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Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in price sensitivity for different tickets, binary outcomes, and nonlinear interactions between ticket features. Our secondary goal is to highlight how this estimation can be integrated into a prescriptive trading strategy for buying and selling tickets in an active marketplace. Academic/practical relevance: We present a novel method for demand estimation with heterogeneous treatment effect in the presence of confounding. In practice, we embed this method within an optimization framework for ticket reselling, providing the ticket reselling platform with a new framework for pricing tickets on its platform. Methodology: We introduce a general double/orthogonalized machine learning method for classification problems. This method allows us to isolate the causal effects of price on the outcome by removing the conditional effects of the ticket and market features. Furthermore, we introduce a novel loss function that can be easily incorporated into powerful, off-the-shelf machine learning algorithms, including gradient boosted trees. We show how, in the presence of hidden confounding variables, instrumental variables can be incorporated. Results: Using a wide range of synthetic data sets, we show this approach beats state-of-the-art machine learning and causal inference approaches for estimating treatment effects in the classification setting. Furthermore, using National Basketball Association ticket listings from the 2014–2015 season, we show that probit models with instrumental variables, previously used for price estimation of tickets in the resale market, are significantly less accurate and potentially misspecified relative to our proposed approach. Through pricing simulations, we show our proposed method can achieve an 11% return on investment by buying and selling tickets, whereas existing techniques are not profitable. Managerial implications: The knowledge of how to price tickets on its platform offers a range of potential opportunities for our collaborator, both in terms of understanding sellers on their platform and in developing new products to offer them. History: This paper has been accepted as part of the 2019 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the National Science Foundation [Grant CMMI-1563343]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.1065 .

Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths.

Sponsoring charity sport events (CSEs) represents an opportunity for businesses to achieve a variety of marketing objectives. Event sponsors need to promote their brand in an authentic manner because CSE participants may be skeptical of the sponsor if they believe the organization is supporting the event solely for commercial purposes. The current research examines the perceptions that CSE participants have for a sponsor’s contribution to the value creation process of the event. Semistructured interviews (N = 17) were conducted with MS (multiple sclerosis) Moonlight Walk 2018 participants to explore how this key stakeholder perceives the contribution of the sponsor (Harbour ISP [Internet service provider]) in the event experience. Five themes were uncovered: raising CSE awareness, cultivating a fundraising network, engaging authentically, celebrating constituents, and providing operational support. Building on the findings of this research, CSE managers and sponsors should work to share the story behind their partnership while integrating event participants in the development of the sponsorship program.

The continuity strategy of business innovation to deal with the disruption condition, it is still many to be designed and explained. Referring to a sociotechnical systems (STSs) approach, this article conceptualizes and practically examines the business analytics, virtual business, and knowledge absorptive capacity that increase and produce the continuity capability of business innovation in uncertain business environment (e.g., COVID-19). We used SmartPLS to empirically evaluate the model based on 145 companies in a developing country of Indonesia that is currently under high disruption. Drawing a technical perspective, business analytics has successfully enhanced business continuity innovation and knowledge absorptive capacity. In a socio perspective, knowledge absorptive capacity significantly produces business continuity innovation. Business continuity innovation suffers if firms adopt virtual business without increasing their knowledge capabilities. Furthermore, mediating effects present higher support from management level to knowledge absorptive capacity strategy, a bigger impact for business analytics, and virtual business to improve and increase the goals opportunities of business continuity innovation. A novel contribution is the integration of STSs (business analytics, virtual business, and knowledge absorptive capacity) that drives business continuity innovation to deal and recover from uncertain environment. Our efforts also align several fields such as information technology-business alignment strategy, knowledge management, and knowledge-business services. Future research would be worthwhile and interesting whether our framework will suitable to different companies/respondents, business context, industry area, and crisis condition.

