Question: An empirical exploration of business factors in the adoption of business-to-business e-commerce.

In this paper, we delve into the intricate process of convergent and discriminant validation through the multitrait-multimethod (MTMM) matrix, a powerful tool for assessing the validity of constructs in business-to-business (B2B) e-commerce adoption studies. We focus on the analysis of various business factors to understand their impact on the adoption of B2B e-commerce. The MTMM matrix allows us to compare the consistency of measurement across different methods and traits, providing a comprehensive view of the validity of our constructs. For instance, when examining the correlation between B2B e-commerce adoption and business factors such as technology readiness, market competition, and organizational culture, we aim to ensure that the correlations within the same method (e.g., survey responses) are higher than those across different methods (e.g., survey vs. interview). This approach helps us validate the specificity and reliability of our findings. A significant challenge in B2B e-commerce research is the potential for halo effect, where the perception of one trait (e.g., technology readiness) can unduly influence the assessment of others (e.g., organizational culture). By employing the MTMM matrix, we can mitigate this effect and ensure that each trait is evaluated independently. However, previous studies have shown that the MTMM matrix can sometimes fail to meet the criteria for discriminant validity, indicating that the correlations between traits rated by the same method are not significantly higher than those between different traits rated by different methods. This highlights the need for careful consideration of the methods used in B2B e-commerce research. Despite these challenges, the MTMM matrix has proven useful in identifying specific constructs with high validity. For instance, in a study by [Author Name] on the leadership behavior of officers, only one variable (Recognition Behavior) met the criteria for discriminant validity, with a validity of 0.29. This suggests that while the MTMM matrix can be a powerful tool, it requires meticulous application to yield meaningful results. In another study by [Author Name] on attitudes toward authority figures, the matrix revealed that attitude toward father showed discriminant validity, whereas attitude toward boss did not. This underscores the importance of examining the validity of individual constructs rather than assuming a generalized attitude toward authority. Overall, the application of the MTMM matrix in B2B e-commerce adoption research provides a rigorous framework for validating constructs and identifying specific business factors that influence adoption. By carefully selecting methods and traits, and by analyzing the matrix for convergent and discriminant validity, researchers can ensure the reliability and validity

Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the Internet. The future of e-commerce is tenuous without a general climate of online trust. Building consumer trust on the Internet presents a challenge for online merchants and is a research topic of increasing interest and importance. This paper provides an overview of the nature and concepts of trust from multi-disciplinary perspectives, and it reviews relevant studies that investigate the elements of online trust. Also, a framework of trust-inducing interface design features articulated from the existing literature is presented. The design features were classified into four dimensions, namely (1) graphic design, (2) structure design, (3) content design, and (4) social-cue design. By applying the design features identified within this framework to e-commerce web site interfaces, online merchants might then anticipate fostering optimal levels of trust in their customers. 2003 Elsevier Ltd. All rights reserved.

The use of Big Data can hold considerable benefits for organizations, but there is as yet little research on the impacts of Big Data use on key variables such as competitive advantage. The antecedents to Big Data use aiming at competitive advantage are also poorly understood. Drawing from prior research on the ICT-competitiveness link, this research examined the relationship of organizational use of Big Data and competitive advantage – as well as certain hypothesized antecedents to this relationship. Data was collected from a nationwide survey in Japan which was addressed to Big Data users in private companies. The result shows that the competitiveness is influenced by the systematic and extensive use of Big Data in the organization, and top management understanding for Big Data use. Antecedents of systematic and extensive Big Data use were found to be the cross-functional activities of analysts, proactive attitude of users, secured data management, and human resources. Future research should strive to verify these respondent opinions using longitudinal approaches based on objective financial data.

This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to the phenomenon of physician adoption of electronic medical records (EMR) technology. UTAUT integrates eight theories of individual acceptance into one comprehensive model designed to assist in understanding what factors either enable or hinder technology adoption and use. As such, it provides a useful lens through which to view what is currently taking place in the healthcare industry regarding EMR adoption. This is mutually beneficial to both the healthcare and MIS communities, as UTAUT offers valuable practical insight to the healthcare industry in explaining why EMR technology has not been more widely adopted as well as what prescriptions may facilitate future adoption, while offering the MIS community the opportunity to strengthen existing theory through an illustration of its application.

Websites provide the key interface for consumer use of the Internet. This research reports on a series of three studies that develop and validate Web site usability, design and performance metrics, including download delay, navigability, site content, interactivity, and responsiveness. The performance metric that was developed includes the subconstructs user satisfaction, the likelihood of return, and the frequency of use. Data was collected in 1997, 1999, and 2000 from corporate Web sites via three methods, namely, a jury, third-party ratings, and a software agent. Significant associations betweenWeb site design elements and Web site performance indicate that the constructs demonstrate good nomological validity. Together, the three studies provide a set of measures with acceptable validity and reliability. The findings also suggest lack of significant common methods biases across the jury-collected data, third-party data, and agent-collected data. Results suggest that Web site success is a first-order construct. Moreover, Web site success is significantly associated with Web site download delay (speed of access and display rate within the Web site), navigation (organization, arrangement, layout, and sequencing), content (amount and variety of product information), interactivity (customization and interactivity), and responsiveness (feedback options and FAQs). (e-Commerce, Web Metrics, or Measurement;Web Site Usability;Design and Performance Constructs; Construct Validity; Nomological Validity)

0950-5849/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.infsof.2009.11.005 * Corresponding author. Tel.: +44 (0) 1782 734090; E-mail address: m.turner@cs.keele.ac.uk (M. Turne Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated. 2009 Elsevier B.V. All rights reserved.

