Market definition in industrial high-technology markets

This dissertation analyzes how marketers define markets in technology-based industries. One of the most important strategic decisions marketers face is determining the optimal market for their products. Market definition is critical in dynamic high technology markets characterized by high levels of market and technological uncertainty. Building on literature from marketing and related disciplines, this research is the first in-depth study of market definition in industrial markets. Using a national, probability sample stratified by firm size, 1,000 marketing executives in nine industries (automation, biotechnology, computers, medical equipment and instrumentation, pharmaceuticals, photonics, software, subassemblies and components, and telecommunications) were surveyed via a mail questionnaire. A 20.8% net response rate yielding 203 surveys was achieved. The market structure-conduct-performance (SCP) paradigm from industrial organization provided a conceptual basis for testing a causal market definition model via LISREL. A latent exogenous variable (competitive intensity) and four latent endogenous variables (marketing orientation, technological orientation, market definition criteria, and market definition success) were used to develop and test hypothesized relationships among constructs. Research questions relating to market redefinition, market definition characteristics, and internal (within the firm) and external (competitive) market definition were also investigated. Market definition success was found to be positively associated with a marketing orientation and the use of market definition criteria. Technological orientation was not significantly related to market definition success. Customer needs were the key market definition characteristic to high-tech firms (technology, competition, customer groups, and products were also important). Market redefinition based on changing customer needs was the most effective of seven strategies tested. A majority of firms regularly defined their market at the corporate and product-line level within the firm. From a competitive perspective, industry, industry sector, and product-market definitions were used most frequently.

Essays on firm behavior and firm performance

This dissertation comprises three individual chapters in an effort to examine different explanatory variables that affect firm performance. Chapter Two proposes an additional determinant of firm survival. Based on a detailed examination of firm survival in the British automobile industry between 1895 and 1970, we conclude that a firm's selection of submarket (defined by quality level) influenced survival. In contrast to findings for the US automobile industry, there is no evidence of first-mover advantage in the market as a whole. However, we do find evidence of first-mover advantage after conditioning on submarket choice. Chapter Three examines the effects of product line expansion on firm performance in terms of survival time. Based on a detailed examination of firm survival time in the British automobile industry between 1895 and 1970, we find that diversification exerts a positive effect on firm survival. Furthermore, our findings support the literature with respect to the impacts of submarket types, pre-entry experience, and timing of entry on firm survival time. Chapter Four examines corporate diversification in U.S. manufacturing and service firms. We develop measures of how related a firm's diverse activities are using input-output data and the NAILS classification to construct indexes of "vertical relatedness" and "complementarity". Strong relationships between these two measures are found. We utilize profitability and excess value as the measure for firm performance. Econometric analysis reveals that there is no relationship between the degree of relatedness of diversification and firm performance for the study period.

Assessing the effectiveness of five-year mechanical engineering technology programs of junior colleges in Taiwan, R.O.C.: An application of the CIPP evaluation model

The purpose of this study was to evaluate the mechanical engineering technology curriculum effectiveness at the junior college in Taiwan by using the CIPP evaluation model. The study concerned the areas of the curriculum, curriculum materials, individualized instruction, support services, teaching effectiveness, student achievement, and job performance. A descriptive survey method was used with questionnaires for data collection from faculty, students, graduates, and employers.^ All categories of respondents tended to agree that the curriculum provides appropriate occupational knowledge and skills. Students, graduates, and faculty tended to be satisfied with the curriculum; faculty tended to be satisfied with student achievement; graduates tended to be satisfied with their job preparation; and employers were most satisfied with graduates' job performance.^ Conclusions were drawn in the context, input, process, and product of the CIPP model. In Context area: Students were dissatisfied with curriculum flexibility in students characteristics. Graduates were dissatisfied with curriculum design for student's adaptability in new economic and industrial conditions; practicum flexibility in student characteristics; and course overlap. Both students and graduates were dissatisfied with practicum credit hours. Both faculty and students were dissatisfied with the number of required courses.^ In Input area: Students, faculty, and graduates perceived audiovisuals and manipulative aids positively. Faculty and students perceive CAI implementation positively. Students perceived textbooks negatively.^ In Process area: Faculty, students, and graduates perceived all support service negatively. Faculty tended to perceive the ratios of graduates who enter advanced study and related occupation, and who passed the professional skills certification, negatively. Students tended to perceive teaching effectiveness in terms of instructional strategies, the quality of instruction, overall suitability, and receivable, negatively. Graduates also tended to identify the instructional strategies as a negative perception. Faculty and students perceived curriculum objectives and practicum negatively. Both faculty and students felt that instructors should be more interested in making the courses a useful learning experience.^ In Product area: Employers were satisfied with graduates' academic preparation and job performance, adaptability, punctuality, and their ability to communicate, cooperate, and meet organization needs. Graduates were weak in terms of equipment familiarity and supervisory ability.^ In sum, the curriculum of the five-year mechanical engineering technology programs of junior college in Taiwan has served adequately up to this time in preparing a work force to enter industry. It is now time to look toward the future and adapt the curriculum and instruction for the future needs of this high-tech society. ^

