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This paper seeks to provide researchers and librarians with an overview of the US Census Bureau's American Community Survey (ACS), with a specific focus on practical issues that users must face when choosing and using ACS datasets.

Each of the following issues are explored subsequent to a general overview of the ACS: choosing among census datasets from different census programs, interpreting and choosing between the different ACS period estimates, selecting census geography, understanding and recalculating margins of error, and accessing the data. Samples of ACS tables and formulas for creating derived estimates are used to illustrate how to interpret and work with the data.

The ACS datasets are fundamentally different from the decennial census as they are period estimates created from rolling sample surveys. The ACS has a steeper learning curve; this complexity is due in part to the number of choices users must make between datasets, but the primary challenge is learning how to understand and work with estimates as opposed to population counts.

While other papers have discussed the benefits and challenges of the ACS, this paper is structured around the practical issues that researchers must face when using it. Special consideration is given to calculating derived estimates using spreadsheet formulas, as this is a key task that many users will need to perform and spreadsheets are the most likely tool users will employ to manipulate the data.

This paper aims to present sample-based estimation methodologies to compute the confidence interval for the mean size of the content of material communicated on the digital social media platform in presence of volume, velocity and variety. Confidence interval acts as a tool of machine learning and managerial decision-making for coping up big data.

Random sample-based sampling design methodology is adapted and mean square error is computed on the data set. Confidence intervals are calculated using the simulation over multiple data sets. The smallest length confidence interval is the selection approach for the most efficient in the scenario of big data.

Resultants of computations herein help to forecast the future need of web-space at data-centers for anticipation, efficient management, developing a machine learning algorithm for predicting better quality of service to users. Finding supports to develop control limits as an alert system for better use of resources (memory space) at data centers. Suggested methodologies are efficient enough for future prediction in big data setup.

In IT sector, the startup with the establishment of data centers is the current trend of business. Findings herein may help to develop a forecasting system and alert system for optimal decision-making in the enhancement and share of the business.

The contribution is an original piece of thought, idea and analysis, deriving motivation from references appended.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The purpose of this paper is to catalogue the practice of goodwill impairment testing in Australia and to provide evidence of the extent of compliance with respect to the disclosure requirements of international financial reporting standards (IFRS).

The research question is addressed using an empirical archival approach with an emphasis on note‐form disclosures in the audited financial accounts of 200 goodwill‐intensive firms listed on the Australian Securities Exchange at 2006. The disclosures regarding impairment testing methodologies along with key input variables for the estimation of recoverable amounts are catalogued and an assessment is made of the extent to which such disclosures confirm with the requirement of AASB136.

The results provide evidence of systematic non‐compliance with the disclosure requirements of the IFRS goodwill impairment testing regime on the part of large listed Australian firms. Insight is gained into the level of difficulty experienced by large, sophisticated and well‐resourced organisations in confronting the challenges associated with changing their financial reporting practices at the time of mandatory adoption of IFRS in Australia.

While previous goodwill impairment testing studies have examined discount rate selection by reporting entities as one input variable solely under the value in use method, this paper provides empirical insights into all aspects of goodwill impairment testing for value in use, fair value and mixed method firms, cataloguing growth rate and forecast period disclosures. The paper provides a baseline study of compliance quality at the inception of IFRS in Australia.

This cautionary note provides a critical analysis of a statistical practice that is used pervasively by researchers in strategic management and related fields in conducting covariance structure analyses: The argument that a “large” sample size renders the χ2 goodness-of-fit test uninformative and a statistically significant result should not be an indication that the model does not fit the data well. Our analysis includes a discussion of the origin of this practice, what the attributed sources really say about it, how much merit this practice really has, and whether we should continue using it or abandon it altogether. We conclude that it is not correct to issue a blanket statement that, when samples are large, using the χ2 test to evaluate the fit of a model is uninformative and should be simply ignored. Instead, our analysis leads to the conclusion that the χ2 test is informative and should be reported regardless of sample size. In many cases, researchers ignore a statistically significant χ2 inappropriately to avoid facing the inconvenient fact that (albeit small) differences between the observed and hypothesized (i.e., implied) covariance matrices exist.

The purpose of this paper is to develop a profile of the sexual behavioural characteristics of black men who have sex with men (MSM) in the Greater Toronto Area (GTA), Canada who constitute a unique mixture in terms of background, race, ethnicity, and culture. Having a profile of the sexual health and risk taking behaviours of these men is important since it provides information on these black Canadian men in comparison other black MSM.

Data were collected as part of a cross-sectional study of black MSM in GTA. Survey participants completed a questionnaire requesting information on socio-demographic characteristics, sexual behaviour, general and mental health, and awareness of social marketing strategies for gay men. The study was conducted in 2007-2008, through convenience sampling. Based on the data collected, the authors characterized the profile of black MSM with respect to sexual risk behaviours.

