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Facebook groups provide a forum for members to post content and engage with others through comments. Sometimes members behave poorly and violate the expectations of group members. In this study, the authors build a research framework based on expectancy violation theory (EVT) to predict and better understand the behaviour and responses of members when faced with violations in their groups.

Facebook group members completed surveys regarding their interactions in social media groups. The independent variable predictors in the study were categorized by personal characteristics, relationship characteristics and group characteristics. Participants also identified expectancy violations they had encountered (either severe or mild) and identified how they would react to the two types of violations. Regression models were developed for severe and mild violations.

The regression models show that personal characteristics such as age, gender and marital status; relationship characteristics such as their social media usage frequency and their social media engagement level; group characteristics such as anonymity of users and purpose of the group as well as the perceived severity of the violation influence how a member will respond to the expectancy violation.

The research study extends the existing expectancy violation literature by providing a comprehensive framework to predict how users will react to negative expectancy violations. This study also has practical implications for how group administrators might manage expectancy violations.

The purpose of this paper is to establish a new conceptual iterative framework for extracting knowledge from open government data (OGD). OGD is becoming a major source for knowledge and innovation to generate economic value, if properly used. However, currently there are no standards or frameworks for applying knowledge continuum tactics, techniques and procedures (TTPs) to improve elicit knowledge extraction from OGD in a consistent manner.

This paper is based on a comprehensive review of literature on both OGD and knowledge management (KM) frameworks. It provides insights into the extraction of knowledge from OGD by using a vast array of phased KM TTPs into the OGD lifecycle phases.

The paper proposes a knowledge iterative value network (KIVN) as a new conceptual model that applies the principles of KM on OGD. KIVN operates through applying KM TTPs to transfer and transform discrete data into valuable knowledge.

This model covers the most important knowledge elicitation steps; however, users who are interested in using KIVN phases may need to slightly customize it based on their environment and OGD policy and procedure.

After its validation, the model allows facilitating systemic manipulation of OGD for both data-consuming industries and data-producing governments to establish new business models and governance schemes to better make use of OGD.

This paper offers new perspectives on eliciting knowledge from OGD and discussing crucial, but overlooked area of the OGD arena, namely, knowledge extraction through KM principles.

This study aims to conduct a regression analysis of factors contributing to truck driver detention time, including shipper, carrier and customer facility characteristics. Specifically, we examine how the impact of private fleets versus for-hire carriers on detention time varies with customer facilities that have different operating hours and cargo processing efficiency.

Based on time-stamped shipment data collected from a large US fresh producer, this study assesses the impacts of contributing factors on detention time through a regression analysis of a large dataset consisting of 16,227 shipment records over a three-month period.

Results show that private fleets are associated with less detention time than for-hire carriers. The advantage of private fleets over for-hire carriers is more pronounced at customer facilities with 24-h shifts or fast processing speeds. The impact differences between private fleets and for-hire carriers are not statistically significant at slow-processing customer facilities or those without 24-h shifts. There appears to be an inverted U-shaped relationship between carrier size and detention time.

This study offers evidence-based recommendations to shippers, carriers, receivers and policymakers on the reduction of detention time.

This is the first empirical study to examine detention time using a comprehensive shipment-level dataset collected from a major US fresh producer. The findings offer managerial insights and policy recommendations to address detention, a significant challenge facing the US trucking industry.

The purpose of this paper is to document that religious adherence in the county of the corporate headquarter and educational attainment of the female director pool near the firm headquarters are influential to the likely addition of female corporate board directors.

The sample covers 1,630 unique firms and 30,369 unique directors covering a ten-year period to investigate the effects of religiosity and educational attainment.

The analysis reveals that while the number of women has increased in general terms, this change is mostly limited to boards that are increasing in size. Women do not tend to replace exiting male board members but are appointed when the board size grows. Therefore, while the number of women is increasing in absolute terms, they are not increasing in relative terms. In areas where religiosity is high, as measured by church affiliation and attendance, female participation in the boardroom is lower and a more educated and qualified female population leads to higher board participation. These effects supersede any regional effects.

The study adds insights into corporate board dynamic, providing new evidence concerning the impact of local conditions on board composition as well as additional information concerning the interplay of board dynamics and female board representation.

The purpose of this paper was to study users’ behaviour when using different search engine results pages (SERPs) to identify what types of scents (cues) were the most useful to find relevant information to complete tasks on the Web based on information foraging theory.

This study has designed three interface prototypes and conducted a qualitative study using the protocol analysis methodology. The subjects were recorded and videotaped to identify patterns of searching behaviours on visualization interfaces of SERPs.

The study found that users found titles of categories or websites, keywords of categories, orientation of results and animation are strong scents that users follow to help find information on SERPs. If certain scents are not used followed on an interface, then their strength will diminish. Furthermore, the study showed that simple scent trails are more important to users than complicated trails.

This study uses a qualitative approach to explore how users behave with different SERP formats, particularity a visualization format, and identify which scents on the interface are important for users to follow to successfully complete tasks on the Web.