Anderson, J. & Narus, J. (2004) Business Market Management. Understanding, Creating, and Delivering Value (in line with Business Marketing: Understand What Customers Value by Anderson and Narus 1998) 2nd ed. Upper Saddle River, JN: Prentice-Hall
Focus groups’ measurement from within presence of customer value models and its assessments, as the focus group allows using of respondents for research success these comprises of people who are involved in the research process that is key to the overall essence of the study as presented by proponents Anderson and Narus that implies to business marketing. There assess measure value in practice, it is crucial to have a shared understanding of exactly what value is in business markets. It provide brief explanation of what value means as for the business markets is the worth in monetary terms of the technical, economic, service, social benefits customer company receives in exchange for the price it pays for market offering. Focus group utilization can change customer’s incentive to purchase that market offering. The need to capture essence of definition of value in the following equation: (Values 2 Prices) > (Valuea 2 Pricea) Values and Prices are the value and price of the supplier’s market offering, and Valuea and Pricea are the value and price of the next best alternative. Simply put, the equation conveys that the customer’s incentive to purchase a supplier’s offering must exceed its incentive to pursue the next best alternative. The field value assessment, gathering data whenever possible is common way to build customer value models, not all situations lend themselves to it. Indeed, the only way to obtain information for value model is to rely on customer perceptions. The result of assessments may not be as precise as those calculated from field value assessments; nonetheless, they can be quite effective. The article adheres to assessment of customer value models as the latter points to the measurement process in lieu to products and services for customers as look to purchasing as a way to increase profits and pressure suppliers to reduce prices. To persuade customers to focus on total costs rather than simply on acquisition price, a supplier must have an accurate understanding of what the customer value, and would value. Thus, markets and doing business based on value delivered gives suppliers the means to get an equitable return for their efforts. The essence of customer value management is to deliver superior value and get an equitable return for it, both of which depend on value assessment. Assessing and truly understanding value in business markets is the beginning of the path to profitable fun.
Clandinin, D. & Connelly, M. (1999). Narrative inquiry: Experience and story in qualitative research. San Francisco: Jossey-Bass
Measuring of narrative inquiry from within qualitative research is vital factor this was not really enclosed by statistics application but rather on descriptive manner that places in a nature of understanding value perception as essential in the study. There implies to the assumption that participants enter the inquiry field in the midst of living their stories and their lives continue. Furthermore, places in which they live and work, their classrooms, their schools, and their communities, are also in the midst when researchers arrive. There is sensitive observation and sometimes intimate co-participation in the intermingling of narratives. Once the narrative process takes hold, the narrative inquiry space pulsates with movements back and forth through time and along a continuum of personal and social considerations. One of the starting points for narrative inquiry is the researcher’s own narrative of experience. The task of composing own narratives of experience is central to narrative inquiry as refer to as composing narrative beginnings as researcher begins his inquiries. Thinking about an inquiry in narrative terms allows study to conceptualize inquiry experience as storied one on several levels. When researchers enter the field, they experience shifts and changes, constantly negotiating, constantly reevaluating, maintaining flexibility and openness to an ever changing landscape. Good narrative working relationships carry with them a sad and wistful sense born of the possibility of temporariness. One of the methodological principles taught in quantitative analysis courses was to specify hypotheses to be tested in research. It does not work like that in narrative inquiry, researchers lay out experimental plots, create their own inquiry fields, manipulate variables according to the working hypotheses in narrative inquiry researcher tends to be at the other end of the continuum from the controlled-plot hypothesis tester. The researcher enter landscape and joins an ongoing professional life. One thing that narrative inquirers do is quickly learn that even if they are familiar with the kind of landscape.
De Bono, E. (2006). Perceived Value: When considering value, perception can be as important as reality, from thinking managers dot com
Descriptive statistics apply as there indicates that a value is not a value unless it is perceived to be one. No matter how real a value may be, it has no value at all until the value is perceived. As one assessment of real value may be very different from another person, there becomes difficult to draw line between effective sales talk and conning people. Even an important area like insurance has many options. The assessment of risks varies enormously between age groups as there must perceived value as matching real value with perceived value is very difficult either way. Perceived value is sometimes higher than the real value, but sometimes lower, once perception is established it tends to remain in force until it is specifically removed, that perception in the meantime, the perception survived intact, belief guides perception in such a way that it reinforces the belief as better to start separate parallel perception than to try to change perception through head on engineering as because perception lies in the area of water logic rather than identity logic.
