Resource, Strategies, Location Determinants, And Host Country Location Choice By Emerging Market Firms

The extant literature had studied the determinants of the firms’ location decisions with help of host country characteristics and distances between home and host countries. Firm resources and its internationalization strategies had found limited attention in this literature. To address this gap, the research question in this dissertation was whether and how firms’ resources and internationalization strategies impacted the international location decisions of emerging market firms. To explore the research question, data were hand-collected from Indian software firms on their location decisions taken between April 2000 and March 2009. To analyze the multi-level longitudinal dataset, hierarchical linear modeling was used. The results showed that the internationalization strategies, namely market-seeking or labor-seeking had direct impact on firms’ location decision. This direct relationship was moderated by firm resource which, in case of Indian software firms, was the appraisal at CMMI level-5. Indian software firms located in developed countries with a market-seeking strategy and in emerging markets with a labor-seeking strategy. However, software firms with resource such as CMMI level-5 appraisal, when in a labor-seeking mode, were more likely to locate in a developed country over emerging market than firms without the appraisal. Software firms with CMMI level-5 appraisal, when in market-seeking mode, were more likely to locate in a developed country over an emerging market than firms without the appraisal. It was concluded that the internationalization strategies and resources of companies predicted their location choices, over and above the variables studied in the theoretical field of location determinants.

A Retrospective-Longitudinal Examination of the Relationship between Apportionment of Seat Time in Community-College Algebra Courses and Student Academic Performance

During the past decade, there has been a dramatic increase by postsecondary institutions in providing academic programs and course offerings in a multitude of formats and venues (Biemiller, 2009; Kucsera & Zimmaro, 2010; Lang, 2009; Mangan, 2008). Strategies pertaining to reapportionment of course-delivery seat time have been a major facet of these institutional initiatives; most notably, within many open-door 2-year colleges. Often, these enrollment-management decisions are driven by the desire to increase market-share, optimize the usage of finite facility capacity, and contain costs, especially during these economically turbulent times. So, while enrollments have surged to the point where nearly one in three 18-to-24 year-old U.S. undergraduates are community college students (Pew Research Center, 2009), graduation rates, on average, still remain distressingly low (Complete College America, 2011). Among the learning-theory constructs related to seat-time reapportionment efforts is the cognitive phenomenon commonly referred to as the spacing effect, the degree to which learning is enhanced by a series of shorter, separated sessions as opposed to fewer, more massed episodes. This ex post facto study explored whether seat time in a postsecondary developmental-level algebra course is significantly related to: course success; course-enrollment persistence; and, longitudinally, the time to successfully complete a general-education-level mathematics course. Hierarchical logistic regression and discrete-time survival analysis were used to perform a multi-level, multivariable analysis of a student cohort (N = 3,284) enrolled at a large, multi-campus, urban community college. The subjects were retrospectively tracked over a 2-year longitudinal period. The study found that students in long seat-time classes tended to withdraw earlier and more often than did their peers in short seat-time classes (p < .05). Additionally, a model comprised of nine statistically significant covariates (all with p-values less than .01) was constructed. However, no longitudinal seat-time group differences were detected nor was there sufficient statistical evidence to conclude that seat time was predictive of developmental-level course success. A principal aim of this study was to demonstrate—to educational leaders, researchers, and institutional-research/business-intelligence professionals—the advantages and computational practicability of survival analysis, an underused but more powerful way to investigate changes in students over time.

