Although the primary role of the computer has been in processing and analysis of survey data, it has increasingly been used in data collection. Computer surveys are not exempt from a common problem: some refuse to participate. Many researchers and practitioners indicate the refusal problem is less for computer surveys, perhaps due to the novelty of the method. What has not been investigated is the refusal problem when on-line surveys are no longer novel. This research study examines the use of one form of computer-assisted data collection, the electronic or on-line survey, as an organizational research tool. The study utilized historical response data and administered an on-line survey to individuals known to be cooperative or uncooperative in other on-line surveys. It investigated nonresponse bias and response effects of typical responders, periodic participants, and typical refusers within a sample of corporate employees in a computer-interactive interviewing environment utilizing on-line surveys. The items measured included: participation, respondent characteristics, response speed, interview length, perceived versus actual interview length, quantity of data, item nonresponse, item response bias, consistency of response, extremity of response, and early and late response. It also evaluated factors reported as important when deciding to participate, preferred data collection method, and preferred time of display. Past participation, attitudes toward on-line organizational surveys, response burden, and response error were assessed. The overall completion rate of 55.7% was achieved in this study. All effort was made to encourage cooperation of all groups, including an invitation to participate, token, on-line pre-notification, 800 number support, two on-line reminders, support of temporary exit, and a paper follow-up survey. A significant difference in the participation of the three groups was found. Only three demographic variables were found to be significant. No significant differences were found in speed of response, interview length, quantity, item nonresponse, item response bias, consistency, and extremity. Significant differences were found in the perceived and actual times to complete the survey.

The purpose of this study was to examine the effects of changes in magnitude of negative reinforcement on multiple schedules with the guinea pigs. In both schedule components, the first response (lever press) after an average of 10 seconds was reinforced. In the constant component of this schedule the reinforcement magnitude (time-off from electric foot shock) was always 15 seconds; whereas, in the manipulated component the magnitude changed in the following sequence: 15, 7.5, 15, 30, and 15 seconds. All subjects showed a gradual decrease in response rate across baseline conditions. When behavioral effects were evaluated relative to this changing baseline, five of six subjects demonstrated that as the reinforcement magnitude decreased in one component, the response rates in both components decreased (negative induction). Likewise, when reinforcement magnitude increased in one component, all subjects showed behavioral induction. Specifically, three subjects showed increases in response rate in both components (positive induction), while two subjects showed decreases in response rate in v both components (negative induction). This research extends the generality of the behavioral induction phenomena on multiple schedules to in elude negative reinforcement with the guinea pig as a function of changes in reinforcement magnitude

Transplants of fetal neural tissue survive and develop in lesion cavities produced in adult rats. The present experiment tested the effect of grafting fetal hippocampal or brainstem tissue on the ability of rats with hippocampal lesions to perform on a differential reinforcement of low response rate (DRL) operant schedule. The DRL interval was 20 s. Eighty-six percent of the hippocampal grafts and 69% of the brainstem grafts developed to maturity. Inspection of sections stained using a silver technique for axis cylinders or taken from rats in which the mature transplant had been injected with Fast blue, indicated that these grafts formed connections with the host brain. Consistent with previous reports, rats with hippocampal lesions were impaired in performance of the DRL task. Rats given fetal grafts of hippocampal tissue into the hippocampal lesion site on the day of lesion production were significantly better in performance of the DRL requirement than were lesion-only rats or rats receiving grafts of fetal brainstem tissue. The results of this study confirm that grafts of fetal brain tissue can both develop in a lesion site in an adult brain and ameliorate lesion-induced behavioral deficits.

No description available.

Personalized communication is when the marketing message is adapted to each individual by using information from a databaseand utilizing it in the various, different media channels available today. That gives the marketer the possibility to create a campaign that cuts through today’s clutter of marketing messages and gets the recipients attention. PODi is a non-profit organization that was started with the aim of contributing knowledge in the field of digital printingtechnologies. They have created a database of case studies showing companies that have successfully implemented personalizedcommunication in their marketing campaigns. The purpose of the project was therefore to analyze PODi case studies with the main objective of finding out if/how successfully the PODi-cases have been and what made them so successful. To collect the data found in the PODi cases the authors did a content analysis with a sample size of 140 PODi cases from the year 2008 to 2010. The study was carried out by analyzing the cases' measurable ways of success: response rate, conversion rate, visited PURL (personalized URL:s) and ROI (Return On Investment). In order to find out if there were any relationships to be found between the measurable result and what type of industry, campaign objective and media vehicle that was used in the campaign, the authors put up different research uestions to explore that. After clustering and merging the collected data the results were found to be quite spread but shows that the averages of response rates, visited PURL and conversion rates were consistently very high. In the study the authors also collected and summarized what the companies themselves claim to be the reasons for success with their marketing campaigns. The resultshows that the creation of a personalized campaign is complex and dependent on many different variables. It is for instance ofgreat importance to have a well thought-out plan with the campaign and to have good data and insights about the customer in order to perform creative personalization. It is also important to make it easy for the recipient to reply, to use several media vehicles for multiple touch points and to have an attractive and clever design.

No description available.

