journal of Housing Markets and Analysis 13, no. 1 (May 10, 2018): 19–55. http://dx.doi.org/10.1108/ijhma-04-2017-0041.

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Purpose This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities. Design/methodology/approach To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics. Findings The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods. Originality/value Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

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van der Burg, Robbert-Jan, Kees Ahaus, Hans Wortmann, and George B. Huitema. "Investigating the on-demand service characteristics: an empirical study." Journal of Service Management 30, no. 6 (December 2, 2019): 739–65. http://dx.doi.org/10.1108/josm-01-2019-0025.

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Purpose Technological developments and new customer expectations of immediacy have driven businesses to adopt on-demand service models. The purpose of this paper is to study the characteristics of a range of on-demand services in order to better understand the meaning of “on-demand” and its implications for service management. This enables the on-demand service logic to be applied to other service contexts, where it may add value for customers. Design/methodology/approach The study starts with a focused literature review and continues with a multiple case study methodology, as the on-demand service concept is in the early stages of theory development. Seven cases were studied, based on a maximum variation sampling strategy. Findings The results show that on-demand services are characterized by three interrelated characteristics: being highly available, responsive and scalable. Analysis further reveals that on-demand services display differences within the conceptual boundaries of these characteristics, i.e. they vary in terms of their availability, responsiveness and scalability. Originality/value Drawing on these findings, the study contributes to the service literature by being the first to specifically conceptualize and define the on-demand services concept and reveal three key characteristics that clarify the distinctive nature of this service type. Accordingly, on-demand services are clearly differentiated from other services. Additionally, the paper discusses the variety within on-demand services and develops an on-demand service continuum that gives detailed insights into the conceptual variations within such services.

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32

Paiva, Patrícia Reis, Nair Heloisa Bicalho de Sousa, and Diogo Onofre Gomes de Souza. "CARACTERÍSTICAS DO PROGRAMA DE DEMANDA SOCIAL DA CAPES / CHARACTERISTICS OF THE CAPES SOCIAL DEMAND PROGRAM." Brazilian Journal of Development 7, no. 1 (2021): 2050–65. http://dx.doi.org/10.34117/bjdv7n1-140.

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Gorbunov, Vladimir K., and Alexander G. Lvov. "Inverse problem of the market demand theory and analytical indices of demand." Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva 21, no. 1 (March 30, 2019): 89–110. http://dx.doi.org/10.15507/2079-6900.21.201901.89-110.

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The inverse problem of the market demand's theory is constructing a collective utility function via a trade statistics consisting of a finite set of pairs ``prices-quantities''. The main computational problem here is the solution of the Afriat's inequalities system, which determines the values of the utility function and the Lagrange multiplier on the trade statistics data, which are ``Afriat's numbers''. This inverse problem is ill-posed one because of multiplicity of inequalities system's solutions and also because of their possible inconsistency and instability. A regularization method for this problem is proposed, based on the relaxation of the Afriat's system yielding local Hausdorf continuity of its solution set, and on the use of characteristics of analytical index numbers determined via Afriat's numbers. These characteristics formalized by choice criteria are: optimism, pessimism, objectivity. The results of constructing analytical index numbers for real trade statistics of Ulyanovsk region are presented.

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34

Wei, Feng, Shou Wei Zhang, and You Hua Ge. "High Speed Dynamic Characteristics Research for LL680 Flexible Rapier Loom." Advanced Materials Research 850-851 (December 2013): 274–78. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.274.

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The textile industry is China's traditional advantage industry. The demands of the textile industry productions efficiency and cost control are much higher than before. LL680 loom frames finite element model was made and analyzed for the companys demand of high-speed improvement, then used experiment to verify. The results show that the modeling method is right and it provide a viable model for structural improvement.

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35

Nababan, Tongam Sihol. "Household Characteristics That Influence Simple Household Demand on Electricity." Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan 16, no. 1 (August 29, 2015): 61. http://dx.doi.org/10.23917/jep.v16i1.938.

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This research aims to analyze the characteristics of households that affect the electric energy consumption of simple households. The second objective is to analyze the probability of each of the factors affecting the electricity energy consumption of small household. The research was conducted in Medan City in the period of March 2014 to November 2014 with samples of 143 small households, the customer of PT. PLN (Persero) Medan, which use the power of electricity for TR-1 /450VA. Data were analyzed by using the logistic regression model. The estimation results indicated that (1) the higher the willingnes to pay (WTP) of households, the higher the tendency to consume elec trical energy per month. (2) the closer the households residence to the city center, the higher the tendency to consume electrical energy than of the households residence in the suburbs, (3) increasingly unfavourable response to electrical quality, the higher the opportunity to consume a greater electric power monthly.