Purpose Recent research and policy reports indicate public sector organizations struggle to leverage information technology-based performance measurement systems and fail to effectively evaluate performance beyond financial metrics. This study aims to focus on organizational factors that influence the assimilation of business intelligence (BI) systems into integrated management control systems and the corollary impact on improving business process performance within public sector organizations. Design/methodology/approach The complete Australian client list was acquired from a leading BI vendor; and the authors surveyed all public sector organizations, receiving 226 individual responses representing 160 public sector organizations in Australia. Using latent construct measurement, structural equation modeling (SEM)-partial least squares is used to test the theoretical model. Findings When top management promotes knowledge creation among the organization’s operational level employees and support their activities with strong BI infrastructure, the same knowledge and infrastructure capabilities that are critical to assimilation in private sector hold in the public sector. However, public sector organizations generally have difficulty retaining staff with expertise in new technologies and attracting new innovative staff that can leverage smart systems to effect major change in performance measurement. When top management effectively manages knowledge importation from external entities to counteract deficiencies, public sector organizations effectively assimilate BI knowledge into performance measurement yielding strong process performance. Research limitations/implications When top management promotes knowledge creation among the organization’s operational level employees and support their activities with strong BI infrastructure, the same knowledge and infrastructure capabilities critical to assimilation in the private sector hold in the public sector. However, public sector organizations generally have difficulty retaining staff with expertise in new technologies and attracting new innovative staff that can leverage smart systems to effect major change in performance measurement. The research extends the theory behind organizational absorptive capacity by highlighting how knowledge importation can be used as an external source facilitating internal knowledge creation. This collaborative knowledge creation leads to affective assimilation of BI technologies and associated performance gains. Practical implications The results provide guidance to public sector organizations that struggle to measure and validate service outcomes under New Public Management regulations and mandates. Originality/value The results reveal that consistent with the philosophies behind New Public Management strategies, private sector measures for increasing organizational absorptive capacity can be applied in the public sector. However, knowledge importation appears to be a major catalyst in the public sector where the resources to retain skilled professionals with an ability to leverage contemporary technologies into service performance are often very limited. Top management team knowledge and skills are critical to effectively leveraging these internal and external knowledge creation mechanisms.

Building on the elaboration likelihood model (ELM) and absorptive capacity, this study develops a four-dimensional model of idea adoption in Virtual Crowdsourcing Communities (VCCs) and examines the influence of different persuasion cues on idea adoption. The research model was tested using hierarchical logistic regression based on a dataset from the Tableau community. The results show that both community recognition of users and community recognition of ideas are positively related to idea adoption. Proactive user engagement has a significant positive impact on idea adoption, while reactive user engagement has no significant impact. Idea content quality, represented by idea length and supporting arguments, has an inverted U-shaped relationship with idea adoption. Community absorptive capacity positively moderates the curvilinear relationship between idea content quality and idea adoption. These results contribute to a better elucidation of the persuasion mechanisms underlying idea adoption in VCCs, and thus provide important implications for open innovation research and practice.

The effects of e-commerce and big data on accounting and cost management should be evaluated comprehensively from various aspects. Increasing internet-based applications deeply affect both accounting and cost management. The effect of three basic variables is emphasized in the study. First, the relationship between business analytics and management accounting, and cost performance was evaluated. Business analytics can play an important role in the effectiveness of management accounting. The findings of the study show that descriptive and predictive analytics have positive effects on the planning, control, and cost management. The productivity increase seen in the planning and control functions of management accounting improves the cost performance in favor of the company. Managerial and practical evaluations have been made in the context of the inferences obtained from the research.

This article is devoted to analysis of models of St. Petersburg paradox, as well as development of software in the sphere of business analysis. This work is based on mathematical models using theories of probability and games as well as expert survey method. It is demonstrated that the St. Petersburg paradox is a mathematical problem of probability theory with artificial conditions. The influence of this problem on economical theory is exemplified by such provisions as the principle of diminishing marginal utility, the use of expected utility as criterion of decision adoption in uncertain environment, as well as foundations of microeconomics of insurance and risk management, theory of games and some approaches to financial simulation. Adoption of decisions on the basis of the St. Petersburg paradox is analyzed. Review of main decisions of the St. Petersburg paradox and their influence for economic theory has confirmed that the St. Petersburg paradox as a mathematical problem can be used as mathematical model upon implementation of financial simulation. Comparative analysis of available BI solutions has confirmed that most of them propose all major functions, and significant differences can be revealed in penetration of expanded functions.

Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts’ opinion, but without a systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed.

