Search results

1 – 10 of 358
Article
Publication date: 8 September 2020

Tipajin Thaipisutikul and Yi-Cheng Chen

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order…

Abstract

Purpose

Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.

Design/methodology/approach

The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users’ regularity–POIs’ popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.

Findings

The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.

Originality/value

This study contributes to the existing literature by incorporating a novel pattern–based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs’ popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.

Details

Industrial Management & Data Systems, vol. 120 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 28 August 2009

Sarah Kovoor‐Misra

The purpose of this paper is to provide a framework for predicting the role and effects of perceived organizational identity (POI) on organizational members' perceptions and…

5793

Abstract

Purpose

The purpose of this paper is to provide a framework for predicting the role and effects of perceived organizational identity (POI) on organizational members' perceptions and behaviors during crisis and change situations, and the scope of the resulting POI changes that may occur.

Design/methodology/approach

The paper brings together research on crisis, change, threat/opportunity, and POI, along with case study data to create a threat/opportunity framework for making these predictions.

Findings

Based on whether threat or opportunity is perceived during crisis and change situations, different aspects of individuals' POIs will become salient. In threat situations, individuals will focus on perceptions of “who we are.” In opportunity situations, individuals will also focus on “who we could be.” The focus of attention and the threat/opportunity context will influence organizational identification, learning, and openness to change; and whether incremental or transformational POI change occurs. The perception of “who we could be” will motivate more change than the ideal organizational identity or the image of “who we want to be” that is typically studied in the literature. The scope of POI change is also dependent on perceptions of identity cost and the identity gap.

Research limitations/implications

Future research can test the hypotheses suggested here in various crisis and change contexts. Also, differentiating between threat and opportunity contexts is important for understanding the role of POI, and the extent to which POI changes can occur in crisis and change situations. Studies of resistance to POI change could consider whether individuals perceived the identity cost and the identity gap as being too low. More research on POI in opportunity contexts could expand understanding of the POI image of “who we could be” in motivating POI change. Finally, further integration of the literature on crisis and change can benefit both fields.

Practical implications

Practitioners can predict which aspects of POI will become salient in threat and opportunity conditions, and manage their different effects. For individuals to learn and change their POIs during crisis and change situations, managers need to diminish heightened perceptions of threat and shift the focus of attention to “who we could be.” Top managers' claims of “who we could be” need to be perceived by organizational members as being desirable and attainable in order to be motivating. Finally, to create transformational POI change, executives need to highlight the identity cost of not changing, and the size of the identity gap.

Originality/value

The threat/opportunity framework enables new predictions of the role and effects of POI in crisis and change situations. The paper highlights the POI image of “who we could be,” defines incremental and transformational POI change, redefines the identity gap concept, and introduces the notion of identity cost to provide a framework for predicting the scope of POI change that has received limited research attention. Finally, the paper contributes to research on POI in opportunity‐oriented conditions, and integrates research on crisis and change.

Details

Journal of Organizational Change Management, vol. 22 no. 5
Type: Research Article
ISSN: 0953-4814

Keywords

Article
Publication date: 16 November 2023

Asma-Qamaliah Abdul-Hamid, Mohd Helmi Ali, Lokhman Hakim Osman, Ming-Lang Tseng and Ahmad Raflis Che Omar

This paper aims to contribute significantly to the empirical investigations on adopting Industry 4.0–circular economy in the Malaysian palm oil industry. The paper also aims to…

Abstract

Purpose

This paper aims to contribute significantly to the empirical investigations on adopting Industry 4.0–circular economy in the Malaysian palm oil industry. The paper also aims to theorise and empirically assess a comprehensive model incorporating three aspects and 51 criteria.

Design/methodology/approach

A two-stage methodology is proposed using the fuzzy Delphi method and the fuzzy-based analytical network process. Twenty-seven criteria on adoptability of industry 4.0–circular economy were selected for the first-stage methodology, followed by identifying each criteria's intersection with the overall objectives.

Findings

The findings indicate that financial constraints, the lack of a collaborative I4.0–CE model, laws and policy, low management support and the training of dedicated employers in I4.0–CE-application are the top five criteria requiring critical attention from the POI.

Practical implications

The overall sustainability advantages of the POI are identified and discussed in depth to establish criteria for industry 4.0–circular economy applications.

Originality/value

This study fills the previous research gap by theoretically explaining POI's industry 4.0 adoption–circular economy from the perspective of two underpinning theories. Due to the pressure towards sustainability, the industry must be ready to adopt industry 4.0–circular economy applications, and resources must be managed appropriately and effectively by sharing and integrating. Advanced industry 4.0 technologies and pragmatic practices such as a circular economy are needed to achieve optimal sustainable development while retaining commercial success.

