The worldwide increased consumption of goods and services squeezes natural resources, thus causing severe damage to the environment. In the backdrop of the growing affirmative reaction of people, the ...inclination to buy green products is on the rise. Therefore, we explore the extant literature to identify the factors established for their role in determining the purchase of products having a less damaging impact on the environment. A systematic literature review was conducted following the Theory‐Context‐Characteristics‐Methodology framework covering 151 empirical studies on green purchase intention and green purchase behavior, published between years 2000 and 2021. This paper identifies factors influencing consumers' green purchase intention and green purchase behavior and provides strategic insights to marketers to create better marketing opportunities for green products.
Trust plays an important role in sharing transactions on short-term rental platforms. However, the impact of host self-description on trust perception and whether trust perception can influence ...purchase behavior remain under-studied. Therefore, a text analytics framework was proposed to research the relationships among host self-description, trust perception and purchase behavior on Airbnb. Specifically, a deep-learning-based method was designed to automatically code trust perception of host self-descriptions. And the linguistic and semantic features of description texts were extracted with text mining methods. The estimated order quantity was used to quantify purchase behavior. Then, the influence of linguistic and semantic features on trust perception was identified, and the relationship between trust perception and purchase behavior was also verified. The empirical analysis derives the following findings: i. The readability of self-description is positively associated with trust perception; ii. Perspective taking expressed in self-description is also helpful; iii. Excessive positive sentiment expression can raise barriers to trust building; iv. Paying more attention to family relationship, openness, service and travel experience in self-description would be helpful; v. Trust perception can promote purchases. These findings can help hosts write better self-description, which contributes to trust building and purchases on short-term rental platforms.
•A deep-learning procedure is proposed to predict guests' trust perception.•The readability and perspective taking in self-description can help build trust.•Excessive positive sentiment expression would decrease trust perception.•More introduction on family, openness, service and travel in self-description would be helpful.•The perceived trust derived from self-description does contribute to purchases.
Recommender systems appear all across the internet. For e-retailers, this represents an opportunity to get more and niche products before customers’ eyes. However, we find that while implementing ...recommender systems does increase overall sales figures, it does not generally improve the relative sales for niche items, leading to a rich-get-richer situation. We find, across a wide range of product categories, that the use of traditional collaborative filters (CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. The decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase as aggregate sales diversity decreases. CFs help individuals explore new products, but similar users still end up exploring the same kinds of products, resulting in concentration bias at the aggregate level. There is one insight for management: Traditional collaborative filters carry the unintended consequence of increasing concentration bias. A firm interested in exposing consumers to a broader assortment of products may prefer a different design from another simply interested in maximizing sales.
We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon’s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on the website of a top retailer in North America across 82,290 products and 1,138,238 users. We report four main findings. First, we demonstrate and quantify across a wide range of product categories that the use of traditional collaborative filters (CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. Furthermore, the design of the CF matters. CFs based on purchase data are associated with a greater effect size than those based on product views. Second, the decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase while aggregate sales diversity decreases. Third, copurchase network analyses show that while recommenders can help individuals explore new products, similar users still end up exploring the same kinds of products, resulting in concentration bias at the aggregate level. Fourth and finally, there is a difference between absolute and relative impact on niche items. Specifically, absolute sales and views for niche items in fact increase, but their gains are smaller compared with the gains in views and sales for popular items. Thus, whereas niche items gain in absolute terms, they lose out in terms of market share. We discuss economic impacts and managerial implications.
The online appendices are available at
https://doi.org/10.1287/isre.2018.0800
.
This study investigated the tourists' novel intentions to save environment and what parameters impact their pro-environment decisions. Structural Equation Modeling was used to test the integrated ...study model to get correct and unbiased path coefficients with the help of 227 responses. The study results revealed that greater the environmental knowledge of environmental friendly products, the more positive attitude towards environmental friendly products was perceived and international tourists perceived it significantly high in comparison to domestic tourists Interestingly Control on Availability didn't impact purchase intentions of tourists in the current study, a finding different from literature across other contexts. Attitude didn't have significant mediating effect in the relationship between environmental knowledge and purchase intentions. On the other hand, purchase intentions positively influenced purchase behavior of tourists. Practical implications are discussed at large.
