Beyond Service Attributes: Do Personal Values Matter?

Purpose – Service firms constantly look for ways to differentiate their offering. Recently, personal values have emerged as a way to understand how customers fulfill deeper needs when consuming a service. This paper aims to examine how personal values operate in the evaluation of higher education services. Like other services, marketing has become essential to higher education as universities compete aggressively for students and differentiate their service offerings. Although attribute-based measures such as SERVQUAL provide useful information to service providers, personal values may offer a deeper understanding of how customers judge the quality and desirability of an educational institution’s services. This study seeks to determine whether personal values in higher education affect perceptions of overall value, satisfaction, and behavioral outcomes including loyalty and intention to recommend.Design/methodology/approach – A survey measured student personal values, service quality, satisfaction, and behavioral outcomes in the USA – the largest exporter of higher educational service, and India – the largest net importer. Data were analyzed using confirmatory factor analysis, path analysis, and t-tests.Findings – The results describe the impact of personal values on satisfaction and behavioral outcomes, while showing differences between India and the USA.Research limitations/implications – The paper provides implications for applying the personal values concept to the marketing of a university. It also serves as a basis for future research on the impact of personal values in other service sectors.Originality/value – The study fills an important gap in the literature by showing that personal values are an important dimension in services. Service firms need to move beyond attributes and measure personal values, as these values do impact customer satisfaction and loyalty

Efficient Scalable Accurate Regression Queries in In-DBMS Analytics

Recent trends aim to incorporate advanced data analytics capabilities within DBMSs. Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute a novel predictive analytics model and associated regression query processing algorithms, which are efficient, scalable and accurate. We focus on predicting the answers to two key query types that reveal dependencies between the values of different attributes: (i) mean-value queries and (ii) multivariate linear regression queries, both within specific data subspaces defined based on the values of other attributes. Our algorithms achieve many orders of magnitude improvement in query processing efficiency and nearperfect approximations of the underlying relationships among data attributes

Fader Networks: Manipulating Images by Sliding Attributes

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space. As a result, after training, our model can generate different realistic versions of an input image by varying the attribute values. By using continuous attribute values, we can choose how much a specific attribute is perceivable in the generated image. This property could allow for applications where users can modify an image using sliding knobs, like faders on a mixing console, to change the facial expression of a portrait, or to update the color of some objects. Compared to the state-of-the-art which mostly relies on training adversarial networks in pixel space by altering attribute values at train time, our approach results in much simpler training schemes and nicely scales to multiple attributes. We present evidence that our model can significantly change the perceived value of the attributes while preserving the naturalness of images.Comment: NIPS 201