Details

Industrial Management & Data Systems, vol. 124 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 18 August 2023

Mukaram Ali Khan, Jeetesh Kumar, Muhammad Haroon Shoukat and Kareem M. Selem

This paper aims to explore the role of perceived organizational injustice (POI) leading to workplace conflict in determining organizational performance (OP) among healthcare…

Abstract

Purpose

This paper aims to explore the role of perceived organizational injustice (POI) leading to workplace conflict in determining organizational performance (OP) among healthcare employees. This paper also examines the serial mediating effects of moral disengagement (MD) and knowledge hiding (KH).

Design/methodology/approach

In all, 244 public and private hospital employees in Pakistan provided the data set.

Findings

According to partial least squares-structural equation modeling findings, the negative association between POI and OP was serially mediated by KH and MD. The recovery process underlying the linkage between POI and OP is tested and highlighted in this paper as a first step in unraveling it.

Research limitations/implications

The findings highlight the significance of taking moral and KH models into account when attempting to understand the moral cognitive processes that employees go through when they see injustice. Organizations should guarantee the equitable distribution of incentives and resources, as distributive and procedural justices are concerned with organizations.

Originality/value

By directing actions meant to prevent MD and KH, the findings may potentially inspire new, more focused treatments to safeguard patient safety and avoid losses in the healthcare industry. One way to reduce unethical conduct and MD is to have people declare or agree to a code of ethics.

Details

International Journal of Conflict Management, vol. 35 no. 2
Type: Research Article
ISSN: 1044-4068

Keywords

Article
Publication date: 25 November 2013

Andrew Ennis, Liming Chen, Chris D. Nugent, George Ioannidis and Alexandru Stan

Improvements and portability of technologies and smart devices have enabled a rapid growth in the amount of user-generated media such as photographs and videos. Whilst various…

Abstract

Purpose

Improvements and portability of technologies and smart devices have enabled a rapid growth in the amount of user-generated media such as photographs and videos. Whilst various media generation and management systems exist, it still remains a challenge to discover the right information, for the right purpose. This paper aims to propose an approach to reverse geocoding by cross-referencing multiple geospatial data sources to enable the enrichment of media and therefore enable better organisation and searching of the media to create an overall picture about places.

Design/methodology/approach

The paper presents a system architecture that incorporates the proposed approach to aggregate several geospatial databases to enrich geo-tagged media with human readable information, which will further enable the goal of creating an overall picture about places. The approach enables the semantic information relating to point of interest.

Findings

Implementation of the proposed approach shows that a single geospatial data source does not contain enough information to accurately describe the high-level geospatial information for geocoded multimedia. However, fusing several geospatial data sources together enables richer, more accurate high-level geospatial information to be tagged to the geocoded multimedia.

Originality/value

The contribution in this paper shows that high-level geospatial information can be retrieved from many data sources and fused together to enrich geocoded multimedia which can facilitate better searching and retrieval of the multimedia.

Details

International Journal of Pervasive Computing and Communications, vol. 9 no. 4
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 25 February 2020

Wolfram Höpken, Marcel Müller, Matthias Fuchs and Maria Lexhagen

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of…

Abstract

Purpose

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

Design/methodology/approach

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

Findings

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

Research limitations/implications

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

Practical implications

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

Originality/value

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

摘要 研究目的

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点(POI)游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI

研究设计/方法/途径

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析(FP-growth参数)和序列样式勘探分析(GSP参数)以确认游客行为模式。

研究结果

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系(support:1%−4.6%;lift:1.4%−32.1%), 序列样式勘探分析确立了相关频率游览次序(support:0.6%−1.7%。

研究理论限制/意义

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

研究现实意义

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

研究原创性/价值

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

Article
Publication date: 1 August 2016

Jakub Trojan

The purpose of this paper is to propose the platform for effective transformation of points of interests (POIs) into augmented reality (AR), specifically into the three major…

Abstract

Purpose

The purpose of this paper is to propose the platform for effective transformation of points of interests (POIs) into augmented reality (AR), specifically into the three major software tools – Junaio, Layar and Wikitude. The objective is to facilitate the creation of POIs for common users of these programs and, thus, encourage the general public to participate in the formation of a new concept of applications using AR and location-based services.

Design/methodology/approach

The subject of this study was analysis of methods used for POI dynamisation under the context of location-based services. This paper suggests methodology based on database format transformation. It is focused on the creation of platform for automated geotagged POI transformation into AR.