We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective. © 2013 Elsevier Ltd. All rights reserved.

As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users’ adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users’ complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users’ product adoption rate over time, where various factors that may influence users’ decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users’ preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users’ preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.

This paper deals with e-tourism, innovation and growth. The Internet is revolutionising the distribution of tourism information and sales. The Korean small and medium-sized tourism enterprises (SMTEs) with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This paper examines problems and solutions related to electronic commerce in the tourism industry and suggests recommendations for successful e-commerce strategies in tourism to be applied by the industry and the government in Korea. Introduction The definitions of tourism innovation (e.g. product, service and technological innovations) remains unclear, with the exception maybe of the Internet. New technologies can produce an essential contribution to tourism development. For tourism businesses, the Internet offers the potential to make information and booking facilities available to large numbers of tourists at relatively low costs. It also provides a tool for communication between tourism suppliers, intermediaries, as well as end-consumers. OECD (2000) revealed that the advent of Internet-based electronic commerce offers considerable opportunities for firms to expand their customer base, enter new product markets and rationalise their business. WTO (2001) also indicated that electronic business offers SMEs the opportunity to undertake their business in new and more cost-effective ways. According to WTO, the Internet is revolutionising the distribution of tourism information and sales. An increasing proportion of Internet users are buying on–line and tourism will gain a larger and larger share of the online commerce market. Obviously, the Internet is having a major impact as a source of information for tourism. However, the SMTEs are facing more stringent impediments to the adoption of new information technology, in particular, e-business. Part of the problem relates to the scale and affordability of information technology, as well as the facility of implementation within rapidly growing and changing organisations. In addition, new solutions configured for large, stable, and internationally-oriented firms do not fit well for small, dynamic, and locally-based tourism firms. Despite these challenges, SMTEs with well-developed and innovative Web sites can now have “equal Internet access” to international tourism markets. This implies equal access to telecom infrastructure, as well as to marketing management and education. According to a UN report (2001), “it is not the cost of being there, on the on-line market place, which must be reckoned with, but the cost of not being there.” It is certain that embracing digital communication and information technology is no longer an option, but a necessity. Thus, one of the most important characteristics of

E-commerce is undergoing an evolution through the adoption of Web 2.0 capabilities to enhance customer participation and achieve greater economic value. This new phenomenon is commonly referred to as social commerce, however it has not yet been fully understood. In addition to the lack of a stable and agreed-upon definition, there is little research on social commerce and no significant research dedicated to the design of social commerce platforms. This study offers literature review to explain the concept of social commerce, tracks its nascent state-of-the-art, and discusses relevant design features as they relate to e-commerce and Web 2.0. We propose a new model and a set of principles for guiding social commerce design. We also apply the model and guidelines to two leading social commerce platforms, Amazon and Starbucks on Facebook. The findings indicate that, for any social commerce website, it is critical to achieve a minimum set of social commerce design features. These design features must cover all the layers of the proposed model, including the individual, conversation, community and commerce levels. 2012 Elsevier B.V. All rights reserved.

By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between etrust and e-marketing effectiveness. 2013 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +98 5118811240; fax: +98 5118811243. E-mail addresses: r-nia@ferdowsi.um.ac.ir, frahimnia@hotmail.com (F. Rahimnia).


Question: An empirical exploration of business factors influencing the adoption of business-to-business e-commerce.

This study focuses on the issue of identifying low-quality product reviews within the context of online shopping platforms, where review quality can significantly fluctuate. We identify and address three primary biases present in current evaluation standards for product reviews. To accurately gauge the quality of reviews, we establish a detailed set of criteria for review assessment. Building on this foundation, we introduce a classification-based method designed to pinpoint low-quality reviews. Our approach is integrated into a two-stage framework aimed at enhancing the process of opinion summarization. Through comprehensive experimentation, we demonstrate that our proposed method not only effectively distinguishes between low-quality and high-quality reviews but also significantly improves the task of opinion summarization by accurately detecting and filtering out low-quality reviews, thereby refining the overall review quality on these platforms.

Availability of information, agility of business process, interoperability of diverse business functions and conformity of the legal requirements are few of the critical success factors for enterprises to survive in the modern and competing global market space. Enterprises while striving to achieve these critical success factors are further exposed to the complexity of massive volumes of variant data and information, which exists in a broad array of formats. Complex and extended business processes spanning across the business functions and partners around the globe, need for integration and interoperability, fulfillment of compliance to legal and regulatory requirements. Enterprise content management (ECM) answers the above questions and offers the capabilities to efficiently and effectively manage the challenges imposed by the demanding business requirements of the modern world. The study in-hand reviews and analyzes the existing literature on enterprise content management domain. In order to conduct a structured literature review, three broader areas of considerations for ECM are identified i.e. business needs & drivers, challenges and recommendations. The review inhand aims at consolidating valuable domain knowledge and research work accomplished in the recent years for the benefits of the industry and academia.

The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.

Businesses have invested considerable resources in the usage of enterprise resource planning (ERP) and business intelligence (BI) systems. These systems were heavily studied in developed countries while there are a few and narrowly focused studies in developing ones. However, studies on the integration of ERP and BI have not been given enough attention (hereafter ERPBI). There are many challenges facing the ERPBI usage in term of the steadily increasing speed with which new technologies are evolving. In addition, there are a number of factors that affecting this usage. Based on the finding of the literature, a model from a critical success factors (CSFs) perspective that examine the relationship between ERPBI usage and organisational performance is proposed. The conceptual model provides a foundation for more research in the future. The expected results of the study will improve the business outcome and help design strategies based on an investigation between ERPBI usage and organisational performance.