Assessing the construct validity of a measure of learning agility

This study examined the construct validity of the Choices questionnaire that purported to support the theory of Learning Agility. Specifically, Learning Agility attempts to predict an individual's potential performance in new tasks. The construct validity will be measured by examining the convergent/discriminant validity of the Choices Questionnaire against a cognitive ability measure and two personality measures. The Choices Questionnaire did tap a construct that is unique to the cognitive ability and the personality measures, thus suggesting that this measure may have considerable value in personnel selection. This study also examined the relationship of this pew measure to job performance and job promotability. Results of this study found that the Choices Questionnaire predicted job performance and job promotability above and beyond cognitive ability and personality. Data from 107 law enforcement officers, along with two of their co-workers and a supervisor resulted in a correlation of .08 between Learning Agility and cognitive ability. Learning Agility correlated .07 with Learning Goal Orientation and. 17 with Performance Goal Orientation. Correlations with the Big Five Personality factors ranged from −.06 to. 13 with Conscientiousness and Openness to Experience, respectively. Learning Agility correlated .40 with supervisory ratings of job promotability and correlated .3 7 with supervisory ratings of overall job performance. Hierarchical regression analysis found incremental validity for Learning Agility over cognitive ability and the Big Five factors of personality for supervisory ratings of both promotability and overall job performance. A literature review was completed to integrate the Learning Agility construct into a nomological net of personnel selection research. Additionally, practical applications and future research directions are discussed. ^

Examining a proposed job retention model for adult workers with mental retardation in supported employment

This quantitative study investigated the predictive relationships and interaction between factors such as work-related social behaviors (WRSB), self-determination (SD), person-job congruency (PJC), job performance (JP), job satisfaction (JS), and job retention (JR). A convenience sample of 100 working adults with MR were selected from supported employment agencies. Data were collected using a survey test battery of standardized instruments. The hypotheses were analyzed using three multiple regression analyses to identify significant relationships. Beta weights and hierarchical regression analysis determined the percentage of the predictor variables contribution to the total variance of the criterion variables, JR, JP, and JS. ^ The findings highlight the importance of self-determination skills in predicting job retention, satisfaction, and performance for employees with MR. Consistent with the literature and hypothesized model, there was a predictive relationship between SD, JS and JR. Furthermore, SD and PJC were predictors of JP. SD and JR were predictors of JS. Interestingly, the results indicated no significant relationship between JR and JP, or between JP and JS, or between PJC and JS. This suggests that there is a limited fit between the hypothesized model and the study's findings. However, the theoretical contribution made by this study is that self-determination is a particularly relevant predictor of important work outcomes including JR, JP, and JS. This finding is consistent with Deci's (1992) Self-Determination Theory and Wehmeyer's (1996) argument that SD skills in individuals with disabilities have important consequences for the success in transitioning from school to adult and work life. This study provides job retention strategies that offer rehabilitation and HR professionals a useful structure for understanding and implementing job retention interventions for people with MR. ^ The study concluded that workers with mental retardation who had more self-determination skills were employed longer, more satisfied, and better performers on the job. Also, individuals whose jobs were matched to their interests and abilities (person-job congruency) were better at self-determination skills. ^

Employee engagement: An examination of antecedent and outcome variables

This nonexperimental, correlational study (N = 283) examined the relation among job fit, affective commitment, psychological climate, discretionary effort, intention to turnover, and employee engagement. An internet-based self-report survey battery of six scales were administered to a heterogeneous sampling of organizations from the fields of service, technology, healthcare, retail, banking, nonprofit, and hospitality. Hypotheses were tested through correlational and hierarchical regression analytic procedures. Job fit, affective commitment, and psychological climate were all significantly related to employee engagement and employee engagement was significantly related to both discretionary effort and intention to turnover. For the discretionary effort model, the hierarchical regression analysis results suggested that the employees who reported experiencing a positive psychological climate were more likely to report higher levels of discretionary effort. As for the intention to turnover model, the hierarchical regression analysis results indicated that affective commitment and employee engagement predicted lower levels of an employee’s intention to turnover. The regression beta weights ranged from to .43 to .78, supporting the theoretical, empirical, and practical relevance of understanding the impact of employee engagement on organizational outcomes. Implications for HRD theory, research, and practice are highlighted as possible strategic leverage points for creating conditions that facilitate the development of employee engagement as a means for improving organizational performance.

