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The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation.

The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme.

A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced.

Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research.

Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically.

This research explores response to personalization from a customer’s perspective.

Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues.

The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed exploratory and confirmatory factor analyses to assess the empirical validity of the model.

The proposed sequence of psychological outcomes is valid, with the exception of one hypothesized relationship. In particular, satisfaction with an online store’s recommender has a strong influence on a consumer’s willingness to purchase one of the items related to his/her shopping goal. However, this satisfaction has no direct effect on a consumer’s intention to make add-on purchases based on the recommender’s suggestions. On the contrary, the results support the idea that add-on purchases are conditioned by a previous purchase related to the consumer’s initial shopping goal. On the other hand, a consumer’s flow state while shopping improves all his/her psychological outcomes linked to an online store’s recommender. The influence of flow state is particularly interesting when seeking to gain a better understanding of consumers’ unplanned purchases based on the recommender’s suggestions. These findings have important implications for practitioners.

This paper discusses in detail and empirically test a set of psychological outcomes that emerge when an e-vendor’s recommender is used to assist a consumer’s shopping process. To the best of the knowledge, this is the first attempt that empirically tests most of the hypothesized relationships within an online store’s RS context.

This paper attempts to identify key factors (i.e., personalization, privacy awareness and social norms) that affect user experiences (UXs) of mobile recommendation systems according to the user involvement theory (push-based and pull-based) and their relationships.

The study is based on an online survey with students from an international business school located in southwestern China. The sample population for the study included randomly selected 600 university students who are active mobile phone users. A total of 470 questionnaires were returned; 456 were valid (14 were invalid due to the incompleteness of their responses), providing a response rate of 65%.

Social norms have the largest impact on user experience quality, followed by personalization and privacy awareness. User involvement in mobile recommendation systems has mediating effects on the above relationships, with larger effects on pull-based systems than on push-based systems.

This study provides an integrated framework for researchers to measure the effects of social, personal and risk factors on the quality of user experience. The results enrich the literature on user involvement, mobile recommendation systems and UX. The findings provide significant implications for both retailers and developers of mobile recommendation systems.

The purpose of this paper is to study the use of online recommendation systems on e‐commerce sites is which becoming more common as marketers recognize their potential to improve their own operations as well as consumers' shopping experiences. Since some consumers question the credibility of these systems, this study compares responses to such systems (classified based on their source into seller and third party systems) with responses to recommendations coming directly from other consumers. The latter may also be better suited for consumers today since many of them utilize direct information from social media on a daily basis. Past research indicates that reactions to such recommendations may depend on the types of goods they describe and therefore this study also tests whether consumer responses vary with types of goods. The study examines consumer reactions to recommendations designed for search, experience, and credence goods. Finally, this study also explores the most desired features of recommendations to help marketers come up with the most effective recommendations that help facilitate purchasing decisions.

The study surveys a convenience sample of 202 undergraduate students to test these objectives. It was a 3 (product types) by 3 (recommendation types) factorial design with multiple dependent variables and three covariates.

The study reveals that, irrespective of the product type, consumers react differently to the three types of recommendations that are tested. This study shows that consumers have the most positive attitudes and most frequently utilize recommendations coming directly from other consumer. This suggests that more attention should be directed to these recommendations in marketing theory and practice. Consumers also hold more positive attitudes towards third‐party recommendation systems than recommendation systems coming from the seller. They also have more positive reactions toward recommendations designed for search and experience goods rather than credence products. Finally, the study also examines the usefulness of different characteristics of these recommendations to help online managers develop most effective recommendations online and finds that it varies with different types of recommendations and products for which recommendations are used.

In addition to the recommendation systems that have been explored in the past (seller and third party systems), the study examines reactions to recommendations coming directly from other consumers, as these recommendations may be better suited for today's audiences. The study shows which recommendation type is best received and most frequently used online. It also tests reactions to recommendations designed for different types of goods. This study includes credence goods, in addition to search and experience products, since consumer reactions to recommendations designed for credence goods have not been yet explored in the past research. It also found that recommendations are better received for goods with a higher number of search features. Finally, the study explores the specific features of different recommendation types and based on the findings proposes how these online recommendations should be structured to be most effective.

A meta-evaluation is an assessment of evaluation practices. Meta-evaluations include assessments of validity and usefulness of two or more studies that focus on the same issues. Every performance audit is grounded explicitly or implicitly in one or more theories of program evaluation. A deep understanding of alternative theories of program evaluation is helpful to gain clarity about sound auditing practices. We present a review of several theories of program evaluation.

