scholarly journals Big Data Analytics Implications for Smart Tourism Destinations Towards the Enrichment of Content Tourism

2019 ◽  
Vol 8 (S3) ◽  
pp. 7-11
Author(s):  
N. Padmaja ◽  
T. Sudha
Keyword(s):  
Big Data ◽  
Business Models ◽  
Big Data Analytics ◽  
Tourism Destination ◽  
Case Study Analysis ◽  
Research Perspectives ◽  
New Business ◽  
Promising Area ◽  
Multiple Case ◽  
Smart Tourism

Smart tourism has huge amount of Social Big data available from tourists can cherish the value conception process for a Smart Tourism Destination. Applying a multiple-case study analysis, a set of regional tourist experiences related and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Near conclusions and data in terms of improving decision making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models, exploitation of Big Data in the context of information-intensive industries and mainly in Tourism. Smart Tourism Destination today is the front line of study in the tourism field and is a promising area from various research perspectives in terms of models, tools and strategies to keep up the process of intelligent configuration of destinations.

The e-ERP Transformation Matrix

2005 ◽  
pp. 158-178
Author(s):  
Colin G. Ash ◽  
Janice M. Burn
Keyword(s):  
Stages Of Change ◽  
Business Models ◽  
Relationship Building ◽  
Case Study Analysis ◽  
Business Transformation ◽  
The Matrix ◽  
New Business ◽  
Value Propositions ◽  
Multiple Case ◽  
Self Service

A model of e-business transformation is developed for ERP enabled organisations, based on the findings of a longitudinal multiple case study analysis of SAP sites. The model is represented as a matrix along three stages of e-business growth. The theory embedded within the matrix recommends that successful e-business transformation with ERP systems occurs when B2B value propositions are realized through integration and differentiation of technologies, used to support new business models for delivering products and services online. In addition, the management focus evolves through employee self-service and empowerment towards extensive relationship building with e-alliances. The matrix can be used by ERP business managers to guide their strategies for organisational transformation but also highlights critical stages of change.

Understanding Data Analytics Is Good but Knowing How to Use It Is Better!

2019 ◽  
pp. 125-160
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Big Data Analytics ◽  
Success Factor ◽  
New Business ◽  
Technical Architecture ◽  
The Veil ◽  
Key Success Factor ◽  
New Business Models

Collecting the data and being able to generate value from it: this is certainly the key success factor of tomorrow's champions, one that will allow you to innovate and create new business models. Faced with the 3Vs of big data, many companies are embarking on big data projects with the main objective: generating value. The goal is to succeed, by the detailed analysis of large amounts of data, to lift the veil and discover hitherto hidden models and barely perceptible correlations, as many new business opportunities that companies must grasp. The key to the success of any big data analytics initiative is to define your goals, identify specific business questions that a suitable technical architecture will need to answer, and use the data experts to generate value from data by using specific algorithms.

Datenbasierte Produktionsoptimierung*/Data-driven production optimization: Areas of development for manufacturing companies

2018 ◽  
Vol 108 (03) ◽  
pp. 108-112
Author(s):  
D. Bauer ◽  
T. Maurer ◽  
T. Bauernhansl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Applied Research ◽  
Business Models ◽  
Big Data Analytics ◽  
Production Optimization ◽  
Data Driven ◽  
Manufacturing Companies ◽  
New Business ◽  
New Business Models

Unternehmen sehen in Big-Data-Analysen ein großes Potenzial zur Optimierung der klassischen Produktionsziele sowie zur Entwicklung neuer Geschäftsmodelle. Eine Studie des Fraunhofer IPA analysiert, welche Herausforderungen bei der Umsetzung dieser Potenziale auftreten. Darauf aufbauend werden Entwicklungsfelder für die angewandte Forschung und produzierende Unternehmen erarbeitet.   Companies expect huge benefits from big data analytics both to improve traditional production targets and to develop new business models. A study conducted by Fraunhofer IPA analyzes the upcoming challenges in exploiting these opportunities. It provides the basis for identifying areas of development for applied research and for manufacturing companies.

Big-data business models: A critical literature review and multiperspective research framework

2020 ◽  
Vol 35 (1) ◽  
pp. 66-91 ◽  
Author(s):  
Martin Wiener ◽  
Carol Saunders ◽  
Marco Marabelli
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Models ◽  
Theory Development ◽  
Big Data Analytics ◽  
Future Research ◽  
Central Research ◽  
Research Perspectives ◽  
Model Research ◽  
Critical Literature

The emergence of “big data” offers organizations unprecedented opportunities to gain and maintain competitive advantage. Trying to exploit the strategic business potential embedded in big data, many organizations have started to renovate their business models or develop new ones, giving rise to the phenomenon of big-data business models. Although big-data business model research is still in its infancy, a significant number of studies on the topic have been published since 2014. We thus suggest it is time to perform a critical review and assessment of the literature at the intersection of business models and big data (analytics), thereby responding to recent calls for further research on and sustained analysis of big-data business models. In particular, our review uses three major criteria (big-data business model types, dimensions, and deployment) to assess the state of the big-data business model literature and identify shortcomings in this literature. On this basis, we derive and discuss five central research perspectives (supply chain, stakeholder, ethics, national, and process), providing guidance for future research and theory development in the area. These perspectives also have practical implications on how to address the current big-data business model deployment gap.

Big Data and Sustainable Consumption: A Review and Research Agenda

2021 ◽  
pp. 097226292110225
Author(s):  
Shobhana Chandra ◽  
Sanjeev Verma
Keyword(s):  
Big Data ◽  
Consumer Behaviour ◽  
Business Models ◽  
Sustainable Consumption ◽  
Big Data Analytics ◽  
Consumption Behaviour ◽  
Consumer Experiences ◽  
Systematic Understanding ◽  
The Impact

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

Big Data and Data Analytics Research

Web Services ◽  
2019 ◽  
pp. 2161-2171
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Web Science ◽  
New Business ◽  
Value Propositions

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

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