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The purpose of this paper is to be the first empirical article to provide necessary standard deviation inputs for adoption in probabilistic prognostications of hotel revenues and expenses, i.e. prognostications that consider risk. Commonly accepted methodologies to develop hotel financial projections resulting in point estimates of upcoming performance have been perceived as egregiously insufficient because they do not consider risk in lodging investments. Previous research has recommended the use of probabilistic methodologies to address this concern, and it has been recommended that analysts use Monte Carlo simulation. This methodology requires the estimation of standard deviations of specific, future hotel revenue and expense items, and this paper provides such inputs based on a large sample of actual, recent data.

This study provides actual standard deviations using a sample of recent hotel profit and loss (P&L) statements for over 3,000 hotels (Over 19,000 P&L statements) to provide analysts with empirically-supported standard deviations that may be applied to Uniform System of Accounts for the Lodging Industry (USALI) hotel revenues and expenses in hotel financial (revenue and expense) prognostications.

Findings are presented for standard deviations based on typical line items as defined in the USALI, and these findings may be used by practitioners as inputs for hotel financial projections. Findings also include that hotel revenue items generally have higher standard deviations than expense items. Findings are presented in detail in the manuscript, including overall findings, as well as findings based on hotel class.

Rather than practitioners adopting standard deviations of hotel revenue and expense line items based on guesswork or judgment, which is the current “state of the art” in hotel financial projections, this paper provides practitioners with actual standard deviations which may be adopted in probabilistic prognostications of hotel revenues and expenses.

This paper may be the first to provide practitioners with actual standard deviations, based on typical USALI line items, for adoption in probabilistic prognostications of hotel revenues and expenses.

As interest in technology ethics is increasing, so is the interest in bringing schools of ethics from non-Western philosophical traditions to the field, particularly when it comes to information and communication technology. In light of this development and recent publications that result from it, this paper aims to present responds critically to recent work on Confucian virtue ethics (CVE) and technology.

Four critiques are presented as theoretical challenges to CVE in technology, claiming that current literature insufficiently addresses: overall applicability, collective ethics issues, epistemic overconfidence within technology corporations and amplification of epistemic overconfidence by the implementation of CVE. These challenges make use of general CVE literature and work on technology critique, political philosophy, epistemology and business ethics.

Implementing CVE in technology may yield some benefits, but these may be outweighed by other outcomes, include strengthening hierarchies, widening inequities, increasing, rather than limiting, predictive activity, personal data collection, misinformation, privacy violations and challenges to the democratic process.

Though not directly advocating against CVE, the paper reveals hitherto unidentified and serious issues that should be addressed before CVE are used to inform ethics guidelines or regulatory policies. It also serves as a foundation for further inquiry into how Eastern philosophy more broadly can inform technology ethics in the West.

This paper seeks to develop experimental laboratory biological techniques for approximation of existing road networks, optimizing transport links, and designing alternative optimal solutions to current transport problems. It studies how slime mould of Physarum polycephalum approximate highway networks of Brazil.

The 21 most populous urban areas in Brazil are considered and represented with source of nutrients placed in the positions of slime mould growing substrate corresponding to the areas. At the beginning of each experiment slime mould is inoculated in São Paulo area. Slime mould exhibits foraging behavior and spans sources of nutrients (which represent urban areas) with a network of protoplasmic tubes (which approximate vehicular transport networks). The structure of transport networks developed by slime mould are analyzed and compared with families of known proximity graphs. The paper also imitates slime‐mould response to simulated disaster.

It was found that the plasmodium of P. polycephalum develops a minimal approximation of a transport network spanning urban areas. Physarum‐developed network matches man‐made highway network very well. The high degree of similarity is preserved even when high‐demand constraints are placed on repeatability of links in the experiments. Physarum approximates almost all major transport links. In response to a sudden disaster, gradually spreading from its epicenter, the Physarum transport networks react by abandoning transport links affected by disaster zone, enhancement of those unaffected directly by the disaster, massive sprouting from the epicenter, and increase of scouting activity in the regions distant to the epicenter of the disaster.

Experimental methods and computer analysis techniques presented in the paper lay a foundation of novel biological laboratory approaches to imitation and prognostication of socio‐economical developments.

The purpose of this paper is to provide a brief overview of condition based maintenance (CBM) with definitions of various terms, overview of some history, recent developments, applications, and research challenges in the CBM domain.

The article presents the insight into various maintenance strategies and provides their respective merits and demerits in various aspects. It then provides the detailed discussion of CBM that includes applications of various methodologies and technologies that are being implemented in the field. Finally, it ends with open challenges in implementing condition based maintenance systems.

This paper surveys research articles and describes how CBM can be used to optimize maintenance strategies and increase the feasibility and practicality of a CBM system.

