The objective of this study is to investigate the approaches utilised by external decision-makers in their evaluation of the different facets of performance of manufacturing organisations, operating as open systems. In process, important linkages which exist among relevant performance parameters are explored. The types and frequencies of performance measures used by the sampled Portuguese financial analysts are compared using factor analysis and multiple regression analysis. Results tend to underscore the significance of the collective performance of all subsystems of the manufacturing system. Based on the findings of this research, implications focusing on the management of organisational performance systems are identified.

The purpose of this study is to develop a guideline/map for small businesses to effectively utilise social media analytics and to create appropriate strategies through an examination of Key Performance Indicators with the business analytics process (Strategy-making map). Also, the researcher examines the new sequential relationships among business analytics types, the role of human analysts in business analytics, and the reciprocal relationships among the social media marketing goals. This is a case study with a local small business’s social media analytics provided by a social media network platform (i.e., Facebook Insights). The map visualises how to interpret the KPIs and how to create effective marketing strategies based on the organisational decision-making model. The results supported that if a business could create a winning strategy based on accurate business analytics with human analysts, the business could achieve multiple social media goals at once with a single marketing strategy.

No description available.

The public sector, particularly healthcare organisations are under ever increasing pressure to do more with less. This coupled with the need to keep up to the constant technological changes and ever increasing abundance of information has led to many public sector organisations adopting Business Intelligence (BI) in order to leverage business value and improve decision-making. However, many organisations such as the National Health Service (NHS) continue to fail in their Information Technology (IT) related initiatives. While the rise of BI and its growing influence in organisations has attracted much academic attention, this has largely been from architectural, design and technological perspectives, whilst little is known about how BI is used by various organisational actors to reach decisions, nor much is understood regarding its resulting impact on organisational power dynamics. Thus, there remains an under researched area of discussion in the literature from the perspective of BI users. While studies report how BI can impact organisational effectiveness, facilitate data driven decision making and supposedly overcome intuitive decision making, the extent to which BI impacts and alters power dynamics between organisational actors across the organisation has received little attention. Accordingly, this research adopts a qualitative case study approach to explore power resulting from BI use within a large NHS trust by conducting 30 semi-structured interviews consisting of operational managers and BI analysts. Through taking a human-centric approach, this research uncovers how BI is altering power dynamics between organisational actors, whereby BI analysts are becoming increasingly influential as a result of their analytical skills. It was found that operational managers are becoming more reliant upon data analysts, resulting in the analysts having more and more influence. However, this research finds it is only when the analysts supplement their technical skill-set with their institutional knowledge, that they have the ability to influence and enact power within the organisational settings. The research also offers insights into the contestations and conflicts which arise from the use of BI, between operational managers and analysts as well as between in-house analysts, based in the operation setting and the centralised analysts, operating across the entire trust. Accordingly, this research empirically validates a BI Power Enactment Framework and proposes the BI Power Matrix, which may assist policy makers in identifying determining key factors which are contributory to the success or failure of technological initiatives.

<p dir="ltr">Wearable devices are ubiquitous. There are over 1.1 billion wearable devices in the<br>market today[1]. The market is projected to grow at a rate of 14.6% annually till 2030[2].<br>These devices collect and store a large amount of data[3]. A major amount of this collected<br>data is stored in the cloud. For many years now, law enforcement organizations have been<br>continuously encountering cases that involve a wearable device in some capacity. There have<br>also been examples of how these wearable devices have helped in crime investigations and<br>insurance fraud investigations [4],[5],[6],[7],[8]. The article [4] performs an analysis of 5 case<br>studies and 57 news articles and shows how the framing of wearables in the context of the<br>crimes helped those cases. However, there still isn’t enough awareness and understanding<br>among law enforcement agencies on leveraging the data collected by these devices to solve<br>crimes. Many of the fitness trackers and smartwatches in the market today have more or<br>less similar functionalities of tracking data on an individual’s fitness-related activities, heart<br>rate, sleep, temperature, and stress [9]. One of the major players in the smartwatch space is<br>Fitbit. Fitbit synchronizes the data that it collects, directly to Fitbit Cloud [10]. It provides<br>an Android app and a web dashboard for users to access some of these data, but not all.<br>Application developers on the other hand can make use of Fitbit APIs to use user’s data.<br>These APIs can also be leveraged by law enforcement agencies to aid in digital forensic<br>investigations. There have been previous studies where they have developed tools that make<br>use of Fitbit Web APIs [11],[12], [13] but for various other purposes, not for forensic research.<br>There are a few studies on the topic of using fitness tracker data for forensic investigations<br>[14],[15]. But very few have used the Fitbit developer APIs [16]. Thus this study aims to<br>propose a proof-of-concept platform that can be leveraged by law enforcement agencies to<br>access and view the data stored on the Fitbit cloud on a person of interest. The results<br>display data on 12 categories - activity, body, sleep, breathing, devices, friends, nutrition,<br>heart rate variability, ECG, temperature, oxygen level, and cardio data, in a tabular format<br>that is easily viewable and searchable. This data can be further utilized for various analyses.<br>The tool developed is Open Source and well documented, thus anyone can reproduce the<br>process.<br>12<br></p>

No description available.