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Chen, Zhengqi, Yingyun Sun, Ai Xin, Sarmad Majeed Malik, and Liping Yang. "Integrated Demand Response Characteristics of Industrial Park: A Review." Journal of Modern Power Systems and Clean Energy 8, no. 1 (2020): 15–26. http://dx.doi.org/10.35833/mpce.2018.000776.

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37

Petras, Elizabeth McLean, and Maria Kousis. "Returning Migrant Characteristics and Labor Market Demand in Greece." International Migration Review 22, no. 4 (1988): 586. http://dx.doi.org/10.2307/2546347.

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38

Vivek S., Belekar. "Study of Urban Travel Demand Characteristics of Twin City." International Journal for Research in Applied Science and Engineering Technology V, no. III (March 13, 2017): 177–84. http://dx.doi.org/10.22214/ijraset.2017.3033.

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Gouvier, W. "Demand characteristics affect performance in mild head injury research." Archives of Clinical Neuropsychology 13, no. 1 (February 1998): 137–38. http://dx.doi.org/10.1016/s0887-6177(98)90628-x.

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Ronzani, Giovanna Miceli, and Anderson Ribeiro Correia. "Impact of demand and airport characteristics on baggage claim." Proceedings of the Institution of Civil Engineers - Transport 168, no. 2 (April 2015): 150–60. http://dx.doi.org/10.1680/tran.12.00048.

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Greenwald, Zoë, Mariève Beauchemin, Louise Charest, Stéphane Lavoie, Alexandra Hamel, Danièle Longpré, and Réjean Thomas. "Understanding characteristics of daily and on-demand PrEP prescriptions." Journal of Virus Eradication 4 (May 2018): 11–12. http://dx.doi.org/10.1016/s2055-6640(20)30377-0.

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42

Levran, I., and A. Baror. "Characteristics of supply and demand in community physiotherapy services." Physiotherapy 101 (May 2015): e863-e864. http://dx.doi.org/10.1016/j.physio.2015.03.1689.

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43

Boysen, Ole. "Food Demand Characteristics in Uganda: Estimation and Policy Relevance." South African Journal of Economics 84, no. 2 (May 20, 2015): 260–93. http://dx.doi.org/10.1111/saje.12093.

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Chaiamarit, Kunjana, and Somboon Nuchprayoon. "Impact assessment of renewable generation on electricity demand characteristics." Renewable and Sustainable Energy Reviews 39 (November 2014): 995–1004. http://dx.doi.org/10.1016/j.rser.2014.07.102.

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Cheshire, Paul, and Stephen Sheppard. "Estimating the Demand for Housing, Land, and Neighbourhood Characteristics." Oxford Bulletin of Economics and Statistics 60, no. 3 (August 1998): 357–82. http://dx.doi.org/10.1111/1468-0084.00104.

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Gouvier, W. D., M. P. S. Maria, J. B. Pinkston, C. M. Morrow, S. R. Miller, K. Carmena, B. Hanna-Pladdy, and R. Plum. "Demand characteristics affect performance in mild head injury research." Archives of Clinical Neuropsychology 13, no. 1 (February 1, 1998): 137–38. http://dx.doi.org/10.1093/arclin/13.1.137a.

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Brusco, Michael J., and T. Reid Johns. "The effect of demand characteristics on labour scheduling methods." International Journal of Operations & Production Management 15, no. 1 (January 1995): 74–88. http://dx.doi.org/10.1108/01443579510077232.

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KAR, Mikail. "CHARACTERISTICS OF MOBILE COMMUNICATION DEMAND: THE CASE OF TURKEY." Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 19, no. 2 (July 13, 2019): 327–46. http://dx.doi.org/10.11616/basbed.v19i47045.547585.

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HEISE, GARY D., and PHILIP E. MARTIN. "???Leg spring??? characteristics and the aerobic demand of running." Medicine & Science in Sports & Exercise 30, no. 5 (May 1998): 750–54. http://dx.doi.org/10.1097/00005768-199805000-00017.

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Gul, Ferdinand A., and Ang Bee Lian. "The Effects of Demand Characteristics in Auditor Independence Studies." Asia-Pacific Journal of Accounting 1, no. 1 (December 1994): 133–46. http://dx.doi.org/10.1080/10293574.1994.10510470.

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