Kaplan, B. & Maxwell, J.A. (1994): Qualitative Research Methods for Evaluation Computer Information Systems. In Anderson J G, C E Aydin and S J Jay (eds) (1994): Evaluating Health Care Information System: Methods and Applications. Sage. Thousand Oaks, CA, pp 45-68
Statistics that support qualitative research methodology that include evaluations of computer systems and information technology. The goal of qualitative research understands issues or particular situations by investigating the perspectives and behavior of the people in these situations and the context within which they act. The qualitative research is conducted in natural settings and uses data in the form of words rather than numbers. Qualitative data are gathered primarily from observations, interviews, and documents, analyzed by variety of systematic techniques. The research approach is useful in understanding causal processes, in facilitating action based on the research results. Qualitative methods are primarily inductive. Hypotheses are developed during the study so as to take into account what is being learned about the setting and the people in it. Qualitative methods may be combined with quantitative methods in conducting study. Validity threats are addressed primarily during data collection and analysis. To determine what might be important to measure, why measured results are measured easily as well as to examine causal processes, not simply what causal relationships exists. Qualitative research typically involves systematic and detailed study of individuals in natural settings, instead of in settings contrived by the researcher, often using open-ended interviews intended to elicit detailed, in-depth accounts of the interviewee’s experiences and perspectives on specific issues, situations, or events. Qualitative methods employ data in the form of words: transcripts of open-ended interviews, written observational descriptions of activities and conversations, documents and other artifacts of people’s actions
Salo, J. & Tähtinen, J. (2004). Retailer Use of Permission-Based Mobile Advertising, Unpublished manuscript.
Content analysis along with case study approach from within M advertising is known, the empirical research was derived from Smart Rotuaari service system, retailers use web portal to send the adverts, which are delivered through WLAN network to consumers using mobile devices. There focus on permission based mobile advertising and its specific features that should be considered when designing and targeting mobile advertising. The empirical part of the chapter analyses data from a field trial where Finnish retailers were able to use mobile advertising. The empirical data is obtained through the use of content analysis. Stats that deal with functional framework for field trials as there empirically evaluate technology new mobile services and that content analysis is the statistical tool used, see how well the retailers were able to meet the demands of the media and the customers, we will explore the targeting and the content of m-adverts by content analysis. Content analysis is the standard analytical tool used for advertising studies (Kassarjian, 1977) as the unit of analysis was each m-advert that was saved to the service system. However, researchers did not include the number of times the advert had been sent to receivers in the analysis as it might skew the results (Stern and Resnik, 1991). The aim of procedure was to achieve higher objectivity, as for reliability, since the coders agreed with all the decisions made, no measures of inter judge reliability were calculated (Perreault and Leigh, 1989). Based on data, retailers and their advertising agencies have to solve the question of how to fit the message and the format into the context of m-advertising (Kiani, 1998; Kunoe, 1998). In the empirical trial, m-adverts resembled traditional newspaper adverts that target groups of people. The content of the m-adverts was not well personalized; few attempts at interaction with the target person were made. Most of the m-ads offered information of a type, that a person living in Oulu had no need for and only a few had entertaining elements attached to them. The RGBW customers should be encouraged in marketing, by offering them added value to share with friends and family.
Sawant, A. P. & Healey, C. G. (2005). A Survey of Display Device Properties and Visual Acuity for Visualization. Knowledge Discovery Lab, Department of Computer Science, North Carolina State University
Survey related statistics and information this is ideal by means of having comprehensive research that assume visualization as advent of computers with high processing power has led to the generation of huge datasets containing large numbers of elements, where each element is often characterized by multiple attributes. There led to critical need for ways to explore and analyze large, multidimensional information spaces. Visualization lends itself well to the challenge by enabling users to visually explore, analyze also discover patterns within their data. Most visualization techniques are based on the assumption that the display device has sufficient resolution, that visual acuity is adequate to complete the analysis tasks. There investigate the strengths and limitations of visual system, in particular to understand how basic visual properties like color, texture, motion as distinguished forming basis for new research on how to best match visualization design to display’s physical characteristics and viewer’s visual abilities. An obvious question is: how can we define this kind of visualization hierarchy? The answer will depend on how many pixels are needed for a visual feature to represent information effectively, and how much physical size is needed for visual system to accurately identify and interpret the visual feature. The survey summarizes what is currently known about these topics, and offers suggestions on how future research could fill in missing details, and then combine the results into a working visualization system. Understanding limits on display resolution and visual acuity will allow researchers to better validate a given visualization technique, and characterize to what extent the technique saturates “visual bandwidth”. When designing visualization, properties of the dataset and the visual features used to represent its data elements must be carefully controlled to produce an effective result. There review the important physical characteristics of display devices as well as discusses physical vision and visual acuity. The focus on properties of different visual features such as color, texture as well as motion along with conclusion and future work.
