3 resultados para Factor-like Domains

em Cochin University of Science


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The influence of salinity on phytoplankton varies widely, because different species have different salinity preferences. Like marine and aquatic species, many phytoplankton species exhibit tolerance to certain salinity, beyond which, it can inhibit their growth. Light is the most important factor that influences phytoplankton growth. In aquatic environments (lakes, sea or estuary) the light incident on the surface is rapidly reduced exponentially with depth (Krik, 1994). In estuaries, the major factor influencing the light availability is the suspended particulate matter, which attenuates and scatters the light. The light changes with time of the day and the season, affecting the amount of light penetrating the water column. Similarly, biological factor like copepod grazing is a major factor influencing the standing crop of phytoplankton. The copepod can actively graze up to 75% of the phytoplankton biomass in a tropical estuary (Tan et. al., 2004). It is in the context that the present study investigates the salinity, light (physical factors) and copepod grazing (biological factor) phytoplankton as the factors controlling phytoplankton growth and distribution

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This study examines the behavioral factors that influence the Indian Investors to invest in the Real Estate Market. Among the various factors that affect the tendency of investors to invest in the real market, certain factors are greatly influenced the investors at greatest extend while others at least level. From this study it is revealed that motivation from the real estate developers and brokers (mean value- 3.46) is most influencing factor and happening of uncertain events (mean value- 1.75) is the least factor that influences the investors’ investment behavior. In this study, the behavioral factor like over confidence and the hypotheses regarding education, religion were analyzed and found that religious factor influences the Indian investors to invest in the real estate

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This research was undertaken with the primary objective of explaining differences in consumption of personal care products using personality variables. Several streams of research reported were reviewed and a conceptual model was developed. Theories on the relationship between self concept and behaviour was reviewed and the need to use individual difference variables to conceptualize and measure the salient dimensions of the self were emphasized. Theories relating to social comparison, eating disorders, role of idealized media images in shaping the self-concept, evidence on cosmetic surgery and persuasibility were reviewed in the study. These came from diverse fields like social psychology, use of cosmetics, women studies, media studies, self-concept literature in psychology and consumer research, and marketing. From the review three basic dimensions, namely self-evaluation, self-awareness and persuasibility were identified and they were posited to be related to consumption. Several personality variables from these conceptual domains were identified and factor analysis confirmed the expected structure fitting the basic theoretical dimensions. Demographic variables like gender and income were also considered.It was found that self-awareness measured by the variable public self-consciousness explain differences in consumption of personal care products. The relationship between public self-consciousness and consumption was found to be most conspicuous in cases of poor self-, evaluation measured by self-esteem. Susceptibility to advertising also was found to explain differences in consumption.From the research, it may be concluded that personality variables are useful for explaining consumption and they must be used together to explain and understand the process. There may not be obvious and conspicuous links between individual measures and behaviour in marketing. However, when used in proper combination and with the help oftheoretical models personality offers considerable explanatory power as illustrated in the seventy five percent accuracy rate of prediction obtained in binary logistic regression.