WEBMINING: ISSUES

Web is an assortment of between related records on at any rate one web workers while web mining proposes dispensing with basic data from web information bases. Web mining is one of the information mining regions where information tunneling procedures are utilized for eliminating data from the web workers. The web information wires site pages, web joins, objects on the web an extraordinary arrangement logs. Web mining is utilized to understand the client lead, assess a specific site page dependent on the data which is dealt with in web log records. Web mining is assessed by utilizing information mining frameworks, unequivocally depiction, grouping, and joining rules. It has some steady zones or applications, for example, Electric conversation, E-learning, E-government, E-plans, E-vote based system, Electric trade, security, awful execution appraisal and advanced library. Recovering the significant site page from the web accommodatingly and appropriately changes into an inconvenient undertaking since web is contained unstructured information, which passes on the gigantic extent of data and expansion the multifaceted thought of regulating data from various web master gatherings. The assortment of material winds up being tricky, concentrate, and channel or assess the basic information for the clients. In this paper, to have dissected the essential considerations of web mining, assembling, cycles and issues. In addition, this task comparatively isolated the web mining research inconveniences

Strategic Managerial Responses to Critical Service Events in Restaurants

Inappropriate managerial responses to critical service events (CSEs) in restaurants contribute to an increased rate of customer defection and restaurant failure. Some restaurant managers lack employee-training strategies that may enhance service recovery from CSEs. This case study explored what employee-training strategies participants deemed essential to enhance service recovery to CSEs. The population for this study was restaurant managers from a U.S. regional chain in South Carolina with at least 3 years of employee-training experience. Organizational learning theory was the conceptual framework for this study. Data collection included semistructured face-to-face interviews with restaurant managers and an exploration of company archival documents related to CSEs. Using Yin\u27s 5 step data analysis method (i.e. compiling, disassembling, reassembling, interpreting, and concluding), 3 major themes emerged: customer needs and requests, which included the importance of listening to customers and affirming their requests; employee actions and attitudes, which included opinions about the ideal employee demonstrating a great attitude when correcting mistakes; and training, which included multiple methods to improve employee performance through continuous training. Recommendations for action included how to instruct employees to listen, apologize, solve problems, and thank customers. Restaurant managers may apply these results to improve service quality and customer experiences. Social implications include strategies to create positive experiences for employees and customers enhancing community employment and business sustainability

Organizational improvisation: a consolidating review and framework

Organizational improvisation is increasingly recognized as a relevant area of management research. However, the cumulativeness of research on improvisation in organizations remains low. This paper organizes existing contributions on organizational improvisation within a new consolidating framework combining degrees (minor, bounded and structural) and levels (individual, interpersonal and organizational) of improvisation. The proposed degree/level framework allows for reviewing the existing literature on organizational improvisation in the management disciplines of strategy, organizational behaviour, organizational theory, innovation and marketing in a systematic manner. It also exposes potential areas for future research across management disciplines, research areas, organizational settings and industries, and beyond existing metaphors, most notably of jazz and improvisational theatre

New Approach for Market Intelligence Using Artificial and Computational Intelligence