Knowledge Management (KM) provides a systematic process to help in the creation, transfer and use of knowledge across the university, leading to increased productivity. While KM has been successfully used elsewhere, universities have been late in adopting it. Before a university can initiate KM, it needs to determine if it is ready for KM or not. Through a web-based survey sent to 1263 faculty members from 59 accredited Library and Information Science programs in universities across North America, this study investigated the e®ect of individual factors of trust, knowledge self-e±cacy, collegiality, openness to change and reciprocity on individual readiness to participate in a KM initiative, and the degree to which this a®ects perceived organisational readiness to adopt KM. 157 valid responses were received. Using structural equation modeling, the study found that apart from trust, all other factors positively a®ected individual readiness, which was found to a®ect organisational readiness. Findings should help universities identify opportunities and barriers before they can adopt KM. It should be a useful contribution to the KM literature, especially in the university context.

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.

In this paper, we examine the importance of year, industry, corporate-parent, and businessspecific effects on the profitability of U.S. public corporations within specific 4-digit SIC categories. Our results indicate that year, industry, corporate-parent, and business-specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate-parent and industry effects. A detailed analysis suggests that industry, corporate-parent, and business-specific effects are related in complex ways.  1997 by John Wiley & Sons, Ltd.

An efficient business system integration (BSI) is the key determinant factor for the organization to stay competitive. The goal of this study is to develop and execute a research project to study BSI in the oil & gas industry and how an efficient BSI in supply chain (SCI) can be used to gain advantage. A mixed (qualitative & quantitative) survey method was employed and structural equation modeling (SEM) is applied to test the theoretical framework and hypotheses. Findings reveal that, total integration do not offer optimum performance and selective disintegration (DIS) is necessary for supply chain efficiency (SCE) and organization performance (PER). This study helps to better understand the degree of integration required for optimum supply chain performance and came up with new construct DIS. Specifically, this study investigated the effect of SCI and DIS on SCE and PER. The compelled sample size precludes evaluating and testing of additional broad models of the relationship amongst the constructs. The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, selective disintegration is necessary for optimum supply chain efficiency.

The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP

Internet self-efficacy (ISE), or the beliefs in one’s capabilities to organize and execute courses of Internet actions required to produce given attainments, is a potentially important factor to explain the consumers’ decisions in e-commerce use, such as eservice. In this study, we introduce two types of ISE (i.e., general Internet self-efficacy and Web-specific self-efficacy) as new factors that reflect the user’s behavioral control beliefs in e-service acceptance. Using these two constructs as behavioral control factors, we extend and empirically validate the Theory of Planned Behavior (TPB) for the World Wide Web (WWW) context. D 2003 Elsevier B.V. All rights reserved.

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.05.022 * Corresponding author. Tel.: +34 922317075. E-mail addresses: eparra@ull.es (E. Parra-López), Bulchand-Gidumal), dgtano@ull.es (D. Gutiérrez-Ta Armas). This work proposes a theoretical model to explain the factors determining the intentions to use social media when organizing and taking vacation trips. Understanding the antecedents of the tourists’ use of these technologies is considered to be crucial for organization managers and destination policy makers. This use of social media technologies determines which elements of the trip might be used by the tourist thus having a great impact on the market. The model and its hypotheses have been tested by means of an approach based on structural equations with the PLS technique. The study was conducted on a sample of 404 individuals who normally use the Internet and had traveled on vacation in the previous 12 months. The conclusions of the study reveal that the intentions to use social media are directly influenced by the perceived benefits of that use (functional, psychological and hedonic and social); however, the costs do not significantly affect the predisposition to use such technologies. It is also shown that there is a series of incentives such as altruism, availability, individual predisposition or trust in the contributions of others which facilitate and promote the use of this type of technology when organizing and taking tourist trips. 2010 Elsevier Ltd. All rights reserved.


Question: An empirical exploration of business factors influencing the adoption of business-to-business e-commerce.

This paper explores the intricacies of information overload within online interaction platforms, proposing a theoretical framework and conducting an empirical study to understand the dynamics of messages in these digital spaces. Recognizing that IEEE does not hold copyright for certain articles, it is noted that full-text access to these specific articles is not provided on the IEEE Xplore database. The study delves into the mechanisms that govern the flow of information and the interactions among users in online environments, highlighting the factors that contribute to information overload and the strategies employed by individuals to manage this deluge of data. Through a comprehensive empirical investigation, the paper aims to shed light on the business factors that influence the adoption of business-to-business e-commerce, offering insights into how organizations can navigate the challenges posed by information saturation to successfully implement and benefit from digital commerce platforms.

Please note that the original document does not directly relate to the question about business-to-business e-commerce adoption. However, the suggested similar document has been crafted to maintain a connection to the question while preserving the length, complexity, and semantic style of the original document.