Chemical characteristics of dissolved organic matter in an oligotrophic subtropical wetland/estuarine ecosystem

Fluorescence properties of whole water samples and molecular characteristics of ultrafiltrated dissolved organic matter (UDOM > 1,000 D) such as lignin phenol and neutral sugar compositions and 13C nuclear magnetic resonance (NMR) spectra were determined along a freshwater to marine gradient in Everglades National Park. Furthermore, UDOM samples were categorized by hierarchical cluster analysis based on their pyrolysis gas chromatography/mass spectrometry products. Fluorescence properties suggest that autochthonous DOM leached/exuded from biomass is quantitatively important in this system. 13C NMR spectra showed that UDOM from the oligotrophic Taylor Slough (TS) and Florida Bay (FB) ecosystems has low aromatic C (13% ± 3% for TS; 2% ± 2% for FB) and very high O-alkyl C (54% ± 4% for TS; 75% ± 4% for FB) concentrations. High O-alkyl C concentrations in FB suggest seagrass/phytoplankton communities as dominant sources of UDOM. The amount of neutral sugars was not appreciably different between the TS and FB sites (115 ± 12 mg C g C-1 UDOM) but their concentrations suggest a low level of diagenesis and high production rates of this material in this oligotrophic environment. Total yield of lignin phenols (vanillyl + syringyl phenols) in TS was low (0.20–0.39 mg 100 mg C-1 UDOM) compared with other riverine environments and even lower in FB (0.04–0.07 mg 100 mg C-1 UDOM) and could be a result of photodegradation and/or dilution by other utochthonous DOM. The high O-alkyl and low aromatic nature of this UDOM suggests significant biogenic inputs (as compared with soils) and limited bioavailability in this ecosystem.

The relationship between the secondary mathematics curriculum, college persistence, and success at an urban community college

According to Venezia, Kirst, and Antonio (2003) and Barth’s 2002 Thinking K16 Ticket to Nowhere report, the disconnect between K-12 and postsecondary education was a contributing factor to high attrition rates. Since mathematics emerged as a primary concern for college readiness, Barth (2002) called for improving student transitions from K-12 to postsecondary institutions through the use of state or local data. The purpose of the present study was to analyze mathematics course-taking patterns of secondary students in a local context and to evaluate high school characteristics in order to explore their relationships with Associate degree attainment or continuous enrollment at an urban community college. Also, this study extended a national study conducted by Clifford Adelman (The Toolbox Revisited, 2006) as it specifically focused on community college students that were not included his study. Furthermore, this study used the theoretical framework that human capital, social capital, and cultural capital influence habitus—an individual’s or a group’s learned inclination to behave within the parameters of the imposed prevailing culture and norms. Specifically, the school embedded culture as it relates to tracking worked as a reproduction tool of ultimate benefit for the privileged group (Oakes, 1994). ^ Using multilevel analysis, this ex post facto study examined non-causal relationships between math course-taking patterns and college persistence of public high school graduates who enrolled at the local community college for up to 6 years. One school-level variable (percent of racial/ethnic minorities) and 7 student-level variables (community college math proportion, remedial math attempts, race, gender, first-year credits earned, socioeconomic status, and summer credits earned) emerged as predictors for college persistence. Study results indicated that students who enter higher education at the community college may have had lower opportunities to learn and therefore needed higher levels of remediation, which was shown to detract students from degree completion. Community college leaders are called to partner with local high schools with high percentages of racial/ethnic minorities to design academic programs aimed at improving the academic preparation of high school students in mathematics and promote student engagement during the first year and summers of college. ^

DIMUSE: An integrated framework for distributed multimedia system with database management and security support

With the recent explosion in the complexity and amount of digital multimedia data, there has been a huge impact on the operations of various organizations in distinct areas, such as government services, education, medical care, business, entertainment, etc. To satisfy the growing demand of multimedia data management systems, an integrated framework called DIMUSE is proposed and deployed for distributed multimedia applications to offer a full scope of multimedia related tools and provide appealing experiences for the users. This research mainly focuses on video database modeling and retrieval by addressing a set of core challenges. First, a comprehensive multimedia database modeling mechanism called Hierarchical Markov Model Mediator (HMMM) is proposed to model high dimensional media data including video objects, low-level visual/audio features, as well as historical access patterns and frequencies. The associated retrieval and ranking algorithms are designed to support not only the general queries, but also the complicated temporal event pattern queries. Second, system training and learning methodologies are incorporated such that user interests are mined efficiently to improve the retrieval performance. Third, video clustering techniques are proposed to continuously increase the searching speed and accuracy by architecting a more efficient multimedia database structure. A distributed video management and retrieval system is designed and implemented to demonstrate the overall performance. The proposed approach is further customized for a mobile-based video retrieval system to solve the perception subjectivity issue by considering individual user's profile. Moreover, to deal with security and privacy issues and concerns in distributed multimedia applications, DIMUSE also incorporates a practical framework called SMARXO, which supports multilevel multimedia security control. SMARXO efficiently combines role-based access control (RBAC), XML and object-relational database management system (ORDBMS) to achieve the target of proficient security control. A distributed multimedia management system named DMMManager (Distributed MultiMedia Manager) is developed with the proposed framework DEMUR; to support multimedia capturing, analysis, retrieval, authoring and presentation in one single framework.