Small and medium sized retailers are central to the private sector and a vital contributor to economic growth, but often they face enormous challenges in unleashing their full potential. Financial pitfalls, lack of adequate access to markets, and difficulties in exploiting technology have prevented them from achieving optimal productivity. Market Intelligence (MI) is the knowledge extracted from numerous internal and external data sources, aimed at providing a holistic view of the state of the market and influence marketing related decision-making processes in real-time. A related, burgeoning phenomenon and crucial topic in the field of marketing is Artificial Intelligence (AI) that entails fundamental changes to the skillssets marketers require. A vast amount of knowledge is stored in retailers’ point-of-sales databases. The format of this data often makes the knowledge they store hard to access and identify. As a powerful AI technique, Association Rules Mining helps to identify frequently associated patterns stored in large databases to predict customers’ shopping journeys. Consequently, the method has emerged as the key driver of cross-selling and upselling in the retail industry. At the core of this approach is the Market Basket Analysis that captures knowledge from heterogeneous customer shopping patterns and examines the effects of marketing initiatives. Apriori, that enumerates frequent itemsets purchased together (as market baskets), is the central algorithm in the analysis process. Problems occur, as Apriori lacks computational speed and has weaknesses in providing intelligent decision support. With the growth of simultaneous database scans, the computation cost increases and results in dramatically decreasing performance. Moreover, there are shortages in decision support, especially in the methods of finding rarely occurring events and identifying the brand trending popularity before it peaks. As the objective of this research is to find intelligent ways to assist small and medium sized retailers grow with MI strategy, we demonstrate the effects of AI, with algorithms in data preprocessing, market segmentation, and finding market trends. We show with a sales database of a small, local retailer how our Åbo algorithm increases mining performance and intelligence, as well as how it helps to extract valuable marketing insights to assess demand dynamics and product popularity trends. We also show how this results in commercial advantage and tangible return on investment. Additionally, an enhanced normal distribution method assists data pre-processing and helps to explore different types of potential anomalies.Små och medelstora detaljhandlare är centrala aktörer i den privata sektorn och bidrar starkt till den ekonomiska tillväxten, men de möter ofta enorma utmaningar i att uppnå sin fulla potential. Finansiella svårigheter, brist på marknadstillträde och svårigheter att utnyttja teknologi har ofta hindrat dem från att nå optimal produktivitet. Marknadsintelligens (MI) består av kunskap som samlats in från olika interna externa källor av data och som syftar till att erbjuda en helhetssyn av marknadsläget samt möjliggöra beslutsfattande i realtid. Ett relaterat och växande fenomen, samt ett viktigt tema inom marknadsföring är artificiell intelligens (AI) som ställer nya krav på marknadsförarnas färdigheter. Enorma mängder kunskap finns sparade i databaser av transaktioner samlade från detaljhandlarnas försäljningsplatser. Ändå är formatet på dessa data ofta sådant att det inte är lätt att tillgå och utnyttja kunskapen. Som AI-verktyg erbjuder affinitetsanalys en effektiv teknik för att identifiera upprepade mönster som statistiska associationer i data lagrade i stora försäljningsdatabaser. De hittade mönstren kan sedan utnyttjas som regler som förutser kundernas köpbeteende. I detaljhandel har affinitetsanalys blivit en nyckelfaktor bakom kors- och uppförsäljning. Som den centrala metoden i denna process fungerar marknadskorgsanalys som fångar upp kunskap från de heterogena köpbeteendena i data och hjälper till att utreda hur effektiva marknadsföringsplaner är. Apriori, som räknar upp de vanligt förekommande produktkombinationerna som köps tillsammans (marknadskorgen), är den centrala algoritmen i analysprocessen. Trots detta har Apriori brister som algoritm gällande låg beräkningshastighet och svag intelligens. När antalet parallella databassökningar stiger, ökar också beräkningskostnaden, vilket har negativa effekter på prestanda. Dessutom finns det brister i beslutstödet, speciellt gällande metoder att hitta sällan förekommande produktkombinationer, och i att identifiera ökande popularitet av varumärken från trenddata och utnyttja det innan det når sin höjdpunkt. Eftersom målet för denna forskning är att hjälpa små och medelstora detaljhandlare att växa med hjälp av MI-strategier, demonstreras effekter av AI med hjälp av algoritmer i förberedelsen av data, marknadssegmentering och trendanalys. Med hjälp av försäljningsdata från en liten, lokal detaljhandlare visar vi hur Åbo-algoritmen ökar prestanda och intelligens i datautvinningsprocessen och hjälper till att avslöja värdefulla insikter för marknadsföring, framför allt gällande dynamiken i efterfrågan och trender i populariteten av produkterna. Ytterligare visas hur detta resulterar i kommersiella fördelar och konkret avkastning på investering. Dessutom hjälper den utvidgade normalfördelningsmetoden i förberedelsen av data och med att hitta olika slags anomalier

A study of the experiential service design process at a luxury hotel

This thesis explores the process of designing experiential services at a luxury hotel. These processes were surfaced by means of a methodology that used the principles of jazz improvisation. Due to similarities between experiential service design and elements in jazz improvisation, representing experiential service design through the jazz improvisation metaphor leads to a new framework for exploring the process of experiential service design that is iterative in nature. A gap in the service design literature is that experiential service design is not operationalized in organizational improvisation, and one contribution from this study will be to fill that gap. This study contributes to the field of knowledge by exposing a new perspective on how experiential services can be better designed by adapting some of the design tools from this luxury hotel; a second contribution is a recommendation for how the improvisational lens works as an investigative tool to research experiential organizations. In the process, some new dimensions to understanding complexity are contributed. The research process utilized qualitative research methods. Frank Barrett (1998) identified seven characteristics of jazz improvisation which I have used as a heuristic device: 1) provocative competence (i.e., deliberately creating disruption); 2) embracing errors as learning sources; 3) minimal structures that allow for maximum flexibility; 4) distributed task (i.e., an ongoing give and take); 5) reliance on retrospective sensemaking (organizational members as bricoleurs, making use of whatever is at hand); 6) hanging out (connecting through communities of practice); and 7) alternating between soloing and supporting. This research is grounded in the body of literature regarding complexity, organizational improvisation, service design and experience design. The role of heterogeneous minimal structures that are fluid and optimize uncertainty is central to this investigation. Themes such as sensemaking and the role of story, meaning-making, organizational actors' use of tangible and intangible design skills, and embracing ambiguity in efforts to design experiential services are explored throughout the dissertation. The anticipatory nature of experiential service design is a principle outcome from the data that is incorporated into the new conceptual framework highlighting a "posture of service"

Managing post-merger corporate culture: A case study of two mergers in the United States transportation industry.