The Ease of Doing Business Index (EDBI) uses 41 variables to compare the business environment of different countries. It is widely used by policy makers, researchers and multinational companies. This paper aims to assess EDBI’s consistency and validity in representing the business environment by using factor analysis. It is found that the EDBI presents a limited consistency and descriptive power of a country’s business environment. The consequence of these findings is that multinational firms should handle carefully the EDBI in their investment decisions. GEE The Ease of Doing Business Index as a tool for Investment location decisions – João Zambujal-Oliveira, Ricardo Pinheiro-Alves

In the current competitive global market, organizations are implementing information and communication technology (ICT) that could add value to their products, processes, and satisfaction of their users. The adoption, implementation and use of homegrown enterprise resource planning (ERP) systems is one of these mechanisms being globally used for recording, processing, storing, and exchanging organizational information anytime anywhere. Although organizations have been utilizing ERP systems, the acceptance of homegrown ERP systems is given less attention as compared to commercial off-the shelf (COTS) software. Hence, this research studied factors that determine acceptance of homegrown ERP through the extension of unified theory of acceptance and use of technology (UTAUT) model. The finding revealed that performance expectancy, effort expectancy, social influence, competitive advantage, cost effectiveness, and facilitations functions are determinants of homegrown ERP system acceptance in Ethiopia. Moreover, experience and voluntariness are found to be significant moderators of the study.

E-commerce has become an important part of business. In South Korea, the market size of online shopping malls was 13,460 billion Korean Won in 2006, and this figure keeps growing. Thus, gaining loyal customers has become a rising concern. In this study, we adopted Lewicki and Bunker's three different types of trust, namely, calculus-based trust, knowledge-based trust, and identification-based trust, in order to investigate their hierarchical relationships in e-commerce and their impacts on customer satisfaction and loyalty. A total of 104 responses from university students were analyzed to test the proposed model and its hypotheses using PLS. The results showed that hierarchical relationships between different types of trust exist in the online environment, and among them, knowledge-based trust has the strongest impact on customer satisfaction. This finding implied that practitioners should focus on developing an appropriate online strategy in terms of how to build up trust-based relationships with online customers.

Consumers with online shopping experience often stick to some special websites. Does it mean that long-term relationships between consumers and the websites form? To solve the problem, this paper analyzed the belief and attitude factors influencing online consumer stickiness intention. Based on the relationships between belief-attitude-intention implied in TPB (Theory of Planned Behavior), according to Expectation-Confirmation theory and Commitment-trust theory, we developed a concept model on online consumer stickiness. Six research hypotheses derived from this model were empirically validated using a survey of online shoppers. SEM (Structure Equation Modeling) was as a data analysis method. The results suggest that online consumer stickiness intention is influenced by consumers’ commitment to the website (e-vendors) and their overall satisfaction to the transaction process, and commitment’s effect is stronger; in turn, both consumers’ commitment and overall satisfaction are influenced by consumers’ ongoing trust significantly; confirmation has a significant effect on overall satisfaction but has not the same effect on commitment. The findings show that the long-term relationships between consumers and special websites exactly exist during online repeat consumption.

Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation Xiaoyu Yu Bang Nguyen Yi Chen Article information: To cite this document: Xiaoyu Yu Bang Nguyen Yi Chen , (2016),"Internet of things capability and alliance: entrepreneurial orientation, market orientation and product and process innovation", Internet Research, Vol. 26 Iss 2 pp. Permanent link to this document: http://dx.doi.org/10.1108/IntR-10-2014-0265

Enterprise Resource Planning (ERP) systems are among the largest information systems (IS) investments made by firms, and the use of such systems is spreading globally. Many researchers have discussed their adoption and implementation, few have investigated the impact of external contextual factors on the success of such technologies in adopting firms. This study aims to fill this gap in research by examining the effects of three external contextual factors, i.e., industry type, industry climate, and national economic climate on ERP success assessment. We obtained data from Estonia and Finland and our analysis shows that industry and national economic climates have significant relationships with ERP success.

OBJECTIVE
This paper provides a review of EHR (electronic health record) implementations around the world and reports on findings including benefits and issues associated with EHR implementation.

MATERIALS AND METHODS
A systematic literature review was conducted from peer-reviewed scholarly journal publications from the last 10 years (2001-2011). The search was conducted using various publication collections including: Scopus, Embase, Informit, Medline, Proquest Health and Medical Complete. This paper reports on our analysis of previous empirical studies of EHR implementations. We analysed data based on an extension of DeLone and McLean's information system (IS) evaluation framework. The extended framework integrates DeLone and McLean's dimensions, including information quality, system quality, service quality, intention of use and usage, user satisfaction and net benefits, together with contingent dimensions, including systems development, implementation attributes and organisational aspects, as identified by Van der Meijden and colleagues.

RESULTS
A mix of evidence-based positive and negative impacts of EHR was found across different evaluation dimensions. In addition, a number of contingent factors were found to contribute to successful implementation of EHR.

LIMITATIONS
This review does not include white papers or industry surveys, non-English papers, or those published outside the review time period.

CONCLUSION
This review confirms the potential of this technology to aid patient care and clinical documentation; for example, in improved documentation quality, increased administration efficiency, as well as better quality, safety and coordination of care. Common negative impacts include changes to workflow and work disruption. Mixed observations were found on EHR quality, adoption and satisfaction. The review warns future implementers of EHR to carefully undertake the technology implementation exercise. The review also informs healthcare providers of contingent factors that potentially affect EHR development and implementation in an organisational setting. Our findings suggest a lack of socio-technical connectives between the clinician, the patient and the technology in developing and implementing EHR and future developments in patient-accessible EHR. In addition, a synthesis of DeLone and McLean's framework and Van der Meijden and colleagues' contingent factors has been found useful in comprehensively understanding and evaluating EHR implementations.

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.

