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The study explores the extent to which business analytics can address business problems using the task-technology fit theory.

The qualitative research approach of pattern matching was adopted for data analysis and 12 semi-structured interviews were conducted. Four propositions derived from the literature on task-technology fit are compared to emerging core themes from the empirical data.

The study establishes the relationships between various forms of fit, arguing that the iterative application of business analytics improves problem understanding and solutions, and contends that both under-fit and over-fit can be acceptable due to the increasing costs of achieving ideal fit and potential unaffected outcomes, respectively. The study demonstrates that managers should appreciate that there may be a distinction between those who create business analytics solutions and those who apply business analytics solutions to solve problems.

Extant studies on business analytics have not focused on how the match between business analytics and tasks affects the level to which problems can be addressed that determines business value. This study enriches the literature on business analytics by linking business analytics and business value through problem resolution demonstrated by task-technology fit. To the authors’ knowledge, this study might be the first to apply pattern matching to study the fit between technology and tasks.

Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.

This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.

This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.

The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.

This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.

As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.

This study aims to build on the well-documented case of the Olympus scandal to dissect how social networks and corporate culture enabled corporate elites to commit fraud across multiple generations of leaders.

A flexible pattern matching approach was used to identify matches and mismatches between behavioural theory in corporate governance and the patterns observed in data from diverse sources.

The study applies the behavioural theory of corporate governance from different perspectives. Social networks and relationships were essential for the execution of the fraud and keeping it secret. The group of corporate elites actively created opportunities for committing misappropriation. This research presents individuals committing embezzlement because the opportunity already exists, and they can enrich themselves. The group of insiders who committed the fraud elaborated the rationalizations to others and asked outside associates to help rationalise the activities, while usually individuals provide rationalizations to themselves only.

The social processes among actors described in this case can inform the design of mechanisms to detect these behaviours in similar contexts.

This study provides both perspectives on the fraud scandal: the one of the whistle-blowers, and the opposing side of the transgressors and their associates. The extant case studies on Olympus presented the timeframe of the scandal right after the exposure. The current study dissects the events during the fraud execution and presents the case in a neutral or a negative light.

Disruptive events caused by cyber incidents, such as supply chain (SC) cyber incidents, can affect firms’ SC operations on a large scale, causing disruptions in material, information and financial flows and impacting the availability, integrity and confidentiality of SC assets. While SC resilience (SCRES) research has received much attention in recent years, the purpose of this study is to investigate specific capabilities for building SCRES to cyber risks. Based on a nuanced understanding of SC cyber risk characteristics, this study explores how to build SC cyber resilience (SCCR) using the perspective of dynamic capability (DC) theory.

Based on 79 in-depth interviews, this qualitative study examines 28 firms representing 4 SCs in Central Europe. The researchers interpret data from semistructured interviews and secondary data using the DC perspective, which covers sensing, seizing and transforming.

The authors identify SCRES capabilities, in general, and SCCR-specific capabilities that form the basis for the realignment of DCs for addressing cyber risks in SCs. The authors argue that SCRES capabilities should, in general, be combined with specific capabilities for SCCR to deal with SC cyber risks. Based on these findings, 10 propositions for future research are provided.

Practitioners should collaborate specifically to address cyber threats and risks in SCs, integrate new SC partners and use new approaches. Furthermore, this study shows that cyber risks need to be treated differently from traditional SC risks.

This empirical study enriches the SC management literature by examining SCRES to cyber risks through the insightful lens of DCs. It identifies DCs for building SCCR, makes several managerial contributions and is among the few that apply the DC approach to address specific SC risks.

This paper aims to estimate the price premium for a sea view on room rent in a Nordic context, i.e. where proximity to the sea is not valued for the presence of swimmable beaches and suntanning activities. The analysis also explores regional and seasonal variations in price premiums.

To do so, the study uses information from a Web search of room rents during winter and summer peak seasons. The investigation is based on hotels located along the St. Lawrence River in the Province of Quebec (Canada), where about 40 to 60 km separate both shores. A matching procedure and hedonic pricing models are used to identify the causal impact of a sea view on individual room rents.

Results suggest that the view price premium varies between 0% and 20%. It is relatively stable on the North Shore, but varies highly on the South Shore, where touristic activities are mainly operating in summertime. The estimation suggests a median local economic benefit of about $30.1M/year.