Purpose The strategic entrepreneurship (SE) literature exists at the intersection of the strategy and entrepreneurship literatures and has grown rapidly over the past two decades. This study aims to document the proliferation of research papers and identifies the major thematic clusters of topics and other summary information for the SE research domain. Design/methodology/approach This is a systematic bibliometric review of 586 articles published over the period 2009–2019 in 143 journals. The inductive quantitative assessment of these articles uses meta-data driven techniques that prioritize reproducibility and rigor in the process of literature analysis. Findings This study identifies six main themes in the strategic literature, namely, Sustainable Competitive Advantage; Knowledge Management; Ecosystems; Strategy; Entrepreneurialism; and Organization and Management. It also reports data on a variety of issues including research techniques, country of data, co-author count and trends and differences between journals based on their journal impact factors and calls for more research in key areas. Originality/value An innovative original analytical tool was developed to facilitate the analysis of research papers in this growing field. This online tool allows multiple tags to be attached to each paper by multiple authors working simultaneously to identify keywords and other aspects that were subsequently used to identify six main thematic areas within the SE literature. This paper highlights emerging research trends and identifies gaps in the literature that provide opportunities for further research in this field.

Objetivo deste artigo é compreender como Business Analytics é identificado como um recurso estratégico no contexto do Gerenciamento de Receita em uma companhia aérea. Nesse âmbito, para responder à questão central desta pesquisa optou-se pela condução de uma pesquisa qualitativa. O processo de coleta de dados foi realizado por meio de observação direta (não participante), de entrevistas e de análise documental. Para a análise de dados, foi realizada uma análise de conteúdo pelo método de análise em espiral de Creswell (2013). Foi possível avançar na abordagem da Visão Baseada em Recursos ao fazer uma articulação teórica entre o campo de Business Analytics (BA) e Gerenciamento de Receita (RM). Dentro das categorias de análise encontradas, cada recurso de capital humano, organizacional e físico foi descrito na rotina da área de Gerenciamento de Receita. Ao descrever essa rotina, foi possível identificar, dentro do modelo VRIO (BARNEY, 1995), como cada atributo foi evidenciado ao buscar garantir a eficiência e eficácia dos recursos que são utilizados pela organização.

Purpose “Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic. Design/methodology/approach For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic. Findings According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way. Originality/value The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).

This paper develops and extends the existing body of literature on human resource management (HRM) and contextual uncertainty. We identify and explore the consequences of present uncertainties of a broad scale and scope for the practice of HRM. We then review salient bodies of theorising, and map out relevant areas of application. This is followed by a presentation of some of the most recent work on uncertainty and HRM that is encompassed in this collection, which brings to bear evidence from around the world.

The purpose of this research is to introduce an innovative decision architecture to assess corporate risks by utilizing accounting narratives and to further examine the association between those risks and operating performance. We run text mining algorithms to identify the types of risks from narratives and incorporate them with a readability measure (i.e., linguistic cue(s)) to conjecture a firm manager’s attitude toward corporate risk. We consider a two-stage network data envelopment analysis (TNDEA) with the benefit of opening up the black-box of a production process via considering internal activities. The outcome derived from TNDEA is determined by users who decide “a priori” what the specification of the model should be, without considering any alternatives. The study further incorporates the fuzzy rough set theory (FRST) with TNDEA by considering inclusion/exclusion or different combinations of inputs, intermediates, and outputs so as to realize a corporate’s underlying business situation. Decision attributes taken from the outcome of FRST-TNDEA and condition attributes gathered from annual reports are jointly inserted into an artificial intelligence (AI) technique to establish the forecasting model. The integrated circuit (IC) industry has long been viewed as an essential backbone of Taiwan’s economy, and therefore it is taken as our research target. The results show that the introduced linguistic cues have positive and considerable impacts on firm performance. Overall, the findings herein provide direct support for recent regulators that require corporates to add a new section on risk factors in accounting narratives so as to prevent users from making biased judgments.

Despite the growing popularity of business analytics (BA) in the increasingly knowledge-based economy, many firms are still skeptical about its strategic value and thus hesitant to adopt BA. To hav...