The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP

The global nature of communication and shopping has as well redefined, seeing that it is the perfect vehicle for online shopping stores. Online convenient shop is mostly reflected in shorter time and less energy spent, including shipping cost reduction, less crowd and queues than real markets, unlimited time and space, which all increase convenience of shopping. Internet shopping for businesses and consumers are being accepted as an alternative shop mode rather than visiting the stores. However, convincing the consumers to shop online is still a challenging task for web retailers in Malaysia. The growth of Internet technology in Malaysia has enormous potential as it reduces the costs of product and service delivery and extends geographical boundaries in bringing buyers and sellers together. This study is conducted to identify factors influencing consumers towards online shopping in Malaysia. The study focused on nine independent variables namely appearance, quick loading, security, sitemap, validity, promotion, attractiveness, believability, and originality. We applied Five-point Likert Scale to measure the influential factors on intention for online shopping.


Question: An empirical study of business factors influencing the adoption of business-to-business e-commerce.

This study explores the significance and function of feedback text comments within online marketplaces, highlighting their role in fostering trust, enabling price premiums, and facilitating seller differentiation. Unlike traditional marketplaces, online platforms offer a unique feature where buyers can leave detailed comments about their transactions, which can significantly impact the reputation of sellers. These comments not only provide valuable insights into the quality of products and services but also serve as a crucial mechanism for building trust between buyers and sellers. Moreover, positive feedback comments can lead to higher price premiums for sellers, allowing them to differentiate themselves in a crowded marketplace. This research delves into the nuances of how feedback text comments contribute to these outcomes, offering empirical evidence on the importance of this feature in online commerce. By analyzing the content and impact of these comments, the study sheds light on how they can be leveraged to enhance the overall online shopping experience and improve seller performance. This investigation into the dynamics of feedback text comments offers practical implications for both sellers and marketplace operators looking to optimize their strategies in the digital commerce landscape.

Internet-based group-buying has become a new growth point for China's e-commerce. By studying B2C group-buying model, this paper has collected data of 20 major Chinese group-buying websites, studied factors that might influence Internet users' group-buying behavior, proposed a new model for evaluating the value of Internet enterprises, and provided a feasible reference model for the evaluation and performance of Internet enterprises.

II The adoption process of cryptocurrencies Management Summary Cryptocurrencies 1 are rapidly gaining more and more interest as a technology that is potentially groundbreaking and disruptive for the whole payments industry on a global scale. However the future of cryptocurrencies is very unclear as there are many different usage scenarios and different stakeholders have different needs. In order to be able to give a better future perspective and to determine possibilities for improvement of cryptocurrencies factors that influence adoption will have to be determined. To achieve this the following main research question was formulated: What are factors influencing the adoption of cryptocurrencies in different usage scenarios for different stakeholders? The way current payment systems work throughout the world varies widely. The added value of cryptocurrencies therefore also hugely differs per geographical area. In order to be able to give clear and concise conclusions this research is scoped towards looking at the European market and the Dutch market in particular. Based on the Diffusion of Innovations Theory, which defines characteristics necessary for an innovation in order to be successfully adopted, a conceptual model to describe the adoption of cryptocurrencies was formulated. This model was based on academic literature resulting from a literature review. The model was then validated by means of qualitative semi‐structured interviews with subject experts. To make sure that a balanced view was obtained stakeholders from a wide range of industries were interviewed: employees from the four biggest Dutch banks, the Dutch Central Bank, the three largest Dutch cryptocurrency exchanges, senior payments consultants, Payment Service Providers, a cryptography expert and the largest Dutch company that accepts cryptocurrency, Thuisbezorgd.nl. During the interviews barriers were identified that have to be overcome in order for cryptocurrencies to be adopted on a large scale. The three main pillars which are important for future mass adoption are: 1. Ease of use: at the moment there is a lack of user‐friendliness when using bitcoins. Sending and receiving bitcoin is still cumbersome and holding bitcoins is prone to many risks. Users need to be able to have more confidence in the safety of their funds. 2. Price stability: the current price volatility driven by speculation and a lack of liquidity makes that it is very risky for a user to keep his funds in cryptocurrency as the value varies wildly. This undermines the function of cryptocurrency as a store of value. 3. Governance: the current bitcoin …

Privacy is a necessary concern in electronic commerce. It is difficult, if not impossible, to complete a transaction without revealing some personal data – a shipping address, billing information, or product preference. Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened. Fortunately, there are technologies to help users protect their privacy. P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology. However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies. Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy. This paper presents both the findings from that study as well as their design implications.

By considering the problems that commercial saffron companies have faced in international markets, the aim of this study is to investigate the impact of website content, including informational and design dimensions, on the effectiveness of e-marketing and e-trust as mediator variables. These aspects are examined with reference to sales and marketing division managers in a sample of 100 commercial saffron corporations in the Khorasan province. The findings support the ideas that website content has an effect on e-marketing and e-trust and that e-trust plays a mediating role in the relationship between etrust and e-marketing effectiveness. 2013 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +98 5118811240; fax: +98 5118811243. E-mail addresses: r-nia@ferdowsi.um.ac.ir, frahimnia@hotmail.com (F. Rahimnia).

The primary questions addressed in this paper are the following: what are the factors that affect students‟ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students‟ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students‟ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students, who were using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students‟ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.

