scholarly journals Comparing Mediation Effect of Functional and Emotional Value in the Relationship between Pros of Applying Big Data Analytics and Consumers’ Responses

2017 ◽  
Vol 9 (4) ◽  
pp. 66
Author(s):  
Shu-Yi Liaw ◽  
Thi Mai Le
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Perceived Value ◽  
Mediation Effect ◽  
Functional Value ◽  
Perceived Values ◽  
Emotional Value ◽  
The Relationship ◽  
Positive Effect

Applying Big Data analytics application brings many benefits for e-vendors and customers. Exploring the effect of consumer perceived value to consumers’ responses under applying Big Data analytics is lacking. And, what kind of perceived values do customers have more concerns under Big Data era. Therefore, the aims of this study are to analyze relationship between pros of applying Big Data analytics and Consumers’ responses under multiple mediators of perceived values as functional value and emotional value. Data analysis was done in a sample of 349 respondents. The results show that applying Big Data analytics have significant positive effect on customers’ responses. Functional and emotional values act as important mediators on the relationship between applying Big Data analytics and consumers’ responses. There are no significant different between mediator effect of functional value and emotional value. The findings of this study will have implications for e-vendors to understand the important mediator of perceived value on customers’ responses under Big Data analytics era.

Big data analytics adoption model for small and medium enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 483-513 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Wan Khairuzzaman Wan Ismail ◽  
Morteza Ghobakhloo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Mediation Effect ◽  
Unified Framework ◽  
Content Type ◽  
Medium Enterprises ◽  
The Relationship

Purpose Big data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view. Design/methodology/approach A survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study. Findings The results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs. Practical implications Providing a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses. Originality/value Although there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

The multiple dimensions of consumption values in ecotourism

2017 ◽  
Vol 11 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Ute Jamrozy ◽  
Kesinee Lawonk
Keyword(s):  
Perceived Risk ◽  
Purchase Intention ◽  
Perceived Value ◽  
Sampling Technique ◽  
Content Type ◽  
Functional Value ◽  
Perceived Values ◽  
Emotional Value ◽  
The Relationship ◽  
Value Functional

Purpose This exploratory study aims to examines the multidimensional aspects of perceived value (functional value, financial value, emotional value, social value, epistemic value and conditional value) in relation to purchase intention in ecotourism. The study evaluates the influence of trust and perceived risk as mediators on perceived value. Design/methodology/approach Data for this exploratory study stem from online survey responses of 314 participants and are analyzed using descriptive analyses, factor analyses and multiple regressions. Findings The study findings show that four significant predictors influence ecotourism purchase intention: emotional value, functional value, boredom alleviation value and epistemic value. Trust partially affects the relationship between perceived values and purchase intention. Meanwhile, there is no mediation effect of perceived risk in the relationship between perceived value and purchase intention. This study concludes that perceived values influence ecotourism purchase intention, with emotional value providing the strongest relation to purchase intention. Research limitations/implications The sample is based on selected criteria for a convenient sampling technique instead of a random sampling technique. However, criteria are in accordance with other ecotourism studies. Originality/value While multidimensional perceived values have been examined before, few papers have provided support for the emotional value dimension in ecotourism.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

Quality dominant logic in big data analytics and firm performance

2019 ◽  
Vol 25 (3) ◽  
pp. 512-532 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter ◽  
Marc de Bourmont
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Online Survey ◽  
Information Quality ◽  
Big Data Analytics ◽  
Firm Strategy ◽  
Theoretical Understanding ◽  
Content Type ◽  
The Relationship

Purpose Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance. Design/methodology/approach The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience. Findings The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance. Practical implications The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model. Originality/value The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

scholarly journals A Study on the Changes in Consumer Perceptions of the Relationship between Ethical Consumption and Consumption Value: Focusing on Analyzing Ethical Consumption and Consumption Value Keyword Changes Using Big Data

2021 ◽  
Vol 59 (2) ◽  
pp. 245-259
Author(s):  
Eunjung Shin ◽  
Ae-Ran Koh
Keyword(s):  
Big Data ◽  
Fair Trade ◽  
Ethical Consumption ◽  
Social Value ◽  
Functional Value ◽  
Emotional Value ◽  
The Relationship ◽  
Value Functional ◽  
Over Time ◽  
Consumption Value

The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword ‘ethical consumption’ and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a twoway mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

scholarly journals Evaluation of Mobile Applications Usability of Logistics in Life Startups

2020 ◽  
Vol 12 (21) ◽  
pp. 9023
Author(s):  
Dae-Ho Byun ◽  
Han-Na Yang ◽  
Dong-Seop Chung
Keyword(s):  
Big Data ◽  
Mobile Applications ◽  
Data Analytics ◽  
Big Data Analytics ◽  
User Interest ◽  
Usability Problems ◽  
Foreign Countries ◽  
Improvement Strategies ◽  
Measurement And Evaluation ◽  
The Relationship

This paper considers the usability of mobile applications operating within a new logistics domain referred to as logistics in life (LIL). The LIL sector has primarily been capitalized on by logistics startups which develop mobile applications or “apps” to provide customized services that penetrate niche spaces outside the reach of traditional logistics firms. The objective of this study is to evaluate whether LIL apps meet usability standards that satisfy users’ experiences. As a way to improve usability, problems should be identified through proper measurement and evaluation methods. To derive usability scores, usability testing targeting representative apps from Korea and foreign countries was conducted. The relationship between usability and user interest for each app was determined through big data analytics followed by recommended improvement strategies.

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