Varshney, U. & Vetter, R. (2002). Mobile Commerce: Framework, Applications and Networking Support, Mobile Networks and Applications, 7 (3), 185-98.
Qualitative discussion in accordance to advances in e-commerce have resulted in significant progress towards strategies, requirements, development of e-commerce applications as this will be in support of mobile commerce. Also, to make mobile commerce application reality, we address networking requirements, discuss support from wireless carriers, and present some open research problems. The article proponents examine how new m-commerce applications can be designed and supported by wireless and mobile networks and mobile middleware. How well these applications become adopted by business will depend on how fast these applications can be deployed, cost value ratio, acceptance of new technologies by users and businesses based on easy to use and uniform interfaces, building of trust necessary to conduct m-commerce transactions while on the move. There, strongly believe that with the widespread deployment of wireless technologies, the next phase of electronic business growth will be in the area of wireless and mobile e-commerce. Thus, aware that consensus within business and industry of such future applications is still in its infancy. However, there interested in examining those future applications and technologies that will form the next frontier of electronic commerce. Upon following the framework, single entity is not forced to do everything to build m-commerce systems rather build on the functionalities provided by others. For statistics tool upon analyzing data as based on the research method known in the dissertation, five point scaling statistics analysis will be realized, applying to Likert scale interpretation. This will be in form of statements from within frequency ratios are determined in percentage adaptation such as for example pattern as below
1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree
widespread deployment of wireless technologies, the next phase of electronic business growth will be in the area of wireless and mobile e-commerce
1 2 3 4 5
the only way to obtain information for value model is to rely on customer perceptions
1 2 3 4 5
most visualization techniques are based on the assumption that the display device has sufficient resolution
1 2 3 4 5
markets and doing business based on value delivered gives suppliers the means to get an equitable return for their efforts
1 2 3 4 5
retailers and their advertising agencies have to solve the question of how to fit the message and the format into the context of m-advertising
1 2 3 4 5
Likert (1932) proposed summated scale for the assessment of survey respondent’s attitudes. Individual items in Likert’s sample scale had five response alternative such as, Strongly approve, Approve, Undecided, Disapprove, Strongly disapprove. He noted that descriptors could be anything, not necessary to have negative and positive responses. He implies that the number of alternatives is also open to manipulation. Indeed, there contemporary work using many classifications besides the traditional five point classifications; some researchers use an even number of categories, deleting the neutral response. Likert’s original work assumed an attitude scale would first be pilot tested for reliability assessment of the individual items. The reliability assessment might use the correlation between the item score and the total. Goldstein and Hersen (1984) states that, “level of scaling obtained from the Likert procedure is rather difficult to determine. The scale is clearly at least ordinal. Those persons with the higher level properties in the natural variable are expected to get higher scores than those persons from lower properties. In order to achieve an interval scale, the properties on the scale variable have to correspond to differences in the trait on the natural variable, seems unlikely that the categories formed by the misalignment of the five responses will all be equal, the interval scale assumption seems unlikely” (p. 52). Some statisticians have no problem with analyzing individual Likert-type items using t-tests or other parametric procedures (Sisson and Stocker, 1989) provided the primary interest is in location only. If the survey process produces order and normality, normal theory procedures can be employed regardless of the attained measurement level.
References
Goldstein G and Hersen M (1984) Handbook of Psychological Assessment. New York: Pergamon Press
Kassarjian H (1977) Content Analysis in Consumer Research. Journal of Consumer Research, 4, 8-18
Kiani GR (1998) Marketing opportunities in the digital world. Internet Research: Electronic Networking Applications and Policy, 8, 185–194
Kunoe G (1998) On the ability of ad agencies to assist in developing one-to-one communication. Measuring the core dialogue. European Journal of Marketing, 32, 1124–1137
Likert R (1932) A Technique for the Measurement of Attitudes. New York:
Archives of Psychology
Perreault W D and Leigh L (1989) Reliability of Nominal Data Base on Qualitative Judgments. Journal of Marketing Research, 26, 135-148
Sisson D A and Stocker H R (1989) Analyzing and interpreting Likert-type survey data. The Delta Pi Epsilon Journal, 31(2): 81-85
Stern B L and Resnik A J (1991) Information Content in Television Advertising, A Replication and Extension. Journal of Advertising Research, 30, 36-46
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