Does corporate reputation matter? Role of social media in consumer intention to purchase innovative food product

The exponential growth of the corporate reputation in food industry has resulted in innovations in every link of its supply chain. There have been studies that have characterized innovation in various industries from the perspective of technology, but far fewer in the area of corporate reputation, consumer perception, and intention towards innovations in food products. This research analyses the innovations in the food industry from the perspective of the consumer and provides a conceptual framework of food innovation stages. The study also investigates the relationship between corporate reputation and intention towards food innovation along with the other components of TPB model with an extension of social media engagement. The results from India and US samples confirm that social media engagement have a significant role to play in creating intention to purchase innovative food products. The study compares the US and Indian samples and identifies differences in subjective norms and perceived behavioural control

Quality of partner relationship and emotional responses to a health threat

This study investigated whether existence of a cohabiting relationship and its quality was related to psychological distress in women facing an acute stressor - a health threat. Levels of social support and self-esteem were tested as predictors of distress. One hundred and fifty-eight women with symptomatic breast problems referred to a diagnostic one-stop breast clinic participated in the study. Levels of psychological distress (stress, anxiety, depression), social support, self-esteem and, for women with partners, quality of partner relationship were measured using standardized self-report instruments. No differences were found between women with and without partners in terms of distress and psychosocial variables. However, women in low quality relationships experienced significantly more distress and received less support than women in high quality relationships. Self-esteem was not related to partner relationships. Low personal self-esteem significantly predicted distress on the appointment day for all groups of women, accounting for between 19% and 54% of the variance. Social self-esteem and ideal social support were also found to be significant predictors of distress for women without partners and cohabiting women in low quality relationships. It would appear that women with self-rated poor quality spousal relationships are at risk of elevated psychological morbidity in the context of investigation for suspected breast disease

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient