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PurposeWhile marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs).Design/methodology/approachThis research employs a fuzzy-set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs.FindingsThe key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use, and (2) the configurations for small firms are different from those for medium-sized firms.Research limitations/implicationsThe research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers.Practical implicationsThe study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.Originality/valueThe study contributes to the literature by addressing an important yet underresearched area, i.e. marketing analytics use in SMEs, applying a configurational approach to the research phenomenon. It highlights different pathways to marketing analytics use in SMEs. The findings provide empirical evidence on the possibility and implication of marketing analytics use being asymmetrical and different between small and medium-sized firms.

While research underpinned by the resource-based view (RBV) appears to suggest that IT-enabled capabilities are positively linked to competitive advantage, such a link is often seen as a black box as the processes through which competitive advantage can be gained appears unclear. In particular, research appears to suggest that information processing capability is linked to decision-making effectiveness and competitive advantage; however, little research appears to examine the interrelationship among them. This study, drawing on the RBV, develops a mediation model to examine the link between competitive advantage and the key tenets of value, rarity, inimitability and non-substitutability of information processing capability in the context of business analytics; and whether this link is mediated through decision-making effectiveness. Based on data collected from 633 UK companies, this study shows that there is a positive link between the value, rarity and inimitability characteristics of information processing capability and competitive advantage, which is partially mediated by decision-making effectiveness. The findings contribute to the theoretical development of the RBV by developing a mediation model that looks inside the black box. They also contribute to managers’ knowledge and understanding of the mechanism through which the strategic value of information processing capability can be maximized.

Fish school feeding behavior analysis based on images can provide important information for aquaculture managers to make effective feeding decision. However, it is a challenging task due to intra-class variation, cross-occlusion, and unbalanced image categories in real high-density industrial farming. At present, most of the existing works on fish school feeding behavior are limited because they seem to ignored the spatial relationship between the region of interest in fish feeding images. To address this research gap, we propose a dual attention network with Efficientnet-B2 for fine-grained short-term feeding behavior analysis of fish school. The algorithm includes EfficientNet-B2 network and two parallel attention modules, which focus on the feature extraction of the feeding region. In addition, several training strategies, such as mish activation function, ranger optimizer, label smoothing, and cosine annealing, are employed to improve the algorithm performance. Especially, label smoothing technique is used to address the problem of image class imbalance. To evaluate the effectiveness of our method, performance of proposed algorithm is analyzed on fish school feeding behavior dataset and it is also compared with benchmark Convolutional Neural Networks (CNNs) including AlexNet, VGG, Inception, ResNet, Densenet, SENet, and MobileNet. Comprehensive experimental results show that proposed algorithm achieves very good results in terms of the accuracy (the test accuracy is 89.56% on datasets), precision, parameters and floating point operations per second (FLOPS), compared with the benchmark classification algorithm. Therefore, we proposed method can be integrated into aquacultual vision system to guide farmers to plan their feeding strategy.

Traceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability.

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

The number of users of well-known e-commerce websites such as Taobao, Jingdong, and Amazon has exceeded one billion. This means that the information provided by these and similar websites which is growing very fast, could be a valuable source of support for decision making, if discovered. All kinds of data derived from e-commerce websites must be managed and analyzed in order for them to remain competitive and to prevent reduced satisfaction or interest of customers. Business Analytics and Big Data Analytics are methodologies that can discover valuable insight hidden in data. In this paper, recent studies on e-commerce and Business Analytics or Big Data Analytics published within the last five years will be surveyed to discover what kind of business value is created with usage of analytics in these studies. This investigation also has the potential to find research gaps and direct future research.

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to managers’ knowledge and understanding by demonstrating how business analytics should be implemented to improve DME.

Tricholoma matsutake (T. matsutake) is a special type of fungus known as "the king of bacteria", and has the very high economic value. However, it is also very difficult to transport due to its corruptibility. Therefore, tracing and tracking the quality and safety of T. matsutake in the cold chain is very important and necessary. Based on changes in the cold chain environmental parameters determine the safety of T. matsutake is a viable option. This paper developed and tested a real-time monitoring traceability system (RM-TM) using emerging Internet of Things (IoT) technologies for monitoring the cold chain logistics environmental parameters of T. matsutake. Finally, system testing and evaluation have shown that RM-TM can track and monitor temperature, humidity, oxygen and carbon dioxide fluctuations in the cold chain in real-time. In addition, the collected data can be used to increase the transparency of cold chain logistics and to more effectively control quality, safety, and traceability. In general, the system evaluation results show that it is reliable and meets the requirements of users.

Using fuzzy-set qualitative comparative analysis (fsQCA), this study investigates the conditions leading to a higher level of innovation. More specifically, the study explores the impact of inter-organisational knowledge transfer networks and organisations' internal capabilities on different types of innovation in Small to Medium size Enterprises (SMEs) in the high-tech sector. A survey instrument was used to collect data from a sample of UK SMEs. The findings show that although individual factors are important, there is no need for a company to perform well in all the areas. The fsQCA, which enables the examination of the impacts of different combinations of factors, reveals that there are a number of paths to achieve better incremental and radical innovation performance. Companies need to choose the one that is closest to their abilities and fits best with their resources.

According to Simon's (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives' perceptions of using agent-based support systems and the criteria for design and development of their ''ideal'' intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of ''ideal'' agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives' perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users' point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation.

Using intelligent agent-based systems to support information processing for executives has not been significantly advanced in both theory and practice. Research into this field tends to focus more on technical aspects than on social perspective. When executives are faced with increasing information availability and uncertainty in the business environment, using intelligent agent-based systems to enhance executives' information processing capability appears both an opportunity and a necessity. This study examines UK executives' perceptions of intelligent agent-based systems for information scanning, filtering, interpretation and alerting. The study follows a deductive research design, i.e. hypothesis formulation and testing from the user's perspective. Qualitative data was collected through focus groups and interviews with executives in the UK. The study produces rich evidence that challenges preconceptions of using agent-based information processing system by executives. The findings develop insight into executives' behavior in information processing, which has implications for intelligent system developers and organizational information processing practice.

Expert systems (ESs) were introduced more than two decades ago, but their effectiveness and success are still in debate. This paper attempts to make a contribution to the better understanding of ES applications from a knowledge transfer perspective. This paper argues that an ES application is knowledge transfer that uses Information and Communication Technologies (ICT). Underpinned by knowledge transfer theories and through a series of empirical investigations of ES projects in agriculture, this study recognises the importance of human interactions in the ESs implementation process. Based on the evidence collected, a number of key players are examined. They are knowledge provider/sender, knowledge engineer, knowledge extensionist and knowledge recipient. This paper represents a first attempt to acknowledge the role of a knowledge extensionist in the ICT-based knowledge transfer process. The name ‘extensionist’ is borrowed from earlier literature and describes an actor whose role is an intermediary in supporting transferring knowledge towards the knowledge user. Findings demonstrate the significant contributions made by extensionists towards the success of ESs applications. It is argued that the rigidity and limitations of ESs in ICT-based knowledge transfer can be significantly reduced with the involvement of close human interactions with the knowledge recipient.

This paper presents a research attempt to develop and evaluate a Web-based tele-diagnosis system for fish diseases. Fish disease diagnosis is a complicated process and requires high level of expertise. A tele-diagnosis system T-VET is being developed and tested in China. T-VET is the key sub system of a Web-based expert system for fish disease diagnosis. This paper looks at why a tele-diagnosis system is needed and what are the advantages and difficulties in developing and using such a system. The system's architecture and components are described. A user-centered approach was adopted throughout the development process to ensure the system would meet users' needs and will be accepted. As a result, the system functions were emerged and improved by users' requirements. The experience and feedback from testing and evaluating the system with potential users are discussed. Some valuable insights into the use of the Internet as a tele-diagnosis tool in aquaculture are highlighted.

A hybrid approach for integrating group Delphi, fuzzy logic and expert systems for developing marketing strategies is proposed in this paper. Within this approach, the group Delphi method is employed to help groups of managers undertake SWOT analysis. Fuzzy logic is applied to fuzzify the results of SWOT analysis. Expert systems are utilised to formulate marketing strategies based upon the fuzzified strategic inputs. In addition, guidelines are also provided to help users link the hybrid approach with managerial judgement and intuition. The effectiveness of the hybrid approach has been validated with MBA and MA marketing students. It is concluded that the hybrid approach is more effective in terms of decision confidence, group consensus, helping to understand strategic factors, helping strategic thinking, and coupling analysis with judgement, etc.

A framework for a hybrid intelligent support system is proposed, on the basis of a discussion of the main problems of current computer‐based support systems and the roles for computer‐based systems in developing marketing strategy. The objectives of the framework are: to integrate the strengths of different support techniques and technologies; to assist strategic analysis; to couple strategic analysis with managers’ judgement; to help managers deal with uncertainty; and to aid strategic thinking. Within this framework, the benefits of different strategic analysis models are combined to offer enhanced support for a logical sequence of strategic analysis, while the advantages of diverse support techniques and technologies are integrated and fitted to support different aspects of the marketing strategy development process. As well as the theoretical basis for the proposed framework, the paper also examines the associated technical issues.

This paper looks at expert systems in management, by using a business game as an experimental vehicle. An expert system called EXGAME was developed to play a business game, which is normally played by students, with minimal human intervention. This paper concentrates on the effectiveness of EXGAME as compared with human players for tasks at different levels. EXGAME was able to replace human players in decision making at the operational level, and indeed outperform them. However, it proved to be impractical to replace human input at the strategic level. The paper also sheds some light on the problems of trying to build an expert system when there is no real expert. A combination of a modular knowledge-base structure and a process of ‘learning by experimentation’ was effective in this case; it is suggested that this may be an appropriate development strategy in other similar situations. © 1998 John Wiley & Sons, Ltd.

Strategic marketing planning is a complex task which involves many expert judgements. Expert systems would have a tremendous impact in this field, but the development of marketing expert systems has proven to be difficult. Describes a hybrid expert system, HYMS, for strategic marketing planning. HYMS aims to provide a structured marketing planning process, guide a user through this process, offer expert advice at key stages and finally make recommendations for users in setting objectives and strategies. It combines the advantages of the expert system and decision support system technology in order to enhance its effectiveness. Presents a a brief review on expert systems and decision support systems in marketing and then discusses the selection of a strategic marketing planning model and software for HYMS. Describes the architecture of the prototype system and the knowledge elicitation issues. Finally, proposes the further extension and enhancement of the HYMS system and offers a brief conclusion from current research.

Ignorance of user factors can be seen as one of the nontechnical issues contributing to expert system failure. An expert advisory system is built for nonexpert users; the users' acceptance is a very important factor for its successful implementation. If an expert advisory system satisfactorily represents the expertise in the domain, there still remains the question: "Will the end-users use the system?" This paper aims to address users' issues by analysing their reactions towards an expert advisory system called ADGAME, developed to help its users make better decisions in playing a competitive business game. Two experiments with ADGAME have been carried out. The research results show that, when the use of the expert advisory system is optional, there is considerable reluctance to use it, particularly amongst the "worst" potential users. Users also doubt the potential benefits in terms of improved learning and confidence in decisions made. Strangely, the one positive expectation that users had, that the system would save them time, proved not to be the case in practice; ADGAME appears to improve the users' effectiveness rather than their efficiency. © 1995.

The complexity of strategic market planning is well known to those working in this area. It requires many expert and intuitive judgements based on a number of interrelated, yet diverse, market factors. It would seem that expert systems and decision support systems might have a large impact in this area but the development of such systems has proven difficult with results, so far, less than originally conceived. This paper describes a hybrid expert system HYMS for strategic marketing planning. HYMS aims to provide a structured marketing planning process, guide a user through this process, offer expert advice at key stages and finally make recommendations for users in setting objectives and strategies. It combines the advantages of the expert system and decision support system technology in order to enhance its effectiveness. The paper first presents a brief review on expert systems and decision support systems in marketing. It also discusses the selection of a strategic marketing planning model fo...

Knowledge management and retrieval are important parts of an e-Learning system. The retrieval intention is hard to express by traditional keywords based retrieval mode. In this paper, based on analyzing keywords in semantic layer, semantic similarity is calculated to catch the user's retrieval intention. Finally, the proper semantic mapping from keywords to concepts in knowledge base is realized. The semantic retrieval approach in this paper is significant for the individualized e-Learning system construction.

Convergence of technologies in the Internet and the field of expert systems (ES) offer new ways of sharing and distributing knowledge (Sedbrook, 1998). Power (2000) argues that rapid advances in Internet technologies have opened new opportunities for enhancing traditional decision support systems and expert systems. Internet technology can change the way that an expert system is developed and distributed. For the first time, knowledge on any subject can directly be delivered to users anywhere and anytime through a Web-based ES. Because the main function of an ES is to mimic expertise and distribute expert knowledge to nonexperts, these benefits can be greatly enhanced with the emergence of the Internet. The current focus on networked and Internet-based applications demands new architectures for “intelligent” systems as well as creating new possibilities for research and development in this field (Caldwell, Breton, & Holburn, 2003). This article provides an overview of Web-based expert systems with examples. Benefits and challenges are discussed by comparing Web-based ES with traditional standalone ES from both the development and the application perspectives using Turban and Aronson’s knowledge engineering framework.

In the context of increasing usage of intelligent agent and ontology technologies in business, this study explores the ways of adopting these technologies to revitalize current executive information systems (EIS) with a focus on semantic information scanning, filtering, and reporting/alerting. Executives’ perceptions on an agent-based EIS are investigated through a focus group study in the UK, and the results are used to inform the design of such a system. A visualization prototype has been developed to demonstrate the main features of the system. This study presents a specific business domain for which ontology and intelligent agent technology could be applied to advance information processing for executives.

Fish disease diagnosis is a complicated process and requires high level of expertise, an expert system for fish disease diagnosis is considered as an effective tool to help fish farmers. However, many farmers have no computers and are not able to access the Internet. Telephone and mobile uses increase rapidly, so, the provision of call centre service appears as a sound alternative support channel for farmer to acquire counseling and support. This paper presents a research attempt to develop and evaluate a call center oriented Hybrid disease diagnosis & consulting system (H-Vet) in aquaculture in China. This paper looks at why H-Vet is needed and what are the advantages and difficulties in the developing and using such a system. A machine learning approach is adopted, which helps to acquire knowledge when enhancing expert systems with the user information collected through call center. This paper also proposes a fuzzy Group Support Systems (GSS) framework for acquiring knowledge from individual expert and aggregating knowledge into workgroup knowledge by H-Vet in the situation of difficult disease diagnosis. The system’s architecture and components are described.

Lack of knowledge and skills among small business managers has been well recognised, but how to effectively support small business managers in their knowledge acquisition and decision making with emerging information and communication technologies is still a challenge. This paper describes an application attempt to develop an intelligent web-based training system for SMEs to aid their learning and decision-making regarding the use of the Internet as a new marketing medium. Based on the training needs analysis carried out by a EU-funded research project TRIMAR, a case-based reasoning (CBR) approach is adopted as an effective tool to help small business managers to learn and to make better decisions. Over seventy small business Internet marketing cases were collected and processed. They form a case base for CBR in the system. The initial feedback from users proves using case base reasoning approach is effective, but the quality and the comprehensiveness of case base are key factors for the usefulness of the system.

This paper offers a qualitative study of the applications of ICT in supporting knowledge transfer for the enhancement of modern agricultural extension. To address the challenge, this paper applies an inter-disciplinary approaches to review, analysis and develop a knowledge transfer model, and to conducts an empirical verification of it. A framework is proposed to illuminate the knowledge transfer process, and its associated channels.