CBM systems are completely practical to implement and applicable to various domains including automotive, manufacturing, aviation, medical, etc. This paper presents a brief overview of literature on CBM and an insight into CBM as a maintenance strategy. CBM has wide applications in automotive, aviation, manufacturing, defense, and other industries. It involves various disciplines like data mining, artificial intelligence, and statistics to enable the systems to be maintenance intelligent. These disciplines help in predicting the future consequences based on the past and current system conditions. Based on the authors’ studies, implementation of such a system is easy and cost effective because it uses existing subsystems to collect statistical data. On top of that it requires building a software layer to process the data and to implement the prognosis techniques in the form of algorithms.

The design of CBM systems highly impact the society in terms of maintenance cost (i.e. reduces the maintenance cost of automobiles, safety by providing real time reporting of the fault using prognosis).

To the best of the authors’ knowledge, this paper is first of its kind in the literature which presents several maintenance strategies and provides a number of possible research directions listed in open research challenges.

While all recoveries are good, some are better than others with regard to their speed and/or magnitude. Many revenue-related key performance indicators (KPIs), such as comparisons to budgets and forecasts that were designed pre-pandemic to assess a hotel's or destination's performance are no longer valid. Therefore, the primary purpose of this conceptual paper is to highlight the need to peg financial-related KPIs relative to competitors' performance during and following a radical market disruption. The secondary purpose of this paper is to summarize advances reported in the literature and in the industry related to competitor benchmarking and accurately identifying competitor sets.

This conceptual paper synthesizes research from disparate sources to offer a series of recommendations to the industry regarding best practices for developing and monitoring revenue-related KPIs during pandemic recovery. Such KPIs will be different based upon hospitality or tourism sector but must be largely founded upon benchmarking off comparable operations.

Industry disruptions triggered by COVID-19 underscore the need (1) to increasingly utilize competitor-based revenue KPI benchmarks; (2) to have reliable competitor benchmarking data more readily available for use by hotels and destination marketing organizations (DMOs) and (3) for both hotels and DMOs to more accurately identify their competitive sets.

The recommendations offered in this paper are anchored with appropriate theories and empirical research; and as a consequence, offer guidance for the industry for KPI formulation during and following the pandemic.

For several decades, scholars have called for “open” or “living systems” approaches to the study of complex organizations. These approaches were especially characterized by the presence of various nonlinear dynamics. Unfortunately, the formal mathematics required to capture these dynamics did not become widely available until the recent mass computing revolution. Meanwhile, these realms of mathematics, known variously as “catastrophes,” “chaos,” and more recently as “complexity,” have caught hold in the physical, biological, and cognitive sciences. This article contends that it is now time for these recent advances in the sciences of nonlinearity to emerge full scale in the social realm as well. However, thus far, this movement is much more metaphorical than it is methodological. Thus, a review of some policy and management applications is undertaken. Of particular interest are those where the uses of phase plane analysis and genetic algorithms portend significant practical import.

The purpose of this paper is to measure and evaluate the performance efficiency of 44 Indian commercial banks, out of which 26 banks belong to the public sector, and 18 banks are from the private sector for the period of 2008-2013.

The two-stage network data envelopment analysis (DEA) approach (i.e. variable return to scale and constant return to scale) is used for the measurement of performance in the Indian banking sector. To verify the robustness of the proposed study, sensitivity analysis is also performed.

A comparative study between public sector banks (PSBs) and private sector banks (PVBs) showed that latter being more productive compared to the former. The investigation highlighted that two banks are most efficient among the PSBs, and eight banks from PVBs are found to be most effective. On the other side, the performance of State Bank of Bikaner & Jaipur and Lakshmi Vilas Bank is discovered to be less significant from PSB and PVB category, respectively.

This study will guide the Indian banks to improve upon the factors in which they are lagging, for the improvement of their overall performance. The quality category parameters, i.e. quality of service, quality of equipment, are not considered due to unavailability of information in the output measures, and the methodology used for the study does not identify the causes or remedies for the inefficiency of the banks.

The developed DEA model would help the decision maker to take decisions on the issues related to the performance of the banks. This paper discusses very practical issues in an analytic manner.

Forecasts of demand for innovative, high‐technology products in the 1980s have usually erred on the high side, often by as much as 50 percent or more. Business decisions based on those inflated forecasts have sometimes spelled disaster: empty plants, layoffs, even bankruptcy. This article examines the problem of overoptimistic forecasting in the high‐technology arena, offers reasons for this bias, and suggests some action recommendations which can be applied by high‐tech decision makers to make forecasts more realistic. These recommendations include getting to know your market better, developing forecasting judgment, and finally, applying a “deflator” of up to 30–50 percent to forecasts that involve an abnormally high level of risk.

This article revisits a paper written by Denis Loveridge 15 years ago about the merit or otherwise of the long view versus its short‐term counterpart. The paper revisits the notions set out in 1988 and enlarges them, by making use of the authors’ practical experience. Two notions lay at the heart of the paper: the question of what people will value in their lives, and Maxwell’s notion of a philosophy of “wisdom”. The authors contrast this view with those seeking an epistemological basis for foresight, concluding that the latter does not have much to offer those who conduct foresight for prosaic aims in business and the public sphere.