Adaptation Learning: An Ambidextrous Perspective

The dissertation examines whether adaptation, through ambidexterity, helps firms improve their performance. Adaptation ambidexterity is an intrafirm process of balancing and integrating exploration and exploitation learning in a firm's partner-specific investment strategy to develop products according to that partner's changing requirements. Specific research questions are whether: (1) adaptation ambidexterity improves new product performance, and (2) whether the marketing environment characteristics of adaptation (market turbulence, technological turbulence, and partner dependence) affect that relationship. To address these concerns, the dissertation develops scales for adaptation ambidexterity, adaptation balance and adaptation integration. Then, moderated regression is used for main effects and moderation effects. The study employs a cross-sectional design and examines the hypothetical relationships. Key participants to be surveyed were determined using a random list of US high-tech manufacturing firms. The results show that adaptation ambidexterity is an important factor that influences new product performance. First, adaptation integration, one of the two components of being ambidextrous, has strong and consistent effects on new product performance. Second, under low market turbulence, low technological turbulence, and low partner dependence adaptation balance may in fact negatively affect performance. Under the high levels of these factors, adaptation balance is non-detrimental condition for ambidexterity. This finding confirms the fact that being balanced without justification may harm business performance. Finally, the negative interaction effect of adaptation integration and technological turbulent environment in this study in fact suggest that adaptation integration is more effective in low technological turbulence that in high technological turbulence.Department of Marketin

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

Impact of transport infrastructure on firm performance: case study of Cuu long delta area, Vietnam

Transport infrastructure plays an important role in promoting the socio-economic development in most countries. In particular, the development of road infrastructure is the basis for promoting enterprises development through expanding market access, lowering logistics cost and inputs cost, etc. The focal point of this paper is to estimate the impact of road infrastructure on firm performance through an empirical research in Cuu Long delta area, Vietnam. By applying the econometric models, the results from this study show that the positive relationship between road infrastructure and firm performance in Cuu Long delta area, and that the level of impact is different across business sectors

IDENTIFICATION OF A BACTERIOCIN PRODUCING BY LACTOCOCCUS LACTIS SUBSP. LACTIS PD14

The strain Lactococcus PD14 isolated from cow’s fresh milk was identified as Lactococcus lactis subsp. lactis. The bacteriocin produced by PD14  was isolated and purified by absorption-desorption method described and then this bacteriocin was purified by solid-phase extraction-SPE and HPLC method with column C18. The result of tricine- SDS-PAGE indicated the molecular weight of bacteriocin was about 3.5 kDa, the same as nisin. The prenisin encoding gene was cloned and sequenced successfully for PD14. The nis gene of the strain PD14 was 99 % homology with nisA and nisZ while 98 % with nisF, 97 % with nisQ. The deduced amino acid sequence of prenisin was determined, which had minor difference from published sequence of nisA in one amino acid. At the position aminoacid -18, prenisin of PD14 had valine while  the known prenisin A had phenylalanine. The matured nisin of PD14 was completely similar to the known amino acid sequence of nisA. Thus, the strain Lactococcus lactis subsp. lactis. PD14 produced a bacteriocin which was identified as nisin A

A comparative impact evaluation of two human resource models for community-based active tuberculosis case finding in Ho Chi Minh City, Viet Nam

Background: To achieve the WHO End TB Strategy targets, it is necessary to detect and treat more people with active TB early. Scale–up of active case finding (ACF) may be one strategy to achieve that goal. Given human resource constraints in the health systems of most high TB burden countries, volunteer community health workers (CHW) have been widely used to economically scale up TB ACF. However, more evidence is needed on the most cost-effective compensation models for these CHWs and their potential impact on case finding to inform optimal scale-up policies. Methods: We conducted a two-year, controlled intervention study in 12 districts of Ho Chi Minh City, Viet Nam. We engaged CHWs as salaried employees (3 districts) or incentivized volunteers (3 districts) to conduct ACF among contacts of people with TB and urban priority groups. Eligible persons were asked to attend health services for radiographic screening and rapid molecular diagnosis or smear microscopy. Individuals diagnosed with TB were linked to appropriate care. Six districts providing routine NTP care served as control area. We evaluated additional cases notified and conducted comparative interrupted time series (ITS) analyses to assess the impact of ACF by human resource model on TB case notifications. Results: We verbally screened 321,020 persons in the community, of whom 70,439 were eligible for testing and 1138 of them started TB treatment. ACF activities resulted in a + 15.9% [95% CI: + 15.0%, + 16.7%] rise in All Forms TB notifications in the intervention areas compared to control areas. The ITS analyses detected significant positive post-intervention trend differences in All Forms TB notification rates between the intervention and control areas (p = 0.001), as well as between the employee and volunteer human resource models (p = 0.021). Conclusions: Both salaried and volunteer CHW human resource models demonstrated additionality in case notifications compared to routine case finding by the government TB program. The salaried employee CHW model achieved a greater impact on notifications and should be prioritized for scale-up, given sufficient resources