•Tested integrated model to get correct and unbiased paths with the help of sound methodology•Interestingly control on Availability didn't impact purchase intentions of tourists•Attitude was not found having significant mediating effect.•Study found Gender and TouristType as significant moderators.
With the explosive growth and fragmented dissemination of information, information encountering, a form of passive information acquisition, has become a dominant way for consumers to obtain ...consumption information online. This means of acquiring information may have implications for consumer behavior. The current research explored the influence of information encountering on purchase behavior. Three online experiments were conducted to test the interaction between information relevance (interest-related vs. problem-related) and consumers' goal state (no-goal vs. other goal) in relation to purchase behavior, and tested whether this link was mediated by customer inspiration. Results showed that information relevance and goal state interacted to predict purchase behavior. Specifically, purchase behavior was highest when participants encountered problem-related information in the no-goal state and when participants encountered interest-related information in the other-goal state. Moreover, customer inspiration mediated these effects. Our work expands the literature on information encountering and customer inspiration, and has implications for enterprises’ information dissemination strategies.
The question of whether consumer purchasing decisions are conscious choices or unconscious has long been studied in marketing. The ability to measure mental changes with high temporal resolution ...makes the EEG-based event-related potentials (ERP) method very useful in studying the distinction between consciousness and unconsciousness. Although experiences with brands significantly affect the awareness or unconsciousness of decisions to purchase, ERP studies have ignored experiences of consumers in relation to brand purchases. For this purpose, EEG recordings of participants were taken in the order they saw brand names: experienced brands, review-based brands, and unknown brands. Participants chose one of the three options for the brands they saw on the screen: buying, not buying, and no idea. 35 people participated in the study. The results indicate that early ERPs, which are unconscious mental reactions, related to purchase decisions for previously unknown brands. Late ERPs associated with conscious mental reactions are related to purchasing review-based brands or experienced brands. We conclude that purchasing decisions about unknown brands occur as a result of automatic, unconscious mental processes, whereas purchasing decisions about previously experienced brands and based on consumer reviews result from conscious mental processes. Our study is the first that demonstrates the relationship between ERP's and purchasing decisions, with an experimental design focused on consumer experience and consciousness.
Drawing on uses and gratification theory, this article elucidates how consumers capitalize on online reviews for purchase decisions in three markets (Taiwan, Thailand, and Vietnam). Three studies ...demonstrate this objective. Study 1 considers online reviews as a one-dimensional construct consisting of three behavioral components, including textual comments, contextual images, and star ratings, that had been validated by using reliability and validity analyses. Study 2 employs an ANOVA to analyze how these components relate to different consumer groups in terms of gender, age, and location. Study 3 explores the mediating role of online trust in the online reviews and purchase decision relationship under the moderating effect of the perceived effectiveness of social media platforms. Using a cross-national survey and a two-stage partial least squares analysis, the authors substantiate the proposed relationships and discuss the theoretical and managerial contributions of this research.
•A valid measure of online reviews consists of textual comments, contextual images, and star ratings.•Three studies investigate how online reviews and online trust engage in affecting purchase decision.•The IPMA and MGA results detail the mediating and moderating effects for improving managerial effectiveness.•At high levels of PESMP, online reviews affect online trust in brand more strongly.
A thorough understanding of online customer's purchase behavior will directly boost e-commerce business performance. Existing studies have overtly focused on purchase intention and used sales rank as ...a natural proxy, which however has limited business application. Additionally, intention to purchase does not necessarily convert to actual retail purchases. We aim to further our understanding of online customer's purchase behavior for an e-commerce platform by predicting the same using deep learning techniques, on a large multidimensional data sample of more than 50,000 unique web sessions. This study used two distinct sets of variables, i.e., platform engagement and customer characteristics, as key predictors of online purchases by retail customers. We further compared the predictive capability of our deep learning method with other widely used machine learning techniques for prediction, including Decision Tree, Random Forest, Support Vector Machines, and Artificial Neural Networks. We found that the deep learning technique outperformed the machine learning techniques when applied to the same dataset. These analyses will help platform designers plan for more platform engagements while simultaneously expanding the academic understanding of purchase prediction for online e-commerce platforms.