當企業的經營面臨不確定的情況時，使用穩健原則固然是可靠的，但是公司如果過度的使用穩健原則來操縱財務報表，將使資產和盈餘嚴重低估和扭曲，因此反而會降低財務報表的可靠性以及攸關性。公司的財務報表是財務分析師預測的來源之一，故當公司的盈餘由於受到管理當局對會計保守程度之操縱而有較大波動幅度時，若分析師相信公司當期盈餘是對未來盈餘的無偏誤預測指標，則財務分析師將會被誤導。因此，公司的會計保守程度對分析師盈餘預測的誤差和不同分析師間對盈餘預測意見不一致之程度應該有重大的影響。 　　本文以民國90年至94年之上市公司為研究對象，經由迴歸模型來分析公司會計保守性與分析師盈餘預測誤差與盈餘預測分歧程度間之關係，以檢視財務分析師是否能察覺保守性會計對公司盈餘的影響而反映於其盈餘預測中。結果發現會計保守性對分析師盈餘預測屬性均有正向影響，表示財務分析師在預測公司未來盈餘時會對管理當局所選擇的會計保守程度加以評估，並考量管理當局利用會計保守性進行盈餘管理的情形，進而影響其對公司未來盈餘的預測。 / Management may overuse accounting conservatism to manage the financial statements and undervalue assets and earnings and reduce the reliability and relevance of financial statements though conservatism is an increasing trend in accounting practice. The conservative information may lead analysts to biased forecast when a company’s earning has high volatility. Consequently, the extent of accounting conservatism should have significant effect on the analysts’ earnings forecast errors and forecast dispersion. This study examines the relationship of accounting conservatism and analysts’ annual earnings forecast errors and forecast dispersion by using a sample of listed firms in Taiwan. The results show that accounting conservatism has a positive relationship with the analyst earnings forecast errors and forecast dispersion. It implies that financial analysts may evaluate the extent of accounting conservatism and make adjustment in earnings forecast.

Purpose: The purpose of this paper is to examine if underlying psychological factors have an impact on shareholders' investment decisions and if forecasting quarterly reports have an impact on shareholders' investment decisions. Methods: The study combines an application of two different methods, survey and an event study. The purpose of the survey was to investigate if psychological factors interfere with shareholders' investment decisions. The Event study is designed to measure equity market reaction to the forecasts and if any changes occurred in the companies' share price. Theoretical: This study is based on established theories in the fields of behavioral finance, the efficient market hypothesis and the economic man. Conlusion: Underlying psychological factors have a certain impact on the shareholders' investment decisions. Both events study and survey, in particularly the part of the survey, which includes forecasts, showed that the shareholders are not affected by the forecasts published by market analysts.

過去文獻指出，當企業之資訊透明度越高時，追隨企業的財務分析師越多，盈餘的預測偏誤、預測離散度越小；另外，當企業資訊透明度較高時，其管理階層也會因為外部人的監督而減少過度投資與投資不足等逆選擇的發生機會。故本研究欲探討分析師對於企業投資總額之關聯性，並且進一步探討分析師與企業過度投資及投資不足之關聯性。以2010年到2013年之台灣上市櫃公司做為樣本。實證結果發現，分析師是否追隨與企業投資總額沒有顯著之關聯性，而過度投資之公司其投資總額與分析師之預測錯誤率與離散度呈現正相關。

This paper investigates the impact of corporate name changes on both of stock performance and analysts’ reaction with the employment of event study. We first examine a sample of 387 listed Australian companies that renamed themselves during the time frame from January 2001 to December 2007. Separate analyses are conducted under three criterion dividing the overall sample into (1)”major” versus “minor” name changes; (2)name changes in “mining-related” versus “non-mining-related” sectors; and (3)name changes “with” versus “without” reasons mentioned. Generally, we find some evidence of significant negative association between corporate name changes and cumulative abnormal return. The result shows, unlike all other subgroups, name changes “with” reasons mentioned generate insignificant positive valuation effects. Separate analyses give two important implications. First, negative cumulative abnormal return in all subgroups is discovered except in the subgroup of name changes “with” reasons mentioned. The difference of abnormal returns within subgroups, in addition, appears to be significant only between name change with reasons mentioned or not. Our findings suggest that analysts react reluctantly to corporate name changes by showing tiny downward forecast revisions, which are far from significant. Instead, it seems analysts make forecast revisions based more on accounting data, which shows insignificant variations between pre- and post-event, than on signals of corporate name changes.