Parameter design considerations in precision engineering

Parameter design is an experimental design and analysis methodology for developing robust processes and products. Robustness implies insensitivity to noise disturbances. Subtle experimental realities, such as the joint effect of process knowledge and analysis methodology, may affect the effectiveness of parameter design in precision engineering; where the objective is to detect minute variation in product and process performance. In this thesis, approaches to statistical forced-noise design and analysis methodologies were investigated with respect to detecting performance variations. Given a low degree of process knowledge, Taguchi's methodology of signal-to-noise ratio analysis was found to be more suitable in detecting minute performance variations than the classical approach based on polynomial decomposition. Comparison of inner-array noise (IAN) and outer-array noise (OAN) structuring approaches showed that OAN is a more efficient design for precision engineering. ^

Analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Blood-brain barrier in vitro model: A tissue engineering approach and validation

This dissertation evaluated the feasibility of using commercially available immortalized cell lines in building a tissue engineered in vitro blood-brain barrier (BBB) co-culture model for preliminary drug development studies. Mouse endothelial cell line and rat astrocyte cell lines purchased from American Type Culture Collections (ATCC) were the building blocks of the co-culture model. An astrocyte derived acellular extracellular matrix (aECM) was introduced in the co-culture model to provide a novel in vitro biomimetic basement membrane for the endothelial cells to form endothelial tight junctions. Trans-endothelial electrical resistance (TEER) and solute mass transport studies were engaged to quantitatively evaluate the tight junction formation on the in-vitro BBB models. Immuno-fluorescence microscopy and Western Blot analysis were used to qualitatively verify the in vitro expression of occludin, one of the earliest discovered tight junction proteins. Experimental data from a total of 12 experiments conclusively showed that the novel BBB in vitro co-culture model with the astrocyte derived aECM (CO+aECM) was promising in terms of establishing tight junction formation represented by TEER values, transport profiles and tight junction protein expression when compared with traditional co-culture (CO) model setups and endothelial cells cultured alone. Experimental data were also found to be comparable with several existing in vitro BBB models built from various methods. In vitro colorimetric sulforhodamine B (SRB) assay revealed that the co-cultured samples with aECM resulted in less cell loss on the basal sides of the insert membranes than that from traditional co-culture samples. The novel tissue engineering approach using immortalized cell lines with the addition of aECM was proven to be a relevant alternative to the traditional BBB in vitro modeling.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

On-Line Real-Time Service-Oriented Task Scheduling Using TUF

We present our approach to real-time service-oriented scheduling problems with the objective of maximizing the total system utility. Different from the traditional utility accrual scheduling problems that each task is associated with only a single time utility function (TUF), we associate two different TUFs—a profit TUF and a penalty TUF—with each task, to model the real-time services that not only need to reward the early completions but also need to penalize the abortions or deadline misses. The scheduling heuristics we proposed in this paper judiciously accept, schedule, and abort real-time services when necessary to maximize the accrued utility. Our extensive experimental results show that our proposed algorithms can significantly outperform the traditional scheduling algorithms such as the Earliest Deadline First (EDF), the traditional utility accrual (UA) scheduling algorithms, and an earlier scheduling approach based on a similar model.

Characterization and modeling of multi-conductor transmission line using Finite-difference Time-Domain method

A two-dimensional, 2D, finite-difference time-domain (FDTD) method is used to analyze two different models of multi-conductor transmission lines (MTL). The first model is a two-conductor MTL and the second is a threeconductor MTL. Apart from the MTL's, a three-dimensional, 3D, FDTD method is used to analyze a three-patch microstrip parasitic array. While the MTL analysis is entirely in time-domain, the microstrip parasitic array is a study of scattering parameter Sn in the frequency-domain. The results clearly indicate that FDTD is an efficient and accurate tool to model and analyze multiconductor transmission line as well as microstrip antennas and arrays.

Analytical Approximations to Predict Performance Measures of Manufacturing Systems with Job Failures and Parallel Processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.