•We analyze purchase data of customers from an e-commerce site.•We use deep neural networks to predict purchases by customers.•Deep neural networks predict more accurately than other machine learning techniques.•We identify the platform and customer characteristics that act as significant predictors.
▪Neha Chaudhuri is Assistant Professor in Information Management at Toulouse Business School, France. Her PhD dissertation from Indian Institute of Management Calcutta explores multi-modal factors responsible for online misinformation. She has completed her Bachelor of Technology in Computer Science and Engineering and Masters of Technology in Information Technology from Indian Institute of Engineering Science and Technology, Shibpur. Her research interests are in deep learning, AI for business, and online misinformation management. Her research has been published in journals like Decision Support Systems along with proceedings of the IEEE as well as those in conferences like HICSSWeB (pre-ICIS).
▪Gaurav Gupta is Assistant Professor in the Strategy and Entrepreneurship department at NEOMA Business School, France. He holds a PhD from Indian Institute of Management Calcutta, India on the role of operating environment on the business model transformation of technology startups using a multi-country sample of case studies. His research interests include technology entrepreneurship, strategic information systems, and business model transformation. His work has been featured in journals like Information & Management, Technological Forecasting & Social Change, Annals of Operations Research, Australasian Journal of Information Systems and in the proceedings of conferences like ICIS, ECIS, HICSS, etc. He has won various awards for case writing including the prestigious EFMD Case Writing Award 2017 and the CEEMAN-Emerald Case Writing Award.
▪Vamsi Vallurupalli holds a PhD. in Management Information Systems from Indian Institute of Management Calcutta, India. In the past, he has worked on data warehousing, business intelligence and business analytics projects of Fortune 500 firms. His research interests are in big data and business analytics, e-commerce and social media. His research has been featured in journals like Decision Support Systems, Electronic Markets and Communications of the Association for Information Systems and proceedings of conferences like Australasian Conference on Information Systems and Workshop on e-Business.
▪Indranil Bose is Professor of Management Information Systems at the Indian Institute of Management, Calcutta. He acts as Coordinator of IIMC Case Research Centre. He holds a BTech from the Indian Institute of Technology, MS from the University of Iowa, and MS and PhD from Purdue University. His research interests are in business analytics, digital transformation, information security, and management of innovation. His publications have appeared in MIS Quarterly, Journal of the MIS, Communications of the ACM, Communications of the AIS, Computers and Operations Research, Decision Support Systems, Electronic Markets, Ergonomics, European Journal of Operational Research, Information & Management, International Journal of Production Economics, Journal of Organizational Computing and Electronic Commerce, Journal of the American Society for Information Science and Technology, Operations Research Letters, Technological Forecasting and Social Change, etc. He serves as Senior Editor of Decision Support Systems and Pacific Asia Journal of the AIS, and as Associate Editor of Communications of the AIS, Information & Management, and Journal of the AIS.
Livestreaming e-commerce has emerged as a highly profitable e-commerce that has revolutionized the retail industry, especially during the COVID-19 pandemic. However, research on livestreaming ...e-commerce is still in its infancy. This study sheds new light on impulsive purchase behavior in livestreaming e-commerce. Based on stimulus-organism-response (SOR) theory, this study introduces the “People-Product-Place” marketing strategy for livestreaming e-commerce from the perspective of consumer perception and aims to understand the impact of marketing strategy on impulsive purchase behavior in e-commerce livestreaming shopping scenes, and to examine the mediating effect of involvement. The study conducted SEM analysis, in Amos, on 437 response sets from an online anonymous survey. The results show that
perceived
e-
commerce anchor attributes
,
perceived scarcity
, and
immersion
positively influence impulsive purchase behavior; that “People-Product-Place” marketing strategy is important; and that effective marketing triggers impulsive purchase.
Perceived e-commerce anchor attributes, perceived scarcity
, and
immersion
positively influence
involvement
, which positively influences impulsive purchase.
Involvement
mediates between
perceived
e-
commerce anchor attributes
,
perceived scarcity
and
immersion
, and impulsive purchase. These findings guide marketers to improve the profitability of livestreaming e-commerce and provide some references of economic recovery for many other countries that also suffered from the impact of the COVID-19 pandemic.
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