Maturity models can help organizations understand their processes and thereby improve them.  The Capability Maturity Model (CMM) is a framework with five index-based levels that describes key elements in an effective software process. By identifying a level of process maturity, the model's guidelines can be followed for improvement in process performance. The aim of the study has been to study the performance of a customer service process and identify its process maturity, in order to find proposals for process improvements. By measuring the current performance with the help of combination of methods the process has been compared with CMM's two first levels. The study is based on the questions (1) How does the process perform today? (2) Which level of maturity in relation to CMM does the process have? and (3) How can the processes maturity be improved? Data has been collected through questionnaires from which the mean values have been compared with the company’s specified goals. Causes of process variation have been studied with the help of regression analyses, employee surveys, interviews with leaders and participatory observations. The performance and maturity of the process has been analysed using CMM's guidelines and the quality tool capability analysis. The study shows (1) that the process varies considerably and that there are assignable causes of variation in the process. The process is not consistent with the target values. The target values ​​have not been updated in recent years and therefore do not match the actual process capability. There are no well-defined routines to manage the process. (2) The Capability Index is estimated to 0.7 which places the process on the first level of CMM. This would indicate that the process is as unstable and uncontrolled. (3) In order to develop from the first level of CMM, process management principles and target values should be implemented. Control parameters should be implemented in the process both from an organizational perspective and as a customer perspective in order to create the conditions for a more stable process. The organization itself should also implement process work, new goals and routines for improvement work in the daily work.

The essential tenet of the behavioral momentum model (BMM) is that relative response rate decreases less in the face of disruption when maintained by a higher reinforcer density. Empirical support exists based on both response-dependent and response-independent reinforcement. In the present study the BMM was tested with college students in 4 multi-element experiments, each using 2 reinforcement schedules and a disrupter. Participants performed a categorical sort (by orientation) of triangles on a computer monitor. Sorting response rates were disrupted by a concurrent task, pressing the keyboard “T” key whenever 2 displayed changing numbers were equal. Initial training established fast (under VR 4) and slow (under DRL 5-s) sorting rates, and provided practice with the disrupting task. In Experiment 1 DRL 5-s provided higher reinforcer density, while in Experiment 2 VR 4 did. In Experiment 3 the higher total reinforcer density was achieved by adding VT 6-s to DRL 5-s while in Experiment 4 it was achieved by adding VT 12-s to VR 4. In all 4 experiments, sorting rate decreased with introduction of the disrupter. In Experiments 1 and 3, relative sorting rate decreased less under DRL based schedule (greater reinforcer density), supporting the BMM. However, in Experiments 2 and 4, relative sorting also decreased less under DRL (lower reinforcer density), contrary to the BMM prediction. Taken together, these data show greater relative resistance to change under DRL (versus VR), independent of reinforcer density. Thus, contrary to the BMM, the nature of the reinforcement schedule seemed to be the principal factor determining behavioral momentum. / May 2009

The essential tenet of the behavioral momentum model (BMM) is that relative response rate decreases less in the face of disruption when maintained by a higher reinforcer density. Empirical support exists based on both response-dependent and response-independent reinforcement. In the present study the BMM was tested with college students in 4 multi-element experiments, each using 2 reinforcement schedules and a disrupter. Participants performed a categorical sort (by orientation) of triangles on a computer monitor. Sorting response rates were disrupted by a concurrent task, pressing the keyboard “T” key whenever 2 displayed changing numbers were equal. Initial training established fast (under VR 4) and slow (under DRL 5-s) sorting rates, and provided practice with the disrupting task. In Experiment 1 DRL 5-s provided higher reinforcer density, while in Experiment 2 VR 4 did. In Experiment 3 the higher total reinforcer density was achieved by adding VT 6-s to DRL 5-s while in Experiment 4 it was achieved by adding VT 12-s to VR 4. In all 4 experiments, sorting rate decreased with introduction of the disrupter. In Experiments 1 and 3, relative sorting rate decreased less under DRL based schedule (greater reinforcer density), supporting the BMM. However, in Experiments 2 and 4, relative sorting also decreased less under DRL (lower reinforcer density), contrary to the BMM prediction. Taken together, these data show greater relative resistance to change under DRL (versus VR), independent of reinforcer density. Thus, contrary to the BMM, the nature of the reinforcement schedule seemed to be the principal factor determining behavioral momentum.

The essential tenet of the behavioral momentum model (BMM) is that relative response rate decreases less in the face of disruption when maintained by a higher reinforcer density. Empirical support exists based on both response-dependent and response-independent reinforcement. In the present study the BMM was tested with college students in 4 multi-element experiments, each using 2 reinforcement schedules and a disrupter. Participants performed a categorical sort (by orientation) of triangles on a computer monitor. Sorting response rates were disrupted by a concurrent task, pressing the keyboard “T” key whenever 2 displayed changing numbers were equal. Initial training established fast (under VR 4) and slow (under DRL 5-s) sorting rates, and provided practice with the disrupting task. In Experiment 1 DRL 5-s provided higher reinforcer density, while in Experiment 2 VR 4 did. In Experiment 3 the higher total reinforcer density was achieved by adding VT 6-s to DRL 5-s while in Experiment 4 it was achieved by adding VT 12-s to VR 4. In all 4 experiments, sorting rate decreased with introduction of the disrupter. In Experiments 1 and 3, relative sorting rate decreased less under DRL based schedule (greater reinforcer density), supporting the BMM. However, in Experiments 2 and 4, relative sorting also decreased less under DRL (lower reinforcer density), contrary to the BMM prediction. Taken together, these data show greater relative resistance to change under DRL (versus VR), independent of reinforcer density. Thus, contrary to the BMM, the nature of the reinforcement schedule seemed to be the principal factor determining behavioral momentum.