Martin J. Liu

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands.

Value marketing through corporate reputation: An empirical investigation of Thai hospitals

Abstract This study examines the value proposition through corporate reputation, as corporate reputation best communicates to consumers and the public the companys value orientation. The research setting for the study was the Thai private hospital industry. A quantitative methodology was employed, using Confirmatory Factor Analysis (CFA) to purify our measurement items and then using regression analysis to test our hypothesis. A model of corporate reputation is proposed, based primarily on a combination of institutional theory, impression management theory, and signalling theory. The study contrasts with most previous studies on this subject, which employ a single approach in researching corporate reputation. Lastly, it explores the implications of corporate reputation with regards to value offering for practitioners and policy makers.

Growing on Steroids: Rapidly Scaling the User Base of Digital Ventures Through Digital Innovation

Digital ventures, start-ups growing by drawing on and adding to digital infrastructures, can scale their business at an unprecedented pace. We view such rapid scaling as a generative process by which a ventures user base increases significantly between two points in time through digital innovation. We studied WeCash, a Chinese digital venture, nearly doubling its user base monthly, to learn more about this generative process. We trace three contingent mechanisms underpinning rapid scaling: data-driven operation, instant release, and swift transformation. We explain these mechanisms and how they interact in the rapid scaling of digital ventures. The research offers an agency perspective on scaling of digital ventures that speaks to the digital innovation literature.

Understanding and predicting what influence online product sales? A neural network approach

Abstract Understanding the factors that influence sales is important for online sellers to manage their supply chains. This study aims to examine the roles of online reviews and reviewer characteristics in predicting product sales. With Amazon.com data captured using our big data architecture, this study performs sentiment analysis to measure the sentiment strength and polarity of review content. The predicting powers of sentiment together with other variables are then examined using neural network analysis. The results indicate that all the proposed variables are important predictors of online sales, and among them helpful votes of reviewer and picture of reviewer are the most influential ones. The findings of this study can be helpful for online sellers to manage their businesses, and the big data architecture and methodology can be generalised into other research contexts.

An empirical analysis of consumer motivation towards reverse exchange

Purpose – Despite the growing interest in reverse exchange, studies on the subject from the perspective of consumer participation and motivation remain sparse. Consumers’ participation in reverse exchange is a key component of supply-chain reverse logistics. To address the gap in existing studies, this paper aims to empirically identify the intention and causes of consumer electronic product exchange (EPE). The proposed research model incorporates value-belief-norm and neutralization theories, linking consumers’ values to their intentions to participate in EPE. Design/methodology/approach – Survey data collected from 250 consumers were analyzed using structural equation modeling. Findings – This discussion shows that people are more likely to present positive attitudes when they are ethically concerned. However, this tendency is not without exceptions and behavior influenced by ethics was not always observed. Upon examination, the findings highlight moderating forces of psychological tension that arise wh...

Explicating industrial brand equity: Integrating brand trust, brand performance and industrial brand image

Purpose – The purpose of this paper is to explore brand equity from multiple perspectives (tangible and intangible) and their joint consequences, namely, on industrial buyers’ brand loyalty and their long-term commitment. The aim is to provide a more comprehensive framework of the buyer’s behavioral response in the business-to-business context by integrating both trust elements and industrial brand attributes (brand performance and industrial brand image). In addition, the study explores the mediation effects of trust and brand attributes on industrial buyers’ responses such as loyalty and long-term commitment. Design/methodology/approach – Using a survey approach, the study includes respondents working in the heating, ventilating and air-conditioning (HVAC) industry in Malaysia, and data are collected in the industrial air-conditioning segment. The research model was tested with SEM. Findings – Findings show that brand performance and industrial brand image directly affect brand trust but with different ...

Fellow-townsmenship as the mechanism for exploring and exploiting business opportunities: A longitudinal reflection of the nineteenth century Ningbo entrepreneurs in Shanghai

This research examines how fellow-townsmenship, a distinctive homophilous social network, functioned among Ningbo entrepreneurs pertaining to their simultaneous exploration and exploitation of business opportunities, or achieving ambidexterity, in the nineteenth century. By investigating data in relevant historical records from museums, archives and libraries, case studies based on two representative Ningbo entrepreneurs from a distinctive business family showcase how those entrepreneurs took advantage of townsmenship to resolve the trade-off between exploration and exploitation. In doing so, simultaneous exploration and exploitation alongside the expansion of fellow townsmenship proved to be effective and successful. This research provides new grounds to examine ambidexterity.