This study includes a meta-evaluation of seven government audits on the efficiency and effectiveness of tourism departments and programs. The seven tourism-marketing performance audits are program evaluations for: Missouri, North Carolina, Tennessee, Minnesota, Australia, and two for Hawaii. The majority of these audits are negative performance assessments. Similarly, although these audits are more useful than none at all, the central conclusion of the meta-evaluation is that most of these audit reports are inadequate assessments. These audits are too limited in the issues examined; not sufficiently grounded in relevant evaluation theory and practice; and fail to include recommendations, that if implemented, would result in substantial increases in performance.

The purpose of this paper is to identify and examine the concerns of administrative and clerical employees towards a web‐based business system and associated training which were not identified either before or during an enterprise resource planning (ERP) implementation. Post‐implementation analyses revealed that while an implementation can be deemed a success immediately following go‐live dates, long‐term planning is essential to maintain change management continuity for administrators and employees.

The stages of concern component of the concerns‐based adoption model offered a method of analysis of the Dallas, Texas, Independent School District's employees to identify the perceptions and levels of acceptance of the users in regards to the implementation of an ERP system in a public school district.

The findings for the research questions assisted in interpreting and categorizing the responses to the open‐ended portion of the stages of concern questionnaire; and providing recommended guidelines for future ERP implementations in similar environments.

The paper shows how leaders in an organization must understand the employees' perceptions of the changes taking place in an ERP implementation and post‐implementation. Based on the findings, a summary, conclusion, and recommendations for further research are provided to assist K‐12 districts in planning for ERP implementations.

The significance of this study encompasses the impact of the integration of new technology with various associated people, processes, and systems. Understanding the impact of such potentially significant change by measuring a user community's overall perception and level of acceptance is a key component in providing guidance for future implementations in similar organizational and institutional environments.

This article draws upon our perspective as academic-practitioners working in the fields of food insecurity, food systems, and inequality to comment, in the early stages of the pandemic and associated lockdown, on the empirical and ethical implications of COVID-19 for socio-economic inequalities in access to food in the UK. The COVID-19 pandemic has sharpened the profound insecurity of large segments of the UK population, an insecurity itself the product of a decade of “austerity” policies. Increased unemployment, reduced hours, and enforced self-isolation for multiple vulnerable groups is likely to lead to an increase in UK food insecurity, exacerbating diet-related health inequalities. The social and economic crisis associated with the pandemic has exposed the fragility of the system of food charity which, at present, is a key response to growing poverty. A vulnerable food system, with just-in-time supply chains, has been challenged by stockpiling. Resultant food supply issues at food banks, alongside rapidly increasing demand and reduced volunteer numbers, has undermined many food charities, especially independent food banks. In the light of this analysis, we make a series of recommendations. We call for an immediate end to the five week wait for Universal Credit and cash grants for low income households. We ask central and local government to recognise that many food aid providers are already at capacity and unable to adopt additional responsibilities. The government's – significant – response to the economic crisis associated with COVID-19 has underscored a key principle: it is the government's responsibility to protect population health, to guarantee household incomes, and to safeguard the economy. Millions of households were in poverty before the pandemic, and millions more will be so unless the government continues to protect household incomes through policy change.

The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users.

We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations.

The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy.

To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

This paper aims to describe the results of applying ethical AI requirements to a healthcare use case. The purpose of this study is to investigate the effectiveness of using open educational resources for Trustworthy AI to provide recommendations to an AI solution within the healthcare domain.

This study utilizes the Hackathon method as its research methodology. Hackathons are short events where participants share a common goal. The purpose of this to determine the efficacy of the educational resources provided to the students. To achieve this objective, eight teams of students and faculty members participated in the Hackathon. The teams made suggestions for healthcare use case based on the knowledge acquired from educational resources. A research team based at the university hosting the Hackathon devised the use case. The healthcare research team participated in the Hackathon by presenting the use case and subsequently analysing and evaluating the utility of the outcomes.

The Hackathon produced a framework of proposed recommendations for the introduced healthcare use case, in accordance with the EU's requirements for Trustworthy AI.

The educational resources have been applied to one use-case.

This is the first time that open educational resources for Trustworthy AI have been utilized in higher education, making this a novel study. The university hosting the Hackathon has been the coordinator for the Trustworthy AI Hackathon (as partner to Trustworthy AI project).

This study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.

In this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.

The outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.

This study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.

This study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.

API recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.