PurposeThe purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.Design/methodology/approachThis paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.FindingsIt was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.Originality/valueThis paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

PurposeResearch on social media frequently analyze social media usage (SMU)'s positive consequences for organizations and individuals; however, recent innovation studies caution that SMU may not always lead to positive new product development (NPD) outcomes. The competing streams of research highlight a fundamental tension that exists in the social media literature exemplified by the question: Is SMU good or bad for NPD? In this manuscript, the authors suggest that a more appropriate question as follows: What are the positive and negative indirect effects of SMU on NPD performance? The purpose of this paper is to discuss the aforementioned points in detail.Design/methodology/approachA literature review provides the model and hypotheses. Using a sample of 168 Chinese firms, the authors conducted an empirical test following multiple regression analysis.FindingsThe results demonstrate that SMU facilitates business analytics (ability) and social legitimacy (opportunity) but impairs entrepreneurial proclivity (motivation). These three constructs in turn mediate the effect of SMU on NPD performance. Moreover, this paper explores how technological turbulence moderates SMU's effects on business analytics, entrepreneurship proclivity and social legitimacy.Research limitations/implicationsThe results may be affected by both the context (solely in China) and type (cross-sectional) of the data set. Future research might take a decompositional approach to study SMU's effect on innovation in different NPD stages. Furthermore, with widely varying purposes (e.g., marketing, information searching, partner collaboration, new product launch, etc.), there is certainly a need for more clarity and understanding of how firms can leverage each of these different social media activities for successful NPD.Practical implicationsFirst, we suggest that managers in China should be explicitly aware of the double-edged sword effect of SMU on NPD performance. Second, this study encourages managers to use social media carefully when technological turbulence becomes intense.Originality/valueDrawing on the ability–motivation–opportunity framework, this is one of the first studies to simultaneously examines the benefits and costs of SMU for NPD. In addition, this paper bridges the separate literatures on social media, business analytics, entrepreneurial proclivity and social legitimacy and contributes to the NPD research.

Enterprise systems can play a fundamental role in the management of business analytics by facilitating knowledge management. Nonetheless, and despite extensive studies about enterprise systems and knowledge management over the past four decades, the information systems discipline still lacks a clear and practical understanding of what types of knowledge should be managed by enterprise systems, and how to facilitate the adoption and effective use of business analytics through knowledge management. To a high degree, the issue is rooted in the ambiguity about the nature and sources of knowledge. Motivated by this need, we conducted an exploratory study to address the research question, what knowledge is critical for data analysts to be able to use business analytics effectively? We interviewed 41 data analysts. Using thematic analysis, we propose the theory of knowledge identity, which explains the role of data analysts’ knowledge in the adoption and effective use of analytics in organisations. This study offers significant theoretical and practical implications.

PurposeIn today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.Design/methodology/approachThis research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).FindingsThe findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.Originality/valueThis study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

The increasing digitalization in the automotive industry is influencing the structure of the traditional value chain and calls for the handling of large amounts of data to remain competitive in a c...

We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

In the era of knowledge-based economy, the firm’s ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilising BA which is desi...

PurposeOne of the most challenging issues businesses face today is seeking ways toward gaining competitive advantage (CA). Although previous research considered several aspects in this regard, the literature has largely overlooked the process of gaining CA via knowledge-based marketing (KBM) capabilities. This paper aims to conceptualize KBM, to develop a new scale for KBM and to examine how to gain CA through KBM.Design/methodology/approachDrawing from the related literature and building on the concept of KBM, this paper conducted a mixed-method approach. After reaching a pool of items using qualitative methods, this paper developed new scales and validated them. This paper developed the model by highlighting the antecedent role of KBM toward CA. In the quantitative phase, using data collected from both public/private and manufacturing/service firms, this paper classified the extracted items into five critical factors. Moreover, the relationship between KBM and CA was tested.FindingsThe results indicate that KBM construct consists of five critical capabilities, including market research, competitive intelligence, supply chain management, customer relationship management and new product development. Moreover, there is a positive and significant relationship between KBM and CA.Originality/valueThe traditional ways to achieve CA no longer work. Instead, success in the existing business context necessitates firms to seek CA through their knowledge capabilities. Previous research did not fully address KBM antecedents toward CA in a comprehensive model. By developing a new scale for KBM, this paper tested its relationship with CA, which has hitherto received scant attention in the field of knowledge and competition.