The authors collected data on 168 black MSM. These men perceived their general health to be good. However, a large proportion of them practiced inconsistent condom use but this varied according to the ethnicity of the partner. Inconsistent condom use also varied by place of birth with Canadian-born men and Caribbean-born men less likely to consistently use condoms than African-born men. In multiple regression analysis, being born in Africa favoured condom use. Men were also more likely to practice inconsistent condoms use when the sexual partner was non-black. Further, when sex with other black men was examined, those who were older (30 years) and had not disclosed their sexuality were more likely to stop using condoms. Other variables which were expected to have associations with inconsistent condom use, based on studies in other jurisdictions – such as previous sexually transmitted infections, sex with women, sex while travelling, and drug use were not related to inconsistent condom use.

While the survey data were based on a relatively small sample size and may not be representative of the entire black MSM population in the GTA, it provides a basis for ongoing and targeted support for black MSM particularly those born in Canada. Older men who are not open with their sexuality may be at a risk of acquiring or transmitting HIV. Future research should focus on these men.

This report provides a perspective on the sexual health and risk taking behaviours of black MSM in Canada. This is particularly important since their social history and health determinants are different from those of US African Americans. The results will stimulate further research targeting this group, and support HIV programmes and services for these men.

Though extant literature has identified goods-based brand image (GBBI) and services-based brand image (SBBI) as drivers of business-to-business (B2B) loyalty, their relative importance has remained unexplored. This study aims to bridge this gap.

The authors have used a retrospective sampling-based methodology to collect data from B2B customers via an offline survey with a sample size of 125 purchase managers.

The authors found that both GBBI and SBBI have positive relationships with B2B loyalty, with customer satisfaction being the mediator. Using the construal level theory (CLT), the authors argue that the B2B purchase term, vendor–customer relationship strength and physical accessibility of the vendor are associated with the construal level of the purchase context. Further, the authors show that B2B customers give higher importance to GBBI in lower construal level and higher importance to SBBI in higher construal level. The authors have also found the moderated mediation effect of customer satisfaction in GBBI–loyalty and SBBI–loyalty relationships with construal level as moderator.

This study contributes to extant literature of B2B branding and purchase decision-making by bringing in concepts of CLT. It also extends the literature of the GBBI–SBBI–loyalty relationship by bringing in newer results, which reassure the coexistence of goods-dominant and service-dominant logic in the B2B marketplace.

Important managerial implications have been discussed to help B2B managers in brand building, product–service design and relationship management.

This paper is a pioneer in using the CLT in the B2B purchase contexts. It also provides a theoretical and psychological underpinning of goods–service dilemmas in the B2B context, which is also noble.

This paper seeks to clarify the use and limitations of the IMS National Disease and Therapeutic Index database, which is frequently used by scholars and courts to provide insights into pharmaceutical markets. Specifically, the paper aims to discuss appropriate and inappropriate uses of the data and details the limitations as a means of drawing generalizable conclusions.

The paper takes the form of a literature review and critical evaluation of data and its uses.

The IMS NDTI can provide useful insights into pharmaceutical markets, and also provide indications of potential trends or behaviors, but cannot be relied on for conclusive evidence of such phenomena in the marketplace. The NDTI has limitations that result from sampling and design issues, as well as the specific method by which the data are collected and coded. Although IMS is clear and forthright in addressing these limitations, many researchers have apparently chosen not to heed these cautions and have drawn unsupportable conclusions from NDTI data.

The implications of drawing inappropriate conclusions from the NDTI database can range from the development of a crucial misunderstanding of the market by pharmaceutical marketers, the establishment of erroneous theories or assumptions into the literature or even the miscarriage of justice when the data are used as the basis for a legal judgment or claim. Users must be cautious when drawing any conclusions from these data.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

The purpose of this paper is to analyze the influence of online hotel reviews (OHRs) on the fit between strategy and use of the management control system (MCS) in small- and medium-sized hotels in Brazil. The study analyzed the influence of the variable OHR on the fit between the deliberate strategy and emergent strategy, as well as the diagnostic use and interactive use, of MCS.

The study was carried out with the application of a questionnaire in small- and medium-sized hotels in Brazil. The analyses are based on 78 responses from Brazilian hotels. The analysis used the modeling of structural equations by parts (SmartPLS).

The results show the influence that external variables have in the adjustment of management systems. Specifically, the authors present quantitative evidence that OHR plays an important role in the adjustment between the deliberate strategy and the diagnostic use of MCS.

The results have several implications for research and practice.

The results have several implications for research and practice. A practical implication of this work is to understand how external variables (e.g. OHR) can be important in the fit of management systems. This study offers value for managers in that it supports the argument that hotels can benefit from the use of OHR in the MCS fit.

This study provides evidence for the influence of external variables, such as OHR, on the fit between strategy and MCS use. The study contributes to the literature by providing new evidence of the role of guest evaluations in aligning strategies with the use of MCS.