The ability to understand the causes of customers' complaints is critical for hotels to improve their service quality, customer satisfaction, and revenue. This study adopts a novel structural topic ...model text analysis method to analyze 27,864 hotel reviews in New York City, and show that it leads to improved inference on consumer dissatisfaction. Our results reveal 10 topics, whose appearances in the negative reviews are substantially higher than those in the positive reviews. In addition, we investigate how customer complaints vary across different hotel grades. Results indicate that customer complaints for high-end hotels are mainly related to service issues, whereas customers of low-end hotels are frequently annoyed by facility-related problems. This research contributes to the hospitality literature by enhancing our understanding of the aspects of hotel customers’ dissatisfaction through rigorous statistical analysis, their correlations, and importance for different hotel grades.
•Using Structural Topic Model to identify 10 negative topics from hotel reviews.•Negative (positive) topics are correlated one another.•Complaints for high-end hotels are mainly related to service and pricing issues.•Customers of low-end hotels are frequently annoyed by facility-related problems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We predict financial distress for 107 listed Chinese companies.•Neural networks provide highest accuracy and are robust to experimental conditions.•Feature selection shows financial indicators ...related to profitability are important.
The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree ...of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A better understanding of the linkage between the objectives and outcomes of innovation will enable firms to plan better for effective utilization of their resources for generation of innovation. The ...current study enhances the understanding of relationships between the intent to innovate and eventual outcome as the intent to innovate drives complex processes and resource allocation that leads to a successful innovation. We analyze the World Bank innovation survey data of IT firms in China and find that drivers of innovation significantly impact the configuration elements of the innovation phenomenon within firms and influence how firms execute collaboration with external partners. This is turn impacts the product and process innovation generated by the firm. The findings imply that firms should decide about their choice of partners for innovation and the mode of engagement with the partners based on the drivers for innovation.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Online reviews are a critical component of the retail business ecosystem today. They help consumers share feedback and readers make informed choices. As such, it is important to understand the ...mechanism driving the creation of reviews and identify factors which make them useful for readers. Extant work in this field has largely ignored the distribution of thematic content in reviews and its role in review diagnosticity. This article attempts to bridge the gap. A novel approach is proposed to explore the distribution of thematic content in reviews, in terms of underlying topics, and test its impact on influence of reviews. The approach is illustrated through a case study using data from Yelp. Implications of the study for theory and practice are discussed.
Changing digital technologies and innovation threaten established business models. Increasing uncertainty in the operating environment often drives the need to transform the core business model of ...firms through a process of digital business transformation (DBT). In this study, we conduct a longitudinal study of two digital startups in the crowdfunding domain and identify the core attributes driving such transformation in digital ventures. We build a framework that examines how DBT takes place in entrepreneurial firms through information exchange with the environment. This research will help entrepreneurs and managers of such firms design and develop reactive business models for market success.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We analyze charity requests registered on the Random Acts of Pizza online community and examine the content of postings and non-content characteristics to identify features that are associated with ...the success of donation. We find that the presence of rational and credible appeals in a message increases the likelihood of receiving a donation, whereas the mere presence of negative emotional appeal does not do so. Our research is useful for those who like to make persuasive charity requests on online platforms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Currently many organizations are in the process of implementing Big Data related projects in order to extract meaningful insights from their data for better decision making. Though there are various ...frameworks postulating the best practices which should be adopted while implementing analytics projects, they do not cater to the complexities associated with a Big Data project. In this paper our goal is two-fold: to develop a new framework that can provide organizations a holistic roadmap in conceptualizing, planning and successfully implementing Big Data projects and to validate this framework through our observation of a descriptive case study of an organization that has implemented such a project. Although the manufacturing sector has been slow in incorporating analytics in their strategic decision making, the situation is changing with increasing use of analytics for product development, operations and logistics. We explore the Big Data project at a manufacturing company, Ramco Cements Limited, India, describe the system developed by them and highlight the benefits accrued from it. We investigate the entire process by which the project is implemented using the lens of our proposed framework. Our results reveal that a clear understanding of the business problem, a detailed and well planned step-by-step project map, a cross functional project team, adoption of innovative visualization techniques, patronage and active involvement of top management and a culture of data driven decision making are essential for the success of a Big Data project.
•A new framework for implementing Big Data analytics projects is proposed.•A visualization based real-time analytics project for Ramco Cements is described.•The case study leads to development of key lessons for Big Data project management.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Social networking site (SNS) driven e-commerce, the latest social commerce (s-commerce) phenomenon, gains prominence with the introduction of the call-to-action feature. The call-to-action feature on ...any sponsored post or advertisement on SNS redirects the user to a linked e-commerce website that offers the product. Information cues available on the SNS are expected to influence user decision making on the linked e-commerce site. Set in the context of Facebook driven e-commerce, this study explores how likes on Facebook influence user's purchase and recommendation decisions on a linked e-commerce website. Using controlled experiments we find that a higher volume of likes on Facebook leads to a higher likelihood of purchasing and recommending a product on the linked e-commerce site. This effect is found to be mediated by the user's initial product attitude formed on Facebook. An additional analysis examining the strength of the influence reveals that the mere presence of likes is not sufficient to impact the user's decision making. In fact, a low volume of likes elicits user behavior similar to absence of likes. The influence is effective only if the number of likes is substantially high. The findings of the study add to the s-commerce literature by establishing the inter-site influence of Facebook likes on user's purchase and post-purchase decisions and providing empirical evidence of the efficacy of SNS-driven e-commerce.
•Facebook likes influence customers' purchases and recommendations on a linked e-commerce site.•Facebook-driven social commerce benefits from a high volume of likes.•Likes act as social proof, and attitude transfer hypothesis explains their inter-site impact.•A controlled laboratory experiment confirms the observed effect.•The volume of Facebook likes needs to be high to impact social commerce.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Pioneering digital firms are often built on novel business models that differentiate them from their competition. Striving for the right business model is a challenge for entrepreneurs. In their ...digital endeavors, firms often experiment with multiple business models before converging on to a specific, focused model that they choose to pursue. An intriguing question in this domain is how do digital market pioneers gain strategic knowledge for the transformation of their business models? We develop an integrative framework that identifies strategic learning and its impact on the digital model adopted by the firm, as a core driver for business model transformation. We examine this framework through a case study of Wishberry, a crowdfunding startup in India that was the pioneer in this domain in India. Its active scanning of the business environment led to strategic learnings that helped transform its business model which was at the core of its sustained market advantages. The insights for this study, which are primarily focused on digital entrepreneurship, extend the theoretical frameworks of strategic learning to the context of market pioneers. The learnings from this study will help entrepreneurs design agile business models that are reactive to market needs.
•Pioneering digital ventures often experiment with multiple business models before converging on to a specific model.•They seek new opportunities while continuously auditing its intrinsic digital capabilities from its digital infrastructures.•Strategic learning from the business environment helps them transform its business model.•Digital infrastructures curtail the costs and risks associated with business model transformation.•Successful transformation of their core business model leads to market dominance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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