Within an organisation, the quality in big data is a cornerstone to operational, transactional processes and to the reliability of business analytics for decision making. In fact, as organizations are harnessing multi-sources data to rise the benefits of their business, the quality of data becomes important and crucial. This paper presents a new approach to query big data sources using Resource Description Framework (RDF) representation to ensure data quality by harvesting more relevant and complete query results. Our approach handles two important types of heterogeneity over multiple data sources: semantic heterogeneity and URI-based entity identification. It proposes (1) a semantic entity resolution method based on inference mechanism using rules to manage the misunderstanding of data, in real world entities (2) Data Quality enhancement using MapReduce-based query rewriting approach includes the entity resolution results to infer and adds implicit data into query results (3) a parallel combination of MapReduce jobs of saturation and query rewriting inferences to handle transitive and cyclic rules for a richer rules’ expression language (4) experiments to assess the efficiency of the proposed approach over real big RDF data originating from insurance and synthetic data sets.

Observations from faculty who teach marketing indicate that a relatively small percentage of students have strong interest in quantitative analysis and many come into the marketing major out of the misperception that it is light on math. In reality, over 98% of marketing professionals use data during their decision-making processes, while 80% of marketing-related problems use data at least in part to be solved. This article outlines a teaching model for analytical decision-making that links marketing problems with marketing research. It is stressed that while only a small percentage of marketing students become marketing researchers all will become consumers of information that is generated by research. Outlined herein is a dual-stage curriculum approach focused on preparing students to be knowledgeable consumers of research by incorporating a problem and metric-based pedagogy. This design is supported by data from 185 marketing professionals who contribute commonly asked “on the job” problems along with frequently used metrics that marketing students should learn as they train to be future marketing professionals.

Purpose Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews. Design/methodology/approach Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process. Findings BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation. Originality/value To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

The tourism workforce is diverse and the literature exploring workforce issues, whilst limited, largely focuses on remunerated workers. There is recognition that volunteering has a significant role to play in achieving the United Nations Sustainable Development Goals (SDGs); however, there is limited literature exploring the role of volunteering relative to the SDGs’ architecture. With reference to the tourism workforce, there is also a noticeable lack of studies exploring how tourism volunteering contributes to realisation of the SDGs. In a first, using the lens of critical tourism, this conceptual paper seeks to holistically explore how tourism volunteering can contribute to SDG realisation from the perspectives of host volunteering and (guest) volunteer tourism. This study maps the potential of both forms relative to the volunteering activities that United Nations Volunteers (UNV) has identified as means by which volunteers can support the SDGs. The findings revealed common areas through which host volunteering and volunteer tourism contribute but taken on the whole, volunteer tourism has the greater potential to contribute to the SDGs as a development agenda. Informed by these findings, we develop six propositions to advance recognition and research relating to the contribution of tourism volunteering to the SDGs.

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 Perform...

Retailers are facing challenges in making sense of the significant amount of data available for a better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on 'purchasing behavior of customers' and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600 K transaction data of a global grocery retailer are analyzed and 175 stores in Istanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.

Big data is of great importance in manufacturing, since knowing the diverse origin of underlying causes of problems is completely necessary for managing continuous improvement. As manufacturers are...

PurposeBusiness analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.Design/methodology/approachThis study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.FindingsThe thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.Research limitations/implicationsThis study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).Practical implicationsThis study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.Originality/valueThis study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Industry 4.0(I4.0) a German project can influence whole industrial and business system by digital transformation of product design, smart manufacturing and delivered to consumer. Industry 4.0(I4.0) project combines the physical world and digital world together through cyber physical systems and helps SMEs in enhancing production processes and increase productivity through emerging technologies such as Internet of Things and Services, Machine learning, Virtual Reality, cloud computing, business analytics etc. However, adoption and implementation of Industry 4.0 project is not an easy task due to existence of various challenges. In the present study, a survey is conducted with 40 Indian manufacturing and service organisations to get their opinion on Industry 4.0 and the level of adoption of in their organization. The objective of this research is to identify the general awareness and understanding of the concepts of Industry 4.0(I4.0) and its related emerging technologies along with their implementation levels in selected organizations. With the feedback received from the selected organizations, this paper analyzes the results and give some discussion.

