The Study of Para-Social Interaction with e-Word-of-Mouth for Influencer Marketing by Complex Computing

The purpose of this study was focused on exploring the relationship among the fans’ preferences, fans’ para-social interaction, and fans’ word-of-mouth. A survey consisted of 21 items based on the literature review and developed by this study. An online survey was distributed to the users of YouTube in Taiwan. A total of 606 valid samples was collected by survey. The instrument passed the reliability and validity test. Further, the data process applied the PLS (partial least squares) regression analysis methodology. The result shows that the ‘attractive’ impacted ‘para-social interaction’, ‘e-word-of-mouth’, and ‘preferences of fans’ positively. In addition, the para-social interaction plays an important role as a mediator between influencer’s attractiveness, w-word-of-mouth, and preferences of fans. Some suggestions were provided for social media influence’ related studies as reference.

Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

The impact of depression diagnosis on diabetes and lifetime hyperglycaemia

Aims The aim of this study was to evaluate longitudinal associations between the mean and variability of HbA1c levels in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD). Methods Individuals with T2D from the UK Biobank with linked primary care records were analysed. An HbA1c measurement within +/- 6-months of T2D diagnosis was taken as baseline, with subsequent HbA1c measurements used as the outcome in generalised least squares regression to evaluate longitudinal associations with a three-level MDD diagnosis variable (MDD controls, pre-T2D MDD cases and post-T2D MDD cases). Results Using 7,968 T2D individuals, we show that MDD has utility in explaining mean HbA1c levels (p=6.53E-08). This is attributable to MDD diagnosis interacting with baseline T2D medication (p=3.36E-04) and baseline HbA1c (p=2.66E-05), but not with time- when all else is equal, the temporal trend in expected HbA1c did not differ by MDD diagnosis. However, joint consideration with baseline T2D medication showed that each additional medication prescribed was associated with a +4 mmol/mol (2.5%) increase in expected HbA1c across follow up for post-T2D MDD cases, relative to pre-T2D MDD cases and MDD controls. Furthermore, variability in HbA1c increased across time for post-T2D MDD cases but decreased for MDD controls and pre-T2D MDD cases. Conclusions These findings suggest closer monitoring of individuals with both T2D and MDD is essential to improve their diabetic control, particularly for those who develop MDD after T2D diagnosis.

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2C and R2CV of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

An Analysis on the Impact of Choice Overload to Consumer Decision Paralysis

As consumers make purchase decisions, they often encounter a large number of options from which they base their choices. Traditional theories such as the Rational Choice theory imply that the more options involved, the more beneficial for the consumer. However, recent studies suggest otherwise. One such study is that of Choice Overload, a phenomenon in which individuals encounter difficulty when they are presented with too many options. Some studies show that Choice Overload causes paralysis in analysis in different industries. Decision Paralysis is the abandonment of making a decision due to overanalysis. The paper focused on proving if Decision Paralysis would take place when there is Choice Overload by analyzing whether the different antecedents of Choice Overload, namely Decision Task Difficulty, Choice Set Complexity, Preference Uncertainty, Decision Goal, and Asymmetric Information, would be affected by the number of options available. A survey was used to measure the different variables, and the data were analyzed through logistic regression and ordinary least squares regression. The results of this study indicate that Decision Task Difficulty and Asymmetric Information directly impact Choice Overload, which then contributes to the high probability of the occurrence of Decision Paralysis. It is difficult for consumers to choose when more options are offered; thus, abandoning their purchasing decision.

The role of novel instruments of brand communication and brand image in building consumers’ brand preference and intention to visit wineries

This research aims to analyze brand communication and brand image as specific drivers of wine brand preference and their influence on wine consumers’ intention to visit associated wineries. Specifically, this paper enhances the understanding of the roles of advertising-promotion, sponsorship-public relations, corporate social responsibility, and social media in brand communication, as well as functional, emotional and reputation components in brand image development in the context of wine tourism industry. Data was collected through a structured and self-administered questionnaire from 486 visitors to wineries in Spain. Partial least squares regression was used to evaluate the measurement model and the hypotheses. The empirical analysis shows that brand communication and brand image have similar positive effects on brand preference, and that brand image mediates the relationship between brand communication and brand preference. This research suggests implications for theory and practice relative to brand management in terms of communication and image; and it proposes insights into novel communication tools and marketing activities for the winery tourism industry. Firms should employ a holistic evaluation of brand communication to involve the whole organization, which would enhance the strategic role that brand communication plays.

Evolution Analysis of Free Fatty Acids and Aroma-Active Compounds during Tallow Oxidation

To explore the role of fatty acids as flavor precursors in the flavor of oxidized tallow, the volatile flavor compounds and free fatty acid (FFAs) in the four oxidization stages of tallow were analyzed via gas chromatography (GC)–mass spectrometry (MS), the aroma characteristics of them were analyzed by GC–olfactory (GC-O) method combined with sensory analysis and partial least-squares regression (PLSR) analysis. 12 common FFAs and 35 key aroma-active compounds were obtained. Combined with the results of odor activity value (OAV) and FD factor, benzaldehyde was found to be an important component in unoxidized tallow. (E,E)-2,4-Heptadienal, (E,E)-2,4-decadienal, (E)-2-nonenal, octanal, hexanoic acid, hexanal and (E)-2-heptenal were the key compounds involved in the tallow flavor oxidation. The changes in FFAs and volatile flavor compounds during oxidation and the metabolic evolution of key aroma-active compounds are systematically summarized in this study. The paper also provides considerable guidance in oxidation control and meat flavor product development.