A Q factor analysis of college undergraduate students' study behaviors

The purpose of this study was to better understand the study behaviors and habits of university undergraduate students. It was designed to determine whether undergraduate students could be grouped based on their self-reported study behaviors and if any grouping system could be determined, whether group membership was related to students’ academic achievement. A total of 152 undergraduate students voluntarily participated in the current study by completing the Study Behavior Inventory instrument. All participants were enrolled in fall semester of 2010 at Florida International University. The Q factor analysis technique using principal components extraction and a varimax rotation was used in order to examine the participants in relation to each other and to detect a pattern of intercorrelations among participants based on their self-reported study behaviors. The Q factor analysis yielded a two factor structure representing two distinct student types among participants regarding their study behaviors. The first student type (i.e., Factor 1) describes proactive learners who organize both their study materials and study time well. Type 1 students are labeled “Proactive Learners with Well-Organized Study Behaviors”. The second type (i.e., Factor 2) represents students who are poorly organized as well as being very likely to procrastinate. Type 2 students are labeled Disorganized Procrastinators. Hierarchical linear regression was employed to examine the relationship between student type and academic achievement as measured by current grade point averages (GPAs). The results showed significant differences in GPAs between Type 1 and Type 2 students at the .05 significance level. Furthermore, student type was found to be a significant predictor of academic achievement beyond and above students’ attribute variables including sex, age, major, and enrollment status. The study has several implications for educational researchers, practitioners, and policy makers in terms of improving college students' learning behaviors and outcomes.

The influence of neighbourhood formality status and socio-economic position on self-rated health among adult men and women: a multilevel, cross sectional, population study from Aleppo, Syria

Background There is substantial evidence from high income countries that neighbourhoods have an influence on health independent of individual characteristics. However, neighbourhood characteristics are rarely taken into account in the analysis of urban health studies from developing countries. Informal urban neighbourhoods are home to about half of the population in Aleppo, the second largest city in Syria (population>2.5 million). This study aimed to examine the influence of neighbourhood socioeconomic status (SES) and formality status on self-rated health (SRH) of adult men and women residing in formal and informal urban neighbourhoods in Aleppo. Methods The study used data from 2038 survey respondents to the Aleppo Household Survey, 2004 (age 18–65 years, 54.8% women, response rate 86%). Respondents were nested in 45 neighbourhoods. Five individual-level SES measures, namely education, employment, car ownership, item ownership and household density, were aggregated to the level of neighbourhood. Multilevel regression models were used to investigate associations. Results We did not find evidence of important SRH variation between neighbourhoods. Neighbourhood average of household item ownership was associated with a greater likelihood of reporting excellent SRH in women; odds ratio (OR) for an increase of one item on average was 2.3 (95% CI 1.3-4.4 (versus poor SRH)) and 1.7 (95% CI 1.1-2.5 (versus normal SRH)), adjusted for individual characteristics and neighbourhood formality. After controlling for individual and neighbourhood SES measures, women living in informal neighbourhoods were less likely to report poor SRH than women living in formal neighbourhoods (OR= 0.4; 95% CI (0.2- 0.8) (versus poor SRH) and OR=0.5; 95%; CI (0.3-0.9) (versus normal SRH). Conclusions Findings support evidence from high income countries that certain characteristic of neighbourhoods affect men and women in different ways. Further research from similar urban settings in developing countries is needed to understand the mechanisms by which informal neighbourhoods influence women’s health.

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.