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Open Access
Article
Publication date: 25 June 2020

Elisabeth Happ, Ursula Scholl-Grissemann, Mike Peters and Martin Schnitzer

Offline retail stores have been working on improving their in-store customer experience; they have begun to realise the physical advantage they have over online channels…

13832

Abstract

Purpose

Offline retail stores have been working on improving their in-store customer experience; they have begun to realise the physical advantage they have over online channels. Especially sports products have a number of unique features, such as high emotional involvement or a sense of community; additionally, sports customers put emphasis on multisensory brand experience at the point of sale. This study examines the in-store customer experience (ISCX) in offline sports retail stores, taking into account the commercial uniqueness of sport.

Design/methodology/approach

A qualitative study (focus groups; n = 16) and quantitative survey (cross-sectional survey design; n = 238) were conducted to measure ISCX in sports retail stores.

Findings

The results suggest that the customers' in-store experience has a significant influence on customers' satisfaction with the sports retailer and their likeliness to recommend the store to friends, which, in turn, is significantly affected by customers' satisfaction with the retailer. Moreover, social responses to actors involved in the service encounter, for example, the interaction with employees, play a significant role for the customer in-store experience. Accordingly, sports customers strive not only for functional benefits inherent in the interaction with customers and employees but also for social benefits.

Originality/value

This study extends the knowledge by (1) replicating the ISCX scale, (2) analysing ISCX in a sports retail environment and (3) examining the influence of ISCX on the Net Promoter Score. Moreover, the findings support managers' know-how about in-store setting and help to maintain the customer relationship.

Details

International Journal of Sports Marketing and Sponsorship, vol. 22 no. 2
Type: Research Article
ISSN: 1464-6668

Keywords

Article
Publication date: 22 September 2017

Juan Carlos Bustamante and Natalia Rubio

In a world where customer empowerment is continuously increasing and changing the service landscape, retailers must provide memorable shopping experiences to retain and attract…

12550

Abstract

Purpose

In a world where customer empowerment is continuously increasing and changing the service landscape, retailers must provide memorable shopping experiences to retain and attract new customers. When customers decide to go shopping in physical stores, they expect to enjoy their visit, experiencing cognitive, affective, social, and physical responses evoked by in-store stimuli. The purpose of this paper is to propose and validate a scale to measure in-store customer experience (ISCX).

Design/methodology/approach

This study’s theoretical review of customer experience (CX) demonstrates that a formative model provides the best structure for measuring the construct ISCX. Furthermore, the study follows the guidelines for rigorous construction of a formative scale, which include three main stages: generation of items, scale purification, and assessment of scale validity and reliability.

Findings

The results provide evidence that a formative third-order scale with a reflective second-order dimension (social experience) and three reflective first-order dimensions (cognitive, affective, and physical experience) has satisfactory psychometric properties. The findings also provide useful information on the effect of the ISCX scale on key performance variables such as satisfaction and loyalty to the store.

Originality/value

The ISCX scale proposed constitutes a useful multi-concept diagnostic tool for use by retailers to create fully experiential shopping environments with differential value for the customer. By providing a complete, robust, precise measure of CX in a retail environment, the scale gives researchers a structured way to examine the causes and consequences of CX in retail.

Details

Journal of Service Management, vol. 28 no. 5
Type: Research Article
ISSN: 1757-5818

Keywords

Open Access
Article
Publication date: 18 July 2022

Youakim Badr

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input…

1225

Abstract

Purpose

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

Design/methodology/approach

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Findings

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

Research limitations/implications

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

Practical implications

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

Originality/value

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

Article
Publication date: 14 July 2022

Subhadip Roy and Priyanka Singh

Measurement scales for sensory experience in retailing exist for sight, touch and sound. In the present study, the authors aim to develop the olfactory experience (OEX) scale in…

Abstract

Purpose

Measurement scales for sensory experience in retailing exist for sight, touch and sound. In the present study, the authors aim to develop the olfactory experience (OEX) scale in the context of retailing.

Design/methodology/approach

Based on literature review and six studies that follow standard scale development protocols (combined n = 1,203), the authors develop and validate a three-dimensional OEX scale. The scale is further validated in the final study in a different market set-up than the first five.

Findings

The authors found the three dimensions of OEX as (scent) company, congeniality and congruity. The OEX scale is found to be generalizable and valid across different cultural and market set-ups. In addition, the OEX (i.e. the scale) was found to effect psychological and behavioral outcomes of the consumer in a significant manner.

Research limitations/implications

The present study contributes to the domain of sensory experience in retailing with the OEX scale and provides three new dimensions of OEX for the academicians to further explore.

Practical implications

The OEX scale provides a ready to use tool for the retailer to gauge the level of OEX in the store and to predict consumer attitudes and behavior.

Originality/value

The study is the first to develop a scale for OEX in retailing or for that matter in consumer behavior.

Details

Journal of Service Management, vol. 34 no. 3
Type: Research Article
ISSN: 1757-5818

Keywords

Article
Publication date: 30 March 2023

Wilson Charles Chanhemo, Mustafa H. Mohsini, Mohamedi M. Mjahidi and Florence U. Rashidi

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the…

Abstract

Purpose

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

Design/methodology/approach

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Findings

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

Originality/value

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 3 January 2023

Saleem Raja A., Sundaravadivazhagan Balasubaramanian, Pradeepa Ganesan, Justin Rajasekaran and Karthikeyan R.

The internet has completely merged into contemporary life. People are addicted to using internet services for everyday activities. Consequently, an abundance of information about…

Abstract

Purpose

The internet has completely merged into contemporary life. People are addicted to using internet services for everyday activities. Consequently, an abundance of information about people and organizations is available online, which encourages the proliferation of cybercrimes. Cybercriminals often use malicious links for large-scale cyberattacks, which are disseminated via email, SMS and social media. Recognizing malicious links online can be exceedingly challenging. The purpose of this paper is to present a strong security system that can detect malicious links in the cyberspace using natural language processing technique.

Design/methodology/approach

The researcher recommends a variety of approaches, including blacklisting and rules-based machine/deep learning, for automatically recognizing malicious links. But the approaches generally necessitate the generation of a set of features to generalize the detection process. Most of the features are generated by processing URLs and content of the web page, as well as some external features such as the ranking of the web page and domain name system information. This process of feature extraction and selection typically takes more time and demands a high level of expertise in the domain. Sometimes the generated features may not leverage the full potentials of the data set. In addition, the majority of the currently deployed systems make use of a single classifier for the classification of malicious links. However, prediction accuracy may vary widely depending on the data set and the classifier used.

Findings

To address the issue of generating feature sets, the proposed method uses natural language processing techniques (term frequency and inverse document frequency) that vectorize URLs. To build a robust system for the classification of malicious links, the proposed system implements weighted soft voting classifier, an ensemble classifier that combines predictions of base classifiers. The ability or skill of each classifier serves as the base for the weight that is assigned to it.

Originality/value

The proposed method performs better when the optimal weights are assigned. The performance of the proposed method was assessed by using two different data sets (D1 and D2) and compared performance against base machine learning classifiers and previous research results. The outcome accuracy shows that the proposed method is superior to the existing methods, offering 91.4% and 98.8% accuracy for data sets D1 and D2, respectively.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

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