Previous efforts in electronic commerce (e-commerce) research in developing countries shows that there is an acute lack of theoretical frameworks and empirical evidence to understand how developing country firms realize electronic commerce benefits amidst their national constraints. This paper sets out to develop a theoretically abstracted but contextually grounded model of how developing country firms can orient their resources and realize these benefits amidst their national constraints. A review of e-commerce and strategy management literature to develop a resource – based model for e-commerce benefits was undertaken. The process-based model provides an understanding of how to identify, integrate, and reconfigure resources to achieve electronic commerce benefits; provides propositions that serves as theoretical platforms for future empirically grounded research on electronic commerce in developing country contexts and brings organizations closer to identifying and categorizing the strategic value of resources and the role managerial capabilities and intangible resources play in sustaining e-commerce benefits. Finally, our findings provides organizations the strategic options to address resources which have lost their value or have become less valuable to their strategic orientation in e-commerce adoption thereby serving as a starting point of examining e-commerce in developing countries through the theoretical lens of information systems and strategic management.

Lack of trust has been frequently cited to be one of the key factors that discourage customers from participating in e-commerce, while cultural differences affect the formation of trust. As such, this cross-cultural study aims at establishing a conceptual framework to investigate the primary antecedents of online trust and the mechanism that converts consumer trust into perceived value and purchase intention in online auction. Two elements of our model bear mention here. First, the authors divide the consumer trust into two facets, seller and intermediary trust, and intend to assess their reciprocal relationships. Second, the authors intend to examine the asymmetrical impact of the negative and positive trustworthiness attributes on trust, and the interrelationship among trust, perceived value and purchase intention across cultural groups.

What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.

IT infrastructure flexibility is now being viewed as an organizational core competency that is necessary for organizations to survive and prosper in rapidly-changing, competitive, business environments. Using data from 200 U.S. and Canadian companies, this study examines the impact of the four components of IT infrastructure flexibility (compatibility, connectivity, modularity, and IT personnel) on strategic IT-business alignment and the extent to which various applications are implemented within an organization. The “extent” of implementation refers to the the organization’s experience with the particular application and the degree to which the application is implemented and used throughout the organization. The findings from analysis of a structural model provide evidence that connectivity, modularity, and IT personnel (among other considerations that we discuss in the paper) make significant, positive impacts on strategic alignment and that all four components result in significant, positive impacts on the applications implementation. The study reinforces the importance of IT infrastructure flexibility to organizations as one source for sustainable competitive advantage.

Abstract Purpose – This paper aims to present electronic procurement benefits identified in four case companies from the information technology (IT), hi-tech sector. Design/methodology/approach – Multi-case study design was applied. The benefits reported in the companies were analysed and classified according to taxonomies from the information systems discipline. Finally, a new benefits classification was proposed. The framework was developed based on information systems literature. Findings – The research confirmed difficulties with benefits evaluation, as, apart from operational benefits, non-financial, intangible benefits at strategic level were also identified. Traditional evaluation methods are unable to capture all benefits categories, especially at strategic level. New taxonomy was created, which allows evaluation of the complex e-procurement impact. In the proposed taxonomy, e-procurement benefits are classified according to their level (operational, tactical, strategic), area of impact, applying scorecard dimensions (customer, process, financial, learning and growth). In addition the benefits characteristic is captured (tangible, intangible, financial and non-financial). Research limitations/implications – Research is based on four case studies only. Findings are specific to case companies and the environment in which they operate. The framework should be tested further in different contexts. Practical implications – The new taxonomy allows evaluation of the complex e-procurement impact, demonstrating that benefits achieved do not concern merely the financial impact. The framework can be applied to preparing new systems implementation as well as to evaluating existing systems. Originality/value – The paper applies information systems frameworks to the electronic procurement field, which allows one to look at e-procurement systems considering its complex impact. The framework can also be used to evaluate different systems, not simply e-procurement.


Question: An empirical study of business factors influencing the adoption of business-to-business e-commerce.

This paper introduces a Latent Semantic Indexing (LSI)-based method for multilingual document clustering, addressing the growing need for effective knowledge management in a globalized world where organizations often maintain repositories of documents in various languages. The traditional approach to knowledge management involves the creation of knowledge repositories to store, share, and reuse tacit knowledge within an organization. These repositories are typically filled with a multitude of formal knowledge elements, primarily in the form of documents. To enhance user navigation and accessibility, knowledge maps, which can be generated through document clustering, serve as a valuable tool. While numerous document clustering techniques have been explored, they predominantly focus on monolingual documents. The advent of globalization and the evolution of the Internet have necessitated the development of multilingual document clustering (MLDC) strategies to construct comprehensive organizational knowledge maps. In response to this need, the paper presents an LSI-based MLDC technique designed to produce knowledge maps from multilingual document collections. The empirical evaluation highlights the efficacy of the proposed method, demonstrating high cluster recall and precision, as well as a balanced performance between monolingual and cross-lingual clustering when applied to a multilingual document corpus. The results affirm the technique's potential to significantly enhance knowledge management practices in multilingual environments. © 2007 Elsevier B.V. All rights reserved.

The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed. # 2002 Elsevier Science B.V. All rights reserved.

Purpose: Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users. Design/methodology/approach: The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use. Findings: Perceived technological barriers negatively affect behavioural control, reflecting people’s assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users. Research limitations/implications: With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult. Practical implications: There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level. Originality/value: This research proves that models like TAM should not treat mobile services as a generic concept, but instead to specifically address individual mobile services. The research also demonstrates the unique value of combining objective usage measurements (reflecting actual behaviour) with traditional survey data in more comprehensively modelling service adoption. 2009 Elsevier Ltd. All rights reserved.

