Die Nutzung mobiler Shopping Apps im Kaufprozess

Rudolph, Thomas; Nagengast, Liane; Bassett, Mag. Melanie; Bouteiller, David

doi:10.1007/s11621-015-0529-1

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Since the advent of the Internet, existing retailers have either extended their traditional in-store business by an additional online channel or new companies that have started up have implemented an e-commerce business model, selling articles online without operating any brick-and-mortar stores [ 73 ]. Regardless of the institutional-economic discussion on retail companies, the tasks intended for trading functions are economically necessary [ 47 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Identifying Application Areas for Machine Learning in the Retail Sector

Brackmann,

Hütsch,

Wulfert

2023

SN COMPUT. SCI.

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Machine learning (ML) has the potential to take on a variety of routine and non-routine tasks in brick-and-mortar retail and e-commerce. Many tasks previously executed manually are amenable to computerization using ML. Although procedure models for the introduction of ML across industries exist, the tasks for which ML can be implemented in retail need to be determined. To identify these application areas, we followed a dual approach. First, we conducted a structured literature review of 225 research papers to identify possible ML application areas in retail, as well as develop the structure of a well-established information systems architecture. Second, we triangulated these preliminary application areas with the analysis of eight expert interviews. In total, we identified 21 application areas for ML in online and offline retail; these application areas mainly address decision-oriented and economic-operative tasks. We organized the application areas in a framework for practitioners and researchers to determine appropriate ML use in retail. As our interviewees provided information at the process level, we also explored the application of ML in two exemplary retail processes. Our analysis further reveals that, while ML applications in offline retail focus on the retail articles, in e-commerce the customer is central to the application areas of ML.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Identifying Application Areas for Machine Learning in the Retail Sector

Brackmann,

Hütsch,

Wulfert

2023

SN COMPUT. SCI.

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“…The mobile shopping application provides other services apart from the interface of the seller and the buyer. In the mobile shopping application, the most attractive services from the mobile shopping application using online surveys include dimension, price, monetary, convenience, and situational value information (Rudolph, Nagengast, Melanie, & Bouteiller, 2015). This new technology in buying and selling answers the needs of consumers and sellers in processing data and instruments depending on the needs of each user (Araste, Mansouri, Foruzand, & Ganj Khanloo, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Service Quality Analysis of Tokopedia Application Using Text Mining Method

Bustami

Noviaristanti

2022

IJOMFA

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Shopping transformation using online applications makes it easier for sellers and buyers to make transactions. In Indonesia, there are several shopping applications, Tokopedia is one of them. Tokopedia is one of the shopping applications developed in Indonesia. To ensure users’ loyalty to the Tokopedia application, quality analysis is done to determine how the application could be improved or developed. This study applied the Quality Measurement Theory to examine the mobile shopping application service quality with three variables: interaction quality, environmental quality, and outcome quality. The data used in this study are users’ reviews of the Tokopedia application on the Google Play Store website. Data retrieval is done by scrapping for the period June 2020 to December 2020 with a total data of 21,616 reviews. The review data is then analyzed using the text mining method. The sentiment analysis results indicated a value of 65% positive sentiment and 35% negative sentiment. In multi-class classification, the results were 16% interaction quality, 26% environment quality and 58% outcome quality. The text network analysis determined how the word network was formed and uncovered negative words mentioned in each indicator in the mobile apps service quality.

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Store Brand Management: Gestaltung des Ausdruckssystems

Redler

2018

Die Store Brand

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Die Nutzung mobiler Shopping Apps im Kaufprozess

Cited by 8 publications

References 12 publications

Identifying Application Areas for Machine Learning in the Retail Sector

Identifying Application Areas for Machine Learning in the Retail Sector

Service Quality Analysis of Tokopedia Application Using Text Mining Method

Store Brand Management: Gestaltung des Ausdruckssystems

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