PurposeThe purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.Design/methodology/approachThis paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.FindingsNeither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.Research limitations/implicationsThis essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.Practical implicationsProject managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.Originality/valueThis essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

Purpose – Business Intelligence (BI) is the company's ability, through the use of technologies and methods, to transform data from external and internal environments into information to improve performance. Capture, management and transformation can be understood as processes and these can present problems and need for improvement. This article presents a case of applying Lean Office in a BI process in a service large company. The goal is to identify BI process-related waste and propose improvements through Lean Office tools and principles. Methodology – The research was developed through a case study. Three data collection instruments were used: participant observation, document analysis and documented interviews. The analyzes were conducted based on the data collected and tools identified in the literature were applied that helped in conducting the analysis of the information transformation process. Findings - The article presented two main results, the detailed presentation of BI activities that are little explored in the literature, especially in developing countries, and the contribution of Lean Office's philosophy and tools for improving the management of a BI process. Originality/value – It contributes to a better understanding and creation of a common vision about the BI process between researchers and practitioners, and with a case that exemplifies the potential of the Lean Office to identify waste, analyze causes and improve the performance of this process. Keywords - Business intelligence. Business analytics. Process improvement. Lean Office.

Purpose The emergence of the knowledge economy and Industry 4.0 has prompted many firms to invest considerable resources into knowledge-based human resource management (HRM) practices and business analytics capabilities aimed at enhancing competitiveness. This paper aims to propose a conceptual model that examines the collective effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance using knowledge-based theory as a theoretical framework. Design/methodology/approach The present study empirically tested the above-said idea by surveying (n = 182) individuals with supervisory capacity in Jordanian 4- and 5-star hotels. The obtained data was analyzed using linear modeling and fuzzy sets (fsQCA) techniques. Findings Results from linear modeling revealed that knowledge-based HRM practices, business analytics and organizational agility are important antecedents for innovative performance. Conversely, findings from fsQCA revealed that organizational agility is a necessary and sufficient condition to achieve high innovative performance. While business analytics is a sufficient condition to achieve high innovative performance. Originality/value This study is among the first to unveil the linear and complimentary effects of knowledge-based HRM practices, business analytics capabilities and organizational agility on innovative performance. Implications for theory and practice are discussed.

Purpose Central to problems affecting effective university–business collaboration (UBC) is the lack of intensive communication in addition to some administrative issues. This paper aims to resolve this issue by use of a uniquely designed “UBC system.” It suggests establishing a unified business analytics portal as one of the most convenient ways of facilitating UBC. This research paper describes the development and functioning of the “UBC system,” which is capable of supporting universities and businesses in terms of the COVID-19 crisis and in enabling the implementation of innovations in their activities. Design/methodology/approach Several dozen organizations and students and graduates of different universities were selected to test the “UBC system.” This research was conducted in several phases. The “UBC system” was developed on the basis of Microsoft 365, which is a solution made on a single platform with the integrated operation of several applications (SharePoint [Easy-Quiz and Survey], Outlook, Teams, Power Automate and Power BI). The system collects, processes and compares the data; schedules online interviews; conducts intelligence quotient testing and surveys; and its business analytic reports and dashboards are shared on the internet and are accessible from any location and from any device. Findings The results of the given study suggest that establishing reliable and convenient online coordination through the “UBC system” can provide help in enabling efficient collaboration between universities and businesses – something that has gained special importance in the time of the COVID-19 pandemic. This paper provides a number of conclusions and recommendations to improve online cooperation between universities and businesses. Practical implications The system can be administered by state entities (such as the Ministry of Education) and by career development centers within universities. The use of these kinds of systems can become a permanent part of effective UBC. The “UBC system” will not only help overcome employment problems in times of crisis but also make it easier to analyze the real situation and to introduce and develop innovative trends by both businesses and universities through mutual cooperation in an automated mode. Originality/value The proposed “UBC system” platform was developed by the authors.