The analysis reveals that a hedonic pricing model might fail to identify causal effects, especially if it does not account for hotel characteristics. A multiple linear regression model does not ensure a causal interpretation if it neglects unobserved characteristics correlated with the view.

The paper proposes a matching identification procedure accounting for spatial confounding to retrieve the causal impact of the view of the sea on hotel room rents. A heterogeneity analysis suggests that view price premium on room rent can vary within seasons but mainly across regions, even for the same amenities.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

The rising occurrence of digitally driven public consumer complaints has made it necessary for enterprises to obtain consumer forgiveness. However, existing research has provided little understanding regarding how to obtain consumer forgiveness effectively. Thus, the present study examined how brand avatars can improve consumer forgiveness in the context of public apology.

This study tested the mechanism of a brand avatar on consumer forgiveness using three studies. Specifically, we explored the direct and mediating effect of empathy toward a brand (Study 1); we identified the moderating mediating effect of humorous responses (Study 2) and product type (Study 3). Data for these studies were collected on Credamo. We analyzed the data using SPSS (26.0) for the primary analysis and PROCESS (3.5) for the mediating and moderating mediating analysis.

The results indicate that brand avatars enhance consumer forgiveness. Moreover, empathy toward a brand plays a mediating role in the effect of brand avatars on consumer forgiveness. Additionally, when a humorous response is present, a brand avatar can enhance customer forgiveness through empathy toward that brand. Compared to utilitarian products, hedonic products can also increase the impact of a brand avatar on empathy toward the brand, thus enhancing consumers' forgiveness.

From the perspective of emotion, this study explored the impact of brand avatars on consumer forgiveness via empathy toward a brand. It augments the research on brand avatars and consumer forgiveness. The study also verified the moderating mediating effect of humor response and product type while expanding the brand avatar research boundary.

When emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution patterns. The purpose of this study is to discover the popularity evolution patterns of social media emergency information and make early predictions.

We collected the data related to the COVID-19 epidemic on the Sina Weibo platform and applied the K-Shape clustering algorithm to identify five distinct patterns of emergency information popularity evolution patterns. These patterns include strong twin peaks, weak twin peaks, short-lived single peak, slow-to-warm-up single peak and slow-to-decay single peak. Oriented toward early monitoring and warning, we developed a comprehensive characteristic system that incorporates publisher features, information features and early features. In the early features, data measurements are taken within a 1-h time window after the release of emergency information. Considering real-time response and analysis speed, we employed classical machine learning methods to predict the relevant patterns. Multiple classification models were trained and evaluated for this purpose.

The combined prediction results of the best prediction model and random forest (RF) demonstrate impressive performance, with precision, recall and F1-score reaching 88%. Moreover, the F1 value for each pattern prediction surpasses 87%. The results of the feature importance analysis show that the early features contribute the most to the pattern prediction, followed by the information features and publisher features. Among them, the release time in the information features exhibits the most substantial contribution to the prediction outcome.

This study reveals the phenomena and special patterns of growth and decline, appearance and disappearance of social media emergency information popularity from the time dimension and identifies the patterns of social media emergency information popularity evolution. Meanwhile, early prediction of related patterns is made to explore the role factors behind them. These findings contribute to the formulation of social media emergency information release strategies, online public opinion guidance and risk monitoring.

Digital servitization research has focused on how manufacturing firms use digital technologies to change business models and offer smart services; less attention has been devoted to the degree to which external actors in the existing ecosystem accept these smart services. Therefore, the authors pose the following research question: How does a manufacturing firm introduce and gain acceptance of new smart services within an established ecosystem?

Building on servitization, ecosystem and legitimacy theories, this paper addresses the research question through an in-depth case study of a world-leading original equipment manufacturer that is currently developing and introducing new smart services in its existing ecosystem.

The findings suggest that external actors emphasize different types of legitimacy in deciding whether to accept a new smart service. The findings also show that the type of legitimacy required to gain acceptance changes throughout the development of the smart service, from the definition of the value proposition to the design and delivery of the service.

This study can assist smart service providers in identifying which type of legitimacy is important for each ecosystem actor and strengthening these types of legitimacy to gain acceptance from the ecosystem.

This study develops a framework to help describe the thresholds for acceptance of a smart service through the development phases, as well as to indicate the types of legitimacy that smart service providers must relate to when seeking to gain acceptance for their new offering.