Hong Kong is an international financial center well known for its efficiency and its ability to adapt and keep up with the times. Recently, however, the Hong Kong banking industry has been losing competitive advantages in some areas, with the adoption of Internet Banking being one of them. Hong Kong banks have been slower than some other international banks in joining the e-commerce evolution, which first emerged in the United States in mid-90s. Financial institutions in the U.S. have introduced and promoted online banking to provide better customer services. Many property and stock investment firms in Hong Kong have also jumped on the bandwagon and adopted the Internet as a channel for providing better and more efficient services ABSTRACT

In this paper, we examine the importance of year, industry, corporate-parent, and businessspecific effects on the profitability of U.S. public corporations within specific 4-digit SIC categories. Our results indicate that year, industry, corporate-parent, and business-specific effects account for 2 percent, 19 percent, 4 percent, and 32 percent, respectively, of the aggregate variance in profitability. We also find that the importance of the effects differs substantially across broad economic sectors. Industry effects account for a smaller portion of profit variance in manufacturing but a larger portion in lodging/entertainment, services, wholesale/retail trade, and transportation. Across all sectors we find a negative covariance between corporate-parent and industry effects. A detailed analysis suggests that industry, corporate-parent, and business-specific effects are related in complex ways.  1997 by John Wiley & Sons, Ltd.

In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.

The variety of available digital tools, strategies and activities might confuse and disorient even an experienced marketer. This applies in particular to B2B companies, which are usually less flexible in uptaking of digital technology than B2C companies. B2B companies are lacking a framework that corresponds to the specifics of the B2B business, and which helps to evaluate a company’s capabilities and to choose an appropriate path. A B2B digital marketing maturity model helps to fill this gap. However, modern marketing offers no widely approved digital marketing maturity model, and thus, some marketing institutions provide their own tools. The purpose of this paper is building an optimized B2B digital marketing maturity model based on a SWOT (strengths, weaknesses, opportunities, and threats) analysis of existing models. The current study provides an analytical review of the existing digital marketing maturity models with open access. The results of the research are twofold. First, the provided SWOT analysis outlines the main advantages and disadvantages of existing models. Secondly, the strengths of existing digital marketing maturity models, helps to identify the main characteristics and the structure of an optimized B2B digital marketing maturity model. The research findings indicate that only one out of three analyzed models could be used as a separate tool. This study is among the first examining the use of maturity models in digital marketing. It helps businesses to choose between the existing digital marketing models, the most effective one. Moreover, it creates a base for future research on digital marketing maturity models. This study contributes to the emerging B2B digital marketing literature by providing a SWOT analysis of the existing digital marketing maturity models and suggesting a structure and main characteristics of an optimized B2B digital marketing maturity model. Keywords—B2B digital marketing strategy, digital marketing, digital marketing maturity model, SWOT analysis.

After more than a decade of comprehensive research work in the area of electronic government (e-government), no attempt has yet been made to undertake a systematic literature review on the costs, opportunities, benefits and risks that influence the implementation of e-government. This is particularly significant given the various related challenges that governments have faced over the years when implementing e-government initiatives. Hence, the aim of this paper is to undertake a comprehensive analysis of relevant literature addressing these issues using a systematic review of 132 studies identified from the Scopus online database and Google Scholar together with a manual review of relevant papers from journals dedicated to electronic government research such as Electronic Government, an International Journal (EGIJ), International Journal of Electronic Government Research (IJEGR) and Transforming Government: People, Process, and Policy (TGPPP). The overall review indicated that although a large number of papers discuss costs, opportunities, benefits and risks, treatment of these issues have tended to be superficial.Moreover, there is a lack of empirical studies which can statistically evaluate the performance of these constructs in relation to the various egovernment systems. Therefore, this research would help governments to better analyse the impact of costs, opportunities, benefits and risks on the success of e-government systems and its pre-adoption from an implementation perspective.

Purpose – Little empirical research has been conducted on competitive intelligence (CI). This paper aims to contribute to the quantitative strand of the CI literature by exploring and validating the theoretical constructs of the CI process. Design/methodology/approach – Data from 601 questionnaires filled out by South African and Flemish exporters were subjected to exploratory factor analysis and construct equivalence analysis between the sub-samples. Findings – The results showed that the CI process consists of three constructs, while the context in which CI takes place consists of four constructs. This agrees to some extent with the literature. When verifying the constructs for both cultures it was found that all but one CI context construct can be viewed as equivalent in both groups. Bias analysis identified one item in the questionnaire that was biased. Via regression analysis it was also indicated that the context in which CI takes place influences the CI process to a large extent. The research identified size as an important influencing factor in a business’ CI process. Practical implications – Businesses involved in CI should take note that an improvement in their formal infrastructure, employee involvement and internal information processes could enhance their CI capability. Originality/value – This paper contributes towards the formalising of the constructs of competitive intelligence.

In this study, we conducted extensive reviews of online shopping literatures and proposed a hierarchy model of online shopping behavior. We collected 47 studies and classified them by variables used. Some critical points were found that research framework, methodology, and lack of cross-cultural comparison, etc So we developed a cross-cultural model of online shopping including shopping value, attitudes to online retailer's attributes and online purchasing based on the integrated V-A-B model.


Question: Learning deterministic decision trees in an end-to-end manner.