Findings

The research results in prototype of online platform which is capable to automatically transform geotagged POI to three major AR applications. It discusses also the model implementation of this platform in Czech national tourist authority.

Research limitations/implications

The paper presents a proof-of-concept of dynamisation and transformation of an unspecified number of POIs stored in a simple table database and their transformation into the AR.

Practical implications

Services of AR are brought for the masses to effectively dynamise tourist information.

Social implications

Results could make the process of multimedialising data (POIs) more suitable for masses.

Originality/value

This paper presents a proof-of-concept of dynamisation and transformation of an unspecified number of POIs stored in a simple table database and their transfer into the three major AR applications.

Details

Journal of Hospitality and Tourism Technology, vol. 7 no. 3
Type: Research Article
ISSN: 1757-9880

Keywords

Article
Publication date: 4 April 2016

Jungkyu Han and Hayato Yamana

The purpose of this paper is to clarify the correlations between amount of individual’s knowledge of a specific area and his/her visit pattern to point of interest (POI

Abstract

Purpose

The purpose of this paper is to clarify the correlations between amount of individual’s knowledge of a specific area and his/her visit pattern to point of interest (POI, interested places) located in the area.

Design/methodology/approach

This paper proposes a visit-frequency-based familiarity estimation method that estimates individuals’ knowledge of areas in a quantitative manner. Based on the familiarity degree, individuals’ visit logs to POIs are divided into a set of groups followed by analyzing the differences among the groups from various points of view, such as user preference, POI categories/popularity, visit time/date and subsequent visits.

Findings

Existence of statistically significant correlations between individuals’ familiarity to areas and their visit patterns is observed by our analysis using 1.4-million POI visit logs collected from a popular location-based social network (LBSN), Foursquare. There exist different skewness of the visit time and visited POI distribution/popularity with regard to the familiarity. For instance, users go to unfamiliar areas on weekends and visit POIs for cultural experiences, such as museums. A notable point is that the correlations can be detected even in the areas in home city, which have not been known so far.

Originality/value

This is the first in-depth work that studies both estimation of individuals’ familiarity and correlations between the familiarity and individuals’ mobility patterns by analyzing massive LBSN data. The methodologies used and the findings of this work can be applicable not only to human mobility analysis for sociology, but also to POI recommendation system design.

Details

International Journal of Pervasive Computing and Communications, vol. 12 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 1 May 1985

Ron L. Cacioppe and Philip Mock

The predominant psychological type among senior police officers is the extraverted‐sensing‐thinking‐judgement type, pragmatic and practical and thus ideally suited to many aspects…

Abstract

The predominant psychological type among senior police officers is the extraverted‐sensing‐thinking‐judgement type, pragmatic and practical and thus ideally suited to many aspects of police work, according to data presented to 119 Australian senior police officers. The high proportion of extraverted‐thinking‐sensing‐judgement types may explain the common macho‐image of policemen. Low levels of self‐actualisation among police officers may limit honesty, openness, flexibility and concern for the good of the police force and society, as well as contributing to stress, so this aspect must be dealt with.

Details

Leadership & Organization Development Journal, vol. 6 no. 5
Type: Research Article
ISSN: 0143-7739

Keywords

Article
Publication date: 14 June 2022

Zhe Jing, Yan Luo, Xiaotong Li and Xin Xu

A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for…

Abstract

Purpose

A smart city is a potential solution to the problems caused by the unprecedented speed of urbanization. However, the increasing availability of big data is a challenge for transforming a city into a smart one. Conventional statistics and econometric methods may not work well with big data. One promising direction is to leverage advanced machine learning tools in analyzing big data about cities. In this paper, the authors propose a model to learn region embedding. The learned embedding can be used for more accurate prediction by representing discrete variables as continuous vectors that encode the meaning of a region.

Design/methodology/approach

The authors use the random walk and skip-gram methods to learn embedding and update the preliminary embedding generated by graph convolutional network (GCN). The authors apply this model to a real-world dataset from Manhattan, New York, and use the learned embedding for crime event prediction.

Findings

This study’s results show that the proposed model can learn multi-dimensional city data more accurately. Thus, it facilitates cities to transform themselves into smarter ones that are more sustainable and efficient.

Originality/value

The authors propose an embedding model that can learn multi-dimensional city data for improving predictive analytics and urban operations. This model can learn more dimensions of city data, reduce the amount of computation and leverage distributed computing for smart city development and transformation.

Details

Industrial Management & Data Systems, vol. 122 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

1 – 10 of 358