Emotion-aware conversational systems are increasingly adopted in healthcare, yet most emotion detection approaches rely on sentiment polarity, which fails to capture emotional nuance and intensity in short, informal chat messages. To overcome these limitations, this study develops an interpretable emotion-aware framework called Word Intensity Scoring Engine (WISE) that explicitly models emotional intensity in short-form conversational text. Grounded in Plutchik’s Wheel of Emotions, WISE integrates lexicon-based emotion labeling, synonym expansion, semantic similarity using word embeddings, and negation-aware feature extraction to calculate intensity-weighted emotion scores at the word level. The WISE framework is evaluated through experimental benchmarking and an expert review involving practicing healthcare professionals. Results show that WISE enhances the performance of ML and LLM-based classifiers. Further, expert evaluations indicate that WISE-derived responses are perceived as professional, contextually appropriate, and empathetic. This study contributes a transparent, theoretically grounded design artifact that advances affective computing research and informs the deployment of emotion-aware conversational agents in healthcare.
Firms want to retain high-performing salespeople, but prior research has yielded conflicting findings regarding the relationship between sales performance and turnover. Furthermore, prior research has yet to inform managers on how to reduce dysfunctional turnover among high performers when managing modern hybrid sales forces that include inside and outside salespeople. Drawing from the content model of turnover, the authors conduct a longitudinal study of salesperson turnover in a Fortune 100 company. Baseline results show that sales performance has a U-shaped relationship with turnover, such that turnover is high for both low and high performers. However, this relationship is stronger among outside salespeople than inside. The authors identify two key factors, pay equity (instrumental driver) and workplace braggability (symbolic driver), that help explain the heterogeneity of turnover likelihood between inside versus outside sales roles. These factors sometimes result in an inverted U-shaped performance–turnover relationship, such that turnover is highest for midrange performers. The findings are robust when controlling for traditional predictors of turnover, including job satisfaction, peer turnover, and external employer poaching. These results inform managers of effective strategies to manage turnover in hybrid sales forces.
Africa's growing role in global supply chains presents an important opportunity for more socially grounded and context-sensitive research in supply chain management (SCM). Despite its economic and demographic significance, African contexts remain underrepresented in mainstream SCM scholarship, which limits understanding of the continent's diversity, complexity, and social dimensions. This article examines governance in African supply chains through three persistent societal challenges: the erosion of local agency, labor exploitation, and marginalization. While dominant frameworks such as transaction cost economics, agency theory, and the relational view offer useful insights into coordination and control, they do not fully capture the relational, ethical, and social dimensions of supply chains in Africa. To address this gap, the article draws on Indigenous African philosophies: Ujamaa, Ubuntu, and Omol & uacute;& agrave;b & iacute; as analytical lenses. The study contributes to the literature through perspectival theorizing, by reinterpreting established SCM phenomena through African philosophical perspectives, and through propositional theorizing, by advancing theoretically informed and contextually grounded propositions and research questions. The article demonstrates how African insights can inform and extend existing SCM theories by promoting more socially impactful and ethically informed governance, and by positioning the supply chain as a potential source of conceptual innovation for globally relevant and socially meaningful theory.
The reactions of internal pyridyl/trialkylsilyl alkynes with the potent hydroboration reagent bis(1-methyl-ortho-carboranyl)borane (HBMeoCb2) results in unanticipated Si-Calkyl cleavage following a regioselective 1,1-hydroboration under mild conditions. The rare Si-Me bond rupture of the trimethylsilyl group occurs when using an intramolecular frustrated Lewis pair system forming a silylium heterocycle with a pendant borate bearing the methyl group. Investigating a methyl/ethyl mixed silyl substituent, SiEtMe2, revealed that the reaction is completely selective in breaking the Si-Me bond over the Si-Et bond. Exposure of the silylium/borate zwitterion to methanol resulted in O-H bond cleavage and ring opening to a zwitterionic pyridinium/borate. These findings provide insight into the challenging selective functionalization of Si-Calkyl bonds.
With the exponential growth in the number of reviews for products and services, it is important to identify reviews that are helpful in making purchase decisions. Prior studies have identified several factors influencing helpfulness of a review. However, the impact that negativity and positivity of review comments have on review helpfulness especially of extreme (1-star and 5-star) reviews has not received much attention. This study focuses on assessing the impact of negativity and positivity of extreme review ratings on review helpfulness by building a zero-One Inflated Beta regression model using a data set from Amazon having product reviews from several categories. We find that negativity (positivity) of review text in an extremely positive (negative) review influences helpfulness of the review positively. The study also finds support for the negativity in review text of an extremely positive review to be more helpful compared to positivity in an extremely negative review.