This paper explores the intersection between two leading classification techniques: decision forests (DFs, encompassing decision jungles) and convolutional neural networks (CNNs), aiming to merge their distinct advantages. Decision forests are lauded for their computational efficiency, attributed to their conditional computation feature, which limits processing to a confined area of the tree, specifically the nodes along a single branch. Conversely, CNNs are celebrated for their accuracy, primarily due to their superior representation learning capabilities. We propose a systematic methodology for integrating conditional computation with representation learning, thereby creating a spectrum of hybrid models that offer varying degrees of accuracy versus efficiency. We term this novel family of models conditional networks. These networks can be conceptualized as either decision trees enhanced with data transformation functionalities or as CNNs with block-diagonal sparse weight matrices and explicit data routing mechanisms. The effectiveness of these hybrid models is empirically assessed using the standard task of image classification on the CIFAR and Imagenet datasets. Our findings reveal that, in comparison to leading CNNs, our hybrid models achieve comparable accuracy with significantly reduced computational expense and a markedly smaller number of parameters.

Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.

We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any unobservable assumptions (no assumption of independence, small mixing time, fully observable states, or even hidden states) and the resulting statement is independent of the number of states or actions. The statement is critically dependent on the size of the rewards and prediction performance of the created classifiers.We also provide some general guidelines for obtaining good classification performance on the created subproblems. In particular, we discuss possible methods for generating training examples for a classifier learning algorithm.

Recent efforts to combine Representation Learning with Formal Methods, commonly known as Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem. Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure, and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem. The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. TreeLSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).

Imitation learning of sequential, goal-directed behavior by standard supervised techniques is often difficult. We frame learning such behaviors as a maximum margin structured prediction problem over a space of policies. In this approach, we learn mappings from features to cost so an optimal policy in an MDP with these cost mimics the expert's behavior. Further, we demonstrate a simple, provably efficient approach to structured maximum margin learning, based on the subgradient method, that leverages existing fast algorithms for inference. Although the technique is general, it is particularly relevant in problems where A* and dynamic programming approaches make learning policies tractable in problems beyond the limitations of a QP formulation. We demonstrate our approach applied to route planning for outdoor mobile robots, where the behavior a designer wishes a planner to execute is often clear, while specifying cost functions that engender this behavior is a much more difficult task.

Recurrent neural network (RNN) models are widely used for processing sequential data governed by a latent tree structure. Previous work shows that RNN models (especially Long Short-Term Memory (LSTM) based models) could learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree-based models. This work proposes a new inductive bias Ordered Neurons, which enforces an order of updating frequencies between hidden state neurons. We show that the ordered neurons could explicitly integrate the latent tree structure into recurrent models. To this end, we propose a new RNN unit: ON-LSTM, which achieve good performances on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference1.

In this paper, a simple and efficient method is proposed using one of the basic reinforcement learning tool Q-learning. This method is to achieve the aim of mobile robot maples navigation with collision-free. Thus, an end-to-end navigation model is built, which uses lidar sensor massage as input and moving command of mobile robot as output, so as to simplify the process of environmental perception and decision making on action. Through simulation and experiments, its effectiveness is proved. After trained by this method, mobile robot can safely reach the navigation target in an unknown environment without any prior demonstration. In addition, an extensive quantitative and qualitative evaluation of this method is presented by the comparison with traditional path planning method based on foregone global environment map.

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel crossentropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.

Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data—which is not necessarily easily obtained—that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.


Question: End-to-end learning for constructing deterministic decision trees.

This paper explores the ensemble learning method known as Random Forests, which is composed of multiple tree predictors. Each tree in the forest is built using a random vector sampled independently and identically distributed for all trees. The generalization error of random forests is shown to converge almost surely to a limit as the number of trees increases. The performance of the forest is determined by the strength of the individual trees and the level of correlation between them. By randomly selecting features for splitting nodes, Random Forests achieve error rates that are competitive with Adaboost, while exhibiting greater robustness to noise. The paper also discusses how increasing the number of features used in splitting nodes affects error, strength, and correlation, which are monitored through internal estimates. Furthermore, these estimates are utilized to assess variable importance in the context of classification and regression tasks, demonstrating the versatility of Random Forests in various applications.

Research in the area of learning structural descriptions from examples is reviewed, giving primary attention to methods of learning characteristic descrip tions of single concepts. In particular, we examine methods for finding the maximally-specific conjunctive generalizations (MSC-generalizations) that cover all of the training examples of a given concept. Various important aspects of structural learning in general are examined, and several criteria for evaluating structural learning methods are presented. Briefly, these criteria include (i) ade quacy of the representation language, (ii) generalization rules employed, computational efficiency, and (iv) flexibility and extensibility. Selected learning methods developed by Buchanan, et al., Hayes-Roth, Vere, Winston, and the authors are analyzed according to these criteria. Finally, some goals are sug gested for future research.

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning. “Poor is the pupil who does not surpass his master.” – Leonardo da Vinci

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.

We present a new ranking algorithm that combines the strengt hs of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used inform ation retrieval measure. The algorithm is based on boosted regression trees , although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accu racy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly du ring boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search training rankers for markets for which only small amounts o f labeled data are available, given a ranker trained on much more data from a larger market.

I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in <italic>n</italic>, 1/ε, 1/δ, and 1/&sgr;, where <italic>n</italic> is the number of Boolean attributes, ε and δ are the usual accuracy and confidence parameters, and &sgr; indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply   the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs <italic>O</italic> (<italic>n</italic><supscrpt>2</supscrpt>ε<supscrpt>−2</supscrpt> ) iterations over <italic>O</italic>(<italic>n</italic><supscrpt>4</supscrpt>ε<supscrpt> −2</supscrpt>ln(<italic>n</italic>/(δε))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.

In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning conndential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with conndential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more eecient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using eecient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.

In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan. PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning. Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.

In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.

Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.

High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism. We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols. A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.