Su Chen, Xinyu Tan, Wenbin Shen, Rongzhi Liu and Yangui Chen
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech…
Abstract
Purpose
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech businesses.
Design/methodology/approach
About 67 high-tech businesses in China focusing on technical innovation from the Guotai’an database are selected to carry out empirical analysis.
Findings
It is observed that the age, educational and professional backgrounds of college entrepreneurs profoundly influence their ventures geared toward high-tech innovation. Moreover, the transformation abilities, managerial proficiency and growth capabilities, which characterize these ventures, notably affect business performance. They further serve as a moderator in the relationship between the entrepreneurial backgrounds of college students and the overall business performance of their enterprises.
Originality/value
It insinuates novel strategic avenues for collegiate entrepreneurs’ entrepreneurial mindset and industrial positioning. Moreover, our findings will not only augment the practical research in the realm of collegiate entrepreneurship but also enhance the study of technological innovation theories, thereby offering further insight and guidance for collegiate entrepreneurs’ innovative endeavors and entrepreneurial pursuits.
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Tsung Hung Lee, Chung-Jen Fu and Yin Yuan Chen
The purpose of this paper is to assess the relationship between trust factors and buying behavior among consumers in the organic food market in Taiwan.
Abstract
Purpose
The purpose of this paper is to assess the relationship between trust factors and buying behavior among consumers in the organic food market in Taiwan.
Design/methodology/approach
The researchers developed a questionnaire using latent variables including the trust factors, utilitarian attitudes, hedonic attitudes, buying behavior and demographic information of consumers of organic foods. Confirmatory factor analysis and the structural equation modeling were conducted using LISREL 8.80 for Windows.
Findings
The empirical results indicated that health content, locally produced products, organic food labels and price premiums positively and significantly affected utilitarian and hedonic attitudes. Both utilitarian attitudes and hedonic attitudes positively and significantly affected respondents’ buying behavior. A series of theoretical implications were identified.
Practical implications
The researchers concluded that providing consumers with practical information related to organic food, establishing local production facilities, developing content, standardizing labeling procedures and promoting a new organic certification system for small-scale producers will encourage more consumers to purchase organic food.
Originality/value
This study first examines the food trust buying behavior of organic foods and related consumption behavior theory questions. It mainly takes the stimulus–organism–response model as the foundation of its approach. Simultaneously, it also conforms to utilitarian behavior theory, and the process by which consumers become better aware of organic foods’ quality.
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Ibrahim Alnawas and Jane Hemsley-Brown
The purpose of this paper is twofold: first, to examine the differential effect of two cognitive (i.e. product experience, outcome focussed) and two emotional experiences (i.e…
Abstract
Purpose
The purpose of this paper is twofold: first, to examine the differential effect of two cognitive (i.e. product experience, outcome focussed) and two emotional experiences (i.e. surprise and immersion) on customers’ cognitive outcomes (i.e. satisfaction, trust and value), and customers’ emotional outcomes (i.e. passion, connection and affection); and second, to test the differential effect of customers’ cognitive and emotional outcomes on switching resistance loyalty (SRL).
Design/methodology/approach
Survey data were collected from 843 respondents using an online panel in the UK. Structural equation modelling was employed to analyse the data (AMOS 18.0).
Findings
First, cognitive experiences had a more significant effect on customers’ cognitive outcomes compared to their effect on customers’ emotional outcomes. Second, emotional experiences had a more significant effect on customers’ emotional outcomes compared to their effect on customers’ cognitive outcomes. Third, the impact of customers’ emotional outcomes on SRL was not significantly higher compared to that of customers’ cognitive outcomes. Fourth, the indirect effect of cognitive experiences on SRL was significantly higher, compared to that of emotional experiences.
Originality/value
The key contribution of this research stems from examining the differential effect of cognitive and emotional experiences on different consumers’ cognitive and emotional outcomes, thus providing deeper insights into the nature of the relationship between such variables.
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Valsaraj Payini, Badrinarayan Srirangam Ramaprasad, Jyothi Mallya, Megha Sanil and Vidya Patwardhan
The purpose of this paper is to investigate the influence of two personality traits (i.e. food neophobia (FN) and domain-specific innovativeness (DSI)) on revisit intentions of…
Abstract
Purpose
The purpose of this paper is to investigate the influence of two personality traits (i.e. food neophobia (FN) and domain-specific innovativeness (DSI)) on revisit intentions of consumers in the food festival context.
Design/methodology/approach
The study adopted a FN scale, DSI scale and food festival revisit intention scale to capture the study constructs. Furthermore, data were collected from 265 food festival attendees in the town of Udupi (State of Karnataka, India). Furthermore, the study adopted structural equating modeling to investigate the relationship between the three study constructs.
