Testing an integrated destination image model across residents and tourists

Tourism research has yet to confirm whether an integrated destination image model is applicable in predicting the overall destination image and behavioral intentions of local residents. This study examines whether the cognitive, affective and overall image - hypothesized to be predictors of behavioral intentions - are applicable to residents and tourists in the resort city of Eilat. The proposed model allowed for the distinct effect of each image component on overall image and behavior to be closely examined. The findings support the applicability of the model to local residents and also showed that among tourists, the affective component exerted a greater influence than the cognitive on overall destination image and future behavior. These findings have theoretical and practical implications for research on destination image

Hosts\u27 Motivations for Participation, Problems and Challenges in Homestay Tourism-Shompole Maasai Kenya

The Tourism sector is a key driver for socio - economic growth in most rural communities in Africa. In Kenya, tourism as an industry is the second highest foreign exchange earner after agriculture and accounts for 10 percent Gross Domestic Product (GDP). Homestay Tourism is a vital subsector of the Kenyan Tourism and has been promoted to diversify its products base, provide availability of beds or accommodation in rural areas, to empower the local communities economically, and enhance the quality of life of local hosts. However, little empirical evidence exists to ascertain motivations that sway homestay owners in rural areas of Kenya to host foreign visitors in their homes. The focus of the study aims to explore and describe primary motivational factors for homestay providers to offer such services, problems, and challenges encountered during service delivery within Shompole - Maasai Community of Kenya. This qualitative study used semi-structured and open-ended questionnaires for face to face interviews with 27 respondents in three out of five villages in Shompole Group Ranch. Findings of the study will assist the County and National governments, homestay providers, tourism planners and stakeholders in developing quality homestay products, marketing and ensure compliance with set guidelines for all players in the sector

Is any awareness necessary for an Ne?

The Error-Related Negativity (Ne or ERN) is a reliable electrophysiological index of error processing, which has been found to be independent of whether a subject is aware of an error or not. A large Ne was equally seen after errors that were consciously detected (Aware errors) and those that were not (Unaware errors), compared to a small negativity for correct responses (CRN). This suggests a dissociation between an automatic, preconscious error processing mechanism and subjective evaluation. A common concern regarding this finding is that subjects could have been somewhat aware of their errors, but did not report them due to lack of confidence. Here we tested this possibility directly using a betting paradigm which allowed us to separate occasions in which the subjects were confident of their response and trials in which they were unsure. In a choice reaction time task, subjects directly judged the accuracy of each response (correct or error) and then bet on this judgment using a high, medium, or low amount of money. The bets were used to determine the level of confidence the subjects had of their response. The average across all subjects regardless of confidence (betting) measure replicated the reported finding of an equal Ne for Aware and Unaware errors which was larger than the CRN. However, when Ne measurement was confined to high confidence (high bet) trials in confident subjects, a prominent Ne was seen only for Aware errors, while confident Unaware errors (i.e., error trials on which subjects made high bets that they were correct) elicited a response that did not differ from the CRN elicited by truly correct answers. In contrast, for low confidence trials in unconfident subjects, an intermediate and equal Ne/CRN was elicited by CRN, Aware and Unaware errors. These results provide direct evidence that the Ne is related to error awareness, and suggest the amplitude of the Ne/CRN depends on individual differences in error reporting and confidence

Motivated Interpretations of Deceptive Information

We examine whether people seek information that might help them make sense of others’ dishonest behavior. Participants were told that a hypothetical partner (either a friend or a stranger) had engaged in a task in which the partner could lie to boost their earnings at the expense of the participant’s earnings. Participants were less likely to search for information that can justify potential dishonest behavior conducted by a friend than by a stranger (Experiment 1). When participants knew for certain that their partners had lied to them, they were less likely to assume that that the lie was justified when told that the partner was a friend rather than a stranger (Experiment 2). The results imply that people are more likely to search for information that may reduce the severity of possible dishonest behavior when a stranger, rather than a friend, is responsible for the behavior

Analisa Strategi Pada Perusahaan Benih Tanaman Berbasis Metode Performance Prism Dan Swot (Studi Kasus: PT. X)

Pengukuran kinerja merupakan suatu metode atau alat yang digunakan untuk mencatat dan menilai pencapaian pelaksanaan kegiatan berdasarkan meningkatkan kualitas pengambilan keputusan dan akuntabilitas. Terdapat 4 elemen pokok pengukuran kinerja antara lain, menetapkan tujuan, sasaran, dan strategi organisasi, merumuskan indikator dan ukuran kinerja, mengukur tingkat ketercapaian tujuan dan sasaran organisasi, evaluasi kinerja. Sedangkan, untuk merumuskan strategi menggunakan analisis SWOT yang merupakan gambaran secara jelas bagaimana peluang dan ancaman eksternal Perusahaan yang disesuaikan dengan kekuatan dan kelemahan internal Perusahaan. Berkaitan dengan pencapaian target penjualan, target penjualan berbagai jenis benih sebagian besar tidak tercapai dimana kondisi ini terjadi pada tahun 2012. Maka dari itu dibutuhkan metode yang dapat diterapkan dalam pengukuran tersebut untuk mengetahui sejauh mana performansi kinerja Perusahaan telah tercapai sehingga dapat merumuskan strategi unggulan yang tepat dan berdaya saing. Hasil penelitian dengan metode Performance Prism didapatkan nilai indeks sebesar 8.60 yang masuk kedalam kategori hijau, sehingga dinyatakan kinerja Perusahaan secara keseluruhan telah memenuhi performa Perusahaan yang diharapkan. Pada analisis SWOT telah didapatkan 5 strategi baru yaitu memperluas pangsa pasar, menjaga hubungan baik dengan supplier, memberikan reward, peningkatan daya tahan benih, dan menjaga hubungan baik dengan koperasi

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Hidden Markov Models and their Application for Predicting Failure Events

We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.Comment: Will be published in the proceedings of ICCS 2020; @Booklet{EasyChair:3183, author = {Paul Hofmann and Zaid Tashman}, title = {Hidden Markov Models and their Application for Predicting Failure Events}, howpublished = {EasyChair Preprint no. 3183}, year = {EasyChair, 2020}