Objective: The main objective is to elaborate and discuss different techniques used in data mining, to analyze different strategies of data mining to make improvements, and to find more powerful mining techniques for the betterment of the business. Methods: Multiple techniques and strategies of data mining are used to improve the business. We employed the data warehouse methods for the improvements of the business using Business Intelligence (BI) and Business Analytics (BA) along with their types and instruments. We are also discussing some tools used for data mining or ordering organizational data. Findings: We employed Business Intelligence-(BI) and Business Analytics-(BA) techniques for the improvement of the business. Earlier, there were only four (Regression, Classification, Association, and Clustering) techniques that were used for business improvements. It is found that Crawler is the best tool for BI or BA data mining. Novelty : This study analyzed that, BI and BA are the best ways used for data mining, data ordering, or format of data in business. Earlier, these ways were not in use for data mining. Data mining may be the best approach to improve the business. Keywords: Business Intelligence (BI); Business Analytics Data Mining (BADM); Data Warehouse (DW); Knowledge Discovery (KDD) Customer Relationship Management (CRM); Enterprise Resource Planning (ERP)

Advances in technology and innovation require companies to embrace these new trends to compete and stay ahead in the business world. In particular, there is a need for companies to incorporate Business Analytics practices within their organizations. Business Analytics consists of two components: Information Systems and Business Process Orientation. This study aims to investigate the impact of the use of Business Analytics on the Supply Chain Performance in apparel companies in Sri Lanka. This research focuses on discussing the objectives developed to achieve the purpose of the study. To achieve this objective, this current study investigates the relationship between the Information System, Supply Chain Performance and the effect of the use of the Information System in the supply chains of Sri Lankan apparel companies. The study uses a quantitative approach. In this study, for quantitative analysis study performs regression analysis and decision tree analysis. This study identifies a positive relationship between the Information System and the Supply Chain Performance. For further future studies, it is advisable to extend this study by examining the performance of medium-and large-scale companies in the country.

The satisfaction managers have in their business analytics is related to their perceived usefulness of it as an IT (Information Technology). However, their belief in the trustworthiness of the busi...

PurposeDespite a growing interest in business analytics (BA) from the business and academic communities, it is still unknown what truly motivates and hinders the adoption of BA. To have a clear picture of what will lead to the successful implementation of BA, this paper identifies contextual variables (e.g. user characteristics, organizational readiness and technology infrastructure/expertise) that significantly influence the BA adoption decision.Design/methodology/approachThis paper conducted a series of classification, discriminant and logistics regressions analyses to analyze the differences in mail survey responses between adopters and nonadopters of BA and then determine what either motivate or inhibit the BA adoption.FindingsThrough a series of hypothesis testing, we discovered that large firms with a greater number of information technology (IT) staff and budget tended to adopt BA more than their smaller counterparts. Also, we found that BA skeptics, who did not fully recognize BA benefit potentials, were more concerned about BA implementation costs and experienced the greater organization resistance to BA adoption than the others did. Therefore, they were less likely to adopt BA.Originality/valueIn the era of knowledge-based economy, the firm's ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilizing BA which is designed to help business executives and policymakers make well-thought and informed decisions. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the exploratory study of BA practices among the Korean firms.

This paper on hand deals with a research gap, since there were no sources on the topic of changes in business administration studies induced by Industry 4.0 until the year 2018. For this reason, a document analysis was used to examine essential studies from the years 2010 to 2016. This was followed by twenty interviews with experts from universities and companies. Furthermore, a total of 71 people took part in a subsequent online survey. With digitization, the requirements for business administration graduates are also changing from technical competence to methodological and digital competence. Digitization has an impact on all areas, politics, society and the economy. The higher education sector is not unaffected by this either, because universities are forerunners in the transfer of knowledge. For this reason, there is a need to reform and at the same time strengthen the education system. Educational institutions should focus on effective learning and relevant learning content. Thus, the present research deals with the changes at university (especially business studies) and company level induced by Industry 4.0, focusing on the demands on business economists coupled with the entrepreneurial changes of tomorrow. Using the mixed-method approach, a qualitative and a quantitative research approach was carried out. This was based on a three-step sequential research approach: 1) document analysis, 2) expert interviews and 3) online surveys. As a result, the majority of the experts that have participated in the survey confirmed a strong impact of digitalization on the working environment. This change of the workplace requires an adaption by the employees as well as a revision of academic curriculars. Traditional education in business administration is not obsolete, however, requires a stronger link with topics of information technologies. Business administration in higher education must be subject to the following changes: 1) adaptation of curricula with regard to current trends business analytics, blockchain, artificial intelligence, 2) adaptation of IT infrastructure and resources in knowledge transfer such as blogs, online lectures, etc. as well as 3) stronger cooperation between business, science and education.