Findings
The findings of this study reveals that DSI seems to exercise significant positive influence on consumers revisit intentions for the food festival. On the contrary, FN seems to exercise a negative influence on consumers revisit intentions. This relation, however, is found to be non-significant.
Practical implications
The findings of this study are expected to help food festival event managers, organizers and practitioners outline and delineate marketing strategies so as to increase the revisit intentions of consumers for the food festivals.
Originality/value
FN and DSI have been extensively studied in the context of consumers’ food consumption and related behaviors. On the contrary, majority of the studies that relate to festival revisit intentions situate consumers’ experiential values and satisfaction with attributes of the festival itself (e.g. location, affordability, quality of food, infrastructure, venue ambiance, service quality, entertainment avenues, timing and duration and size of the festival) as key precursors to their revisit propensity. This research endeavor, however, attempts to investigate whether, notwithstanding other food festival attributes, personality traits of individuals (i.e. FN and DSI) exercise any influence on food festival revisit intentions.
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Ramesh Roshan Das Guru, Marcel Paulssen and Arnold Japutra
This study aims to extend research in marketing on two important relational constructs, customer satisfaction and brand attachment, by comparing their long-term effects on…
Abstract
Purpose
This study aims to extend research in marketing on two important relational constructs, customer satisfaction and brand attachment, by comparing their long-term effects on customer behaviors with different levels of performance difficulty in a relatively understudied domain of durable products.
Design/methodology/approach
Using a two-stage quantitative study with US customers from five durable product categories, the authors first explored the hierarchy of customers’ loyalty behaviors based on increasing effort in a pretest study (N = 675). Then, the authors tested the effectiveness of satisfaction and brand attachment for customers’ loyalty behaviors over a nine-month period in a longitudinal study (N = 2,284) with customers from the same product categories.
Findings
Compared to satisfaction, brand attachment emerges as a stronger long-term predictor of customer behaviors. The performance difficulty of customer behaviors positively moderates the impact of brand attachment and negatively moderates the impact of customer satisfaction. Brand attachment is particularly effective in predicting difficult-to-perform customer behaviors, which require customers to expend resources such as time and money. Customer satisfaction is mainly effective for predicting easy-to-perform behaviors, but its long-term impact is significantly lower for easy-to-perform behaviors than brand attachment.
Research limitations/implications
The use of consumer durables in the study and samples from only one country restricts the generalizability of the findings.
Practical implications
The complementary roles of customer satisfaction and brand attachment are highlighted. Only satisfying customers is not enough to engage customers in behaviors that require resources such as money, time and energy for the brand.
Originality/value
A comparative study on the long-term effectiveness of two established relational metrics in explaining different customer behaviors varying in their performance difficulty in an understudied domain of durable products.
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Christian Rainero and Giuseppe Modarelli
In the disruptive technologies era, the lack of convincing business cases on blockchain (BC) adoption about food supply chain, the existence of uncertainties and barriers to…
Abstract
Purpose
In the disruptive technologies era, the lack of convincing business cases on blockchain (BC) adoption about food supply chain, the existence of uncertainties and barriers to adoption due to knowledge scarcity on characteristics as well as the potentialities and risks involved in it, have triggered the need to investigate the first multinational BC adoption for food supply chain in Europe, to consider how it can guarantee knowledge for the consumption/purchase decision-making and the creation-mechanism of consciousness for sustainable behavioral choice.
Design/methodology/approach
The authors provide a field exploratory analysis based on customers' perceptions and real knowledge about BC (as a knowledge-constructive tool) in the food and beverage sector. This connected with the need for an informed context, favoring sustainable conscious decision-making related to both the food chain and innovation acceptance. This analysis included the use of innovation acceptance as a corporate social responsibility (CSR) strategic orientation through a survey- and interview-based field analysis (80 respondents).
Findings
The findings of this study can be considered as antecedents of innovation acceptance in the sector. The analysis assesses consumers' scarce knowledge and perceptions on the BC system, the scarce usage level and the higher acquiring propensity for traceable foodstuffs generating bi-directional/dimensional value, considering that consumption habits could change through security and certainty antecedents and induced knowledge provided by external technological intervention.
Originality/value
By trying to match innovation and the knowledge-construction need as a vehicle for acceptance, the theoretical contribution would empower the literature on food traceability from the perspective of strategic BC application through a from-knowledge-to-knowledge strategy.
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Azra Nazir, Roohie Naaz Mir and Shaima Qureshi
The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud…
Abstract
Purpose
The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.
Design/methodology/approach
This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.
Findings
DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.
Originality/value
To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.
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Mohammad Irfan Bala and Mohammad Ahsan Chishti
Fog computing is a new field of research and has emerged as a complement to the cloud which can mitigate the problems inherent to the cloud computing model such as unreliable…
Abstract
Purpose
Fog computing is a new field of research and has emerged as a complement to the cloud which can mitigate the problems inherent to the cloud computing model such as unreliable latency, bandwidth constraints, security and mobility. This paper aims to provide detailed survey in the field of fog computing covering the current state-of-the-art in fog computing.
Design/methodology/approach
Cloud was developed for IT and not for Internet of Things (IoT); as a result, cloud is unable to meet the computing, storage, control and networking demands of the IoT applications. Fog is a companion for the cloud and aims to extend the cloud capabilities to the edge of the network.
Findings
Lack of survey papers in the area of fog computing was an important motivational factor for writing this paper. This paper highlights the capabilities of the fog computing and where it fits in between IoT and cloud. This paper has also presented architecture of the fog computing model and its characteristics. Finally, the challenges in the field of fog computing have been discussed in detail which need to be overcome to realize its full potential.
Originality/value
This paper presents the current state-of-the-art in fog computing. Lack of such papers increases the importance of this paper. It also includes challenges and opportunities in the fog computing and various possible solutions to overcome those challenges.
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Patricia Bazan and Elsa Estevez
The objective of the work is to analyze the impact of the Internet of Things (IoT) concepts and associated technologies in the framework of organizations and the management of…
Abstract
Purpose
The objective of the work is to analyze the impact of the Internet of Things (IoT) concepts and associated technologies in the framework of organizations and the management of their processes and how event orientation, as well as the structure of said business processes, can play an important role in this new organizational model. The main contribution of this work is to present a conceptualization of the research, identify approaches and challenges that require further study, and as a result, a proposal for future research.
Design/methodology/approach
The methodology comprises a qualitative analysis using secondary data. The approach relies on searches of scientific papers conducted in well-known databases, identifying research work around the IoT and Industry 4.0 applied to business process management. Based on the identified papers, the authors selected the most relevant and the latest publications, and categorized their contributions and findings based on open and selective coding. In total, the analysis is based on 95 papers that were selected and analyzed in depth.
Findings
The results of this research allow analyzing and ordering the existing contributions around Industry 4.0 and its impact on current organizations. The proposed conceptualization was derived from the analysis of the state of the research and identifies four categories: (1) improvements caused by Industry 4.0 and its impact on inter-organizational relations, (2) new architectural models and infrastructure of remote resources, their movement from the cloud to the edge and its effect on business processes, (3) context-aware concepts brought to business process management (BPM) linked to unstructured business processes and (4) complex event processing as a possible means for business processes sensitive to IoT signals.
Practical implications
The construction of current software ecosystems is strongly affected by the variety of information sources that feed them, as well as their volume. In addition, business processes represent organizations internally and are challenged to transcend the limits of companies due to the mentioned changes in software ecosystems. Industry 4.0 in conjunction with BPM re-defines the business process management paradigm and leads them to acquire the dynamism and sensitivity to the context that they usually did not have, as well as force them to move toward distributed platforms.
Originality/value
This paper assesses the state of the art in Industry 4.0 and business process management. The area can be defined as the intersection of two bigger areas highly relevant for organizations; on the one hand, the management and execution of business processes; and on the other hand, new conceptual, technological and methodological challenges to information systems that have to become more sensitive to event processing and also have to consume a large volume of data permanently and ubiquitously.
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Mohammad Khalid Pandit, Roohie Naaz Mir and Mohammad Ahsan Chishti
The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational…
Abstract
Purpose
The intelligence in the Internet of Things (IoT) can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment. The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer, which offers a computing infrastructure to minimize the latency in service delivery and execution. For this purpose, a task scheduling policy based on reinforcement learning (RL) is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.
Design/methodology/approach
To realize this, the authors proposed a two-level neural network (NN)-based task scheduling system, where the first-level NN (feed-forward neural network/convolutional neural network [FFNN/CNN]) determines whether the data stream could be analyzed (executed) in the resource-constrained environment (edge/fog) or be directly forwarded to the cloud. The second-level NN ( RL module) schedules all the tasks sent by level 1 NN to fog layer, among the available fog devices. This real-time task assignment policy is used to minimize the total computational latency (makespan) as well as communication costs.
Findings
Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.
Originality/value
The proposed algorithm fundamentally solves the problem of task scheduling in real-time fog-based IoT with best resource utilization, minimum makespan and minimum communication cost between the tasks.