One company proactively sought to recognize and adopt the best cultural characteristics of both pre-merger partners. The other company chose to rapidly integrate two competitors with an expectation that the culture of the acquired organization would be assimilated into the culture of the new owner.The number and value of mergers and acquisitions involving a United States company continue to grow at record rates. The excitement about doing a merger or acquisition is driven by the anticipation of financial success due to reduced competition, operational synergies, and access to larger customer bases. The dark side of mergers and acquisitions, however, is that two-thirds of them either fail or under perform expectations. Although blame is often placed on financial considerations or unrealistic business plans, there has been a recent interest in how the human side of mergers and acquisitions may affect their ultimate success.The creation of these case studies has contributed to the body of knowledge by providing the rationale, results, and consequences that might be analogous to other organizations considering a post-merger culture change. The two mergers selected for this research represent the extreme ends of the change spectrum. The case studies were written based on 23 personal interviews with current and previous employees in a range of positions at both companies. The research also relies heavily on document examination, reference to published materials, and observations of the companies in their natural setting.Since no two mergers are alike, this case study research provides information that may be of value to those considering a merger or acquisition. Merger participants should take into account such factors as the workforce size, geographical distribution, strength of collective bargaining agreements, and tenure of employees when making post-merger culture change decisions of their own.In the second case, no underlying development strategy was used to guide the process. Operating problems attributable to the disregard of job skills developed within months. Many employees who resisted were given an exit opportunity causing a knowledge deficit in territories germane to the acquired company. Severe and costly service disruptions resulted which took years for the company to recover from.This dissertation examines and discusses---in case study format---the different approach the leadership of two organizations took to manage corporate culture in their transportation industry mergers.The second case studies the merger of two competitors that sought to build a larger end-to-end network. Using the Harrison and Stokes (1992) descriptors, the acquired company in this example had an achievement type culture while the acquiring company had and still has a role type culture. In this case the post-merger culture change (P. M. C. C.) methodology consisted simply of requiring that the acquired company adopt the rules and practices of the acquiring company.Using descriptors coined by Harrison and Stokes in 1992, the first case study examines a merger that featured the combination of a company with a power culture with a competitor that had a support culture. According to senior management, both cultures contributed to the financial success of the predecessor companies. With the approaching merger, however, the leadership team recognized that a clash of the different values, attitudes, and driving forces could be detrimental to the new company.One topic often disregarded when a merger is planned is how the corporate cultures of the two companies will react with each other when the companies are brought together. Since every organization has a unique culture, it is possible that the two cultures could clash and undermine the benefits of the merger by reducing productivity, disrupting operations, disturbing the supply chain, or alienating customers.The post-merger culture change (P.M.C.C.) at one company relied on the identification and adoption of best practices from both predecessor companies. That merger has been declared to be successful by senior management based on levels of employee satisfaction, profitability, and share price as indicators

Descriptive business process models at run-time

Today's competitive markets require organisations to react proactively to changes in their environment if financial and legal consequences are to be avoided. Since business processes are elementary parts of modern organisations they are also required to efficiently adapt to these changes in quick and flexible ways. This requirement demands a more dynamic handling of business processes, i.e. treating business processes as run-time artefacts rather than design-time artefacts. One general approach to address this problem is provided by the community of [email protected], which promotes methodologies concerned with self-adaptive systems where models reflect the system's current state at any point in time and allow immediate reasoning and adaptation mechanisms. However, in contrast to common self-adaptive systems the domain of business processes features two additional challenges: (i) a bigger than usual abstraction gap between the business process models and the actual run-time information of the enterprise system and (ii) the possibility of run-time deviations from the planned models. Developing an understanding of such processes is a crucial necessity in order to optimise business processes and dynamically adapt to changing demands. This thesis explores the potential of adopting and enhancing principles and mechanisms from the [email protected] domain to the business process domain for the purpose of run-time reasoning, i.e. investigating the potential role of Descriptive Business Process Models at Run-time (DBPMRTs) in the business process management domain. The DBPMRT is a model describing the enterprise system at run-time and thus enabling higher-level reasoning on the as-is state. Along with the specification of the DBPMRT, algorithms and an overall framework are proposed to establish and maintain a causal link from the enterprise system to the DBPMRT at run-time. Furthermore, it is shown that proactive higher-level reasoning on a DBPMRT in the form of performance prediction allows for more accurate results. By taking these steps the thesis addresses general challenges of business process management, e.g. dealing with frequently changing processes and shortening the business process life cycle. At the same time this thesis contributes to research in [email protected] by providing a complex real-world use case as well as a reference approach for dealing with volatile [email protected] of a higher abstraction level