The purpose of this paper is to take a behavioural perspective to reveal why employees hesitate to use business analytics in their operations throughout the whole organisation. We gather quantitative data with a survey in the financial services industry with 332 responses including both users of analytic tools as well as non-users. The results reveal that on an individual level it is skills being important for usage but not perceived value. On the organisational side, perceived norms from supervisors and peers as well as accessibility are important. Further analyses on the level of different business analytic tool categories show e.g. that the attitude of employees is important for forecasting but not for other tools. Practical implications are that supervisors should be convinced of the importance of analytic tools to foster usage among employees and selfservice options for having access to software supporting business analytics should be offered.

We describe our experience in teaching two different levels of undergraduate business analytics courses during the coronavirus disease of 2019 (COVID-19) pandemic In particular, we focus on two challenges that arose during the shift to emergency remote teaching: 1) engaging students and 2) teaching students how to use software We discuss our efforts to mitigate the effects of these problems and highlight the differences in implementing our strategies in a general-education (i e , required for business majors) course versus an upper-level elective Finally, we discuss lessons learned and recommendations for other educators regardless of their teaching modality © 2021 by the Association for Information Systems

Nowadays insurance industry has huge innovation potential. Several key vectors for developing the concept of insurance tech include machine learning, business analytics, consumer protection rules, Big Data, artificial intelligence, neural networks, blockchain, and telematics. Technological innovations become widespread only when a community that supports them emerges, and COVID-19 has rapidly accelerated the changes that were already in full swing to a greater extent than any other factor. COVID-19 has helped reinforce the story and illustrate the results that technologies achieve on a large scale. Modern marketing and management approaches in insurance are viewed as an activity to optimize and control the insurance company's innovation and marketing activities. It would allow taking a strategically advantageous position in the insurance market. There are two kinds of insurance marketing: structural and commodity. Structural marketing could help to solve the problem of the economic efficiency of the activity of insurance companies. Commodity marketing helps to improve financial activity and, as a result, to increase profitability. This article summarizes the arguments and counterarguments within the scientific discussion on the place and prospects marketing and management in insurance (strategies, functions, principles) in the context of key innovation metrics. The study's primary purpose is to confirm the hypothesis about the functional link between the level of innovative development of the country and key insurance determinants as drivers for transformation in marketing strategies of insurance companies. In this regard, the array of input data is presented in the form of seven independent variables (regressors), six of which denote innovation measures, one is control variable, and five dependent variables (regressands), which identify the insurance sector. The study of the impact of innovation metrics on the insurance sector of the country in the article is carried out in the following logical sequence: 1) the formation of an array of input data; selection of relevant indicators using Principal Component Analysis; 2) formalization of functional relationships between variables by constructing five-panel Multifactor regression models with Random Effects; and 3) interpretation of the obtained results. Seventeen countries of Central and Eastern Europe were selected as the object of the study for the period from 2004 till 2019. The study empirically confirms the above hypothesis, which is evidenced by the following identified dependences. Key insurance determinants depend on innovation fluctuations. The most significant positive influence on the dependent variables is exercised by the Innovations index, Research and development expenditure, and Patent applications by residents. The study results could be helpful for insurance companies that provide new insurance technologies and seek to optimize activities to support innovative development. The main directions of marketing and management in insurance should be considered from two positions applying new technologies in insurance marketing and introducing new insurance products or services.

Statement of Problem: Collaboration a Means to Chaos Likert Type Survey Instrument: Collaborating with other students on academic assignments without having permission from your professor. A large Southwest university examines five years of data about the distinction that may contribute to ethical misconduct in the classroom and misguided actions as a future professional. The classroom models the evolution of technological shifts, which requires broader interaction across disciplines to manage projects. The encouragement to build teamwork skills through class assignments benefits preparation to enter the market economy to lead in e-commerce, social media, information Systems, and Business Analytics. The survey data indicates that